• Cisco Talos is tracking a cryptocurrency-stealing campaign that abuses the Google Visualization API for command and control (C2), retrieving obfuscated JavaScript from a publicly published Google Sheets document and injecting it into the victim's browser session. 
• The actors use a variation on ClickFix social engineering. Instead of convincing targets to run commands against the operating system, they convince targets to paste JavaScript into the Chrome address bar or install it into the Tampermonkey browser extension, which also provides persistence. 
• The lure poses as a leaked vulnerability report describing a nonexistent API flaw at cryptocurrency swap services, and is aimed at users willing to exploit it for financial gain. Talos observed lures distributed through Telegram, DarkForums, and paste sites. 
• The injected script functions as a web skimmer. It hooks the browser's fetch API, replaces cryptocurrency deposit addresses in server responses and the user's clipboard, and displays counterfeit "bonus" interface elements. 

Cisco Talos has recently observed a criminal campaign that leveraged an interesting twist on what we refer to as “legitimate service abuse.” In this monthslong campaign, the criminal actors used the Google Visualization API as part of a scheme to inject malicious JavaScript into two cryptocurrency trading websites. 

This campaign uses a twist on the tactics associated with “ClickFix” social engineering attacks, in which targets are manipulated into copying and pasting PowerShell or other commands and executing them to launch malware. Rather than targeting the victim device’s OS, the actors behind this campaign aim to convince the user to inject malicious code into their own browser session.

Early versions of this campaign began in early October 2025. The social engineering lures used initially focused on getting targets to paste a code snippet directly into the Chrome web browser’s navigation bar; the latest version relies on a legitimate Chrome plugin, Tampermonkey, to inject a loader script pasted in by the user and provide persistence across sessions with the current targeted site.

In March 2026, the actors behind the campaign began using the Google Visualization API to deliver malicious scripts stored in a Google Sheets document. In July, after frequent disruption of their posts on shared text sites, the actors moved to hosting all the components of their campaign in Google Docs and Google Sheets. 

So far, the actors behind the scheme have largely targeted individuals who frequent web discussion boards and forums focused on cryptocurrency trading, software development, basic cybersecurity, and hacking. The lure used in the campaign is designed to appeal mostly to would-be cybercriminals looking to make a quick profit off an “API vulnerability” that doesn’t exist to get bigger payouts on cryptocurrency trades.  

While this campaign doesn’t pose a specific threat to most organizations, the approaches that the actors here are using do. These techniques and tools could be leveraged in other malware and web attacks with much wider impact, including supply-chain attacks on e-commerce sites and other customer-facing systems.

Just Google it 

Google application abuse for C2 is not new by any stretch of the imagination. There have been multiple cases of state-sponsored actors using Google Sheets APIs, Google Drive, and other Google cloud services to help control deployed malware, concealing communications within traffic to otherwise trusted network spaces.

Hunting for these threats usually requires examination of DNS traffic and the processes that are making the requests to reach these destinations — like a random executable making a DNS request for “docs.google[.]com”. But when the requests are made from within a browser session, that makes detection much more difficult.

The Google Visualization API is a feature of Google Docs that is almost as old as the platform itself. Initially introduced in 2008, the API provides free, unauthenticated read-only access to the contents of any Google Sheets spreadsheet that has been publicly published to the web via queries embedded in a URI. These queries result in delivery of data from within the spreadsheet in JSON format or as an HTML table. 

A Visualization API request URI looks like this:

https[:]//docs.google[.]com/spreadsheets/d/[document identifier] /gviz/tq?[query language input formatted for HTTP] 

The API’s query language is very similar to Structured Query Language (SQL). For example, to get all of the content from a sheet’s column B returned as a JSON object, the query portion of the URI would be:

/gviz/tq?tqx=out:json&tq=SELECT%20B

API responses in JSON are returned in the format like the one below, ready to be parsed by the calling JavaScript application:

This is a read-only API, so it can’t be used by an application to alter the data in the spreadsheet. However, an application could append to the data to a spreadsheet connected to a Google Forms page by sending an HTML POST request. Presto! A full C2 system hidden within HTTPS traffic to a trusted domain.

The campaign Talos observed used the Visualization API to retrieve two cells from a remote spreadsheet containing obfuscated JavaScript, which were injected into the web browser session of a target. Used in combination with other abused legitimate services, the actors behind this campaign could modify the query used to change which cells were retrieved and injected, or if necessary change the spreadsheet targeted by the query — which they did after we disrupted their operations the first time.

Phishing for sharks 

The actors behind this scheme are looking for a very specific kind of mark: someone who is willing to commit fraud using technical means that they don’t clearly understand. Early versions of their phishing campaign documented by researchers used email, carrying links to a Google Docs document purportedly leaked security report. Actors also used comments on Pastebin and other text-sharing sites and direct messages in various forums to post links to versions of the Docs file.

In January, operators of the most recent incarnation of the scheme set up a Telegram account to facilitate the scam. The Telegram channel only allows posts from the channel administrator, and the threat actors delete older posts each time a new version of the lure is published to disguise the fact that they’re essentially just reposting the same fictional “exploit” over and over again.

Talos found multiple lures related to this Telegram channel posted in dark web forums, including the cybercrime-focused DarkForums. Lures were also posted in the comments on text files shared on Pastebin and a number of other text-sharing sites. Some (like the one above) directed recipients to a Telegram channel, while others linked directly to a Google Docs document promoted in the Telegram channel.

Waves of these messages were sent out at least twice a month, coinciding with new postings on the Telegram channel.  

Baiting the hook 

Talos saw two variants of the Google Docs document linked in the phishing messages. Both used the same “docs.google[.]com” URL and the same file name (“API Logic Flaw”).  The first was active from April 12 – 16, 2026; the second was active starting at least as early as April 19 and remained active as of July 22, despite it being reported to Google through multiple channels.

The initial version we observed targeted the cryptocurrency trading site “SwapZone[.]io”. It was formatted in the style of a vulnerability report and discussed a purported weakness in an older version of the ChangeNOW cryptocurrency exchange’s API allegedly still exposed through SwapZone; if accessed, the report claimed, it would result in “~38% higher payouts” on trades of Bitcoin for other cryptocurrencies.

All the reader had to do to leverage this mythical old API, according to the lure document, was copy the script shared through a “paste[.]sh” link into the navigation bar of the Chrome browser preceded by “javascript:”.

On April 18, Talos observed a new revision of the lure document. The new lure used the same “docs.google[.]com” URL as the previous lure but was rewritten to target a different trading site — “SimpleSwap[.]io”, another cryptocurrency trading aggregator. The fake exploit this time was a flaw in a “loyalty bonus” function that allegedly triggered a 25% boost in the value of the trade, according to the updated lure document.

This wasn’t the only revision to the lure. In the new version, instructions were given to install the Tampermonkey browser extension from the Chrome Web Store to activate the “bonus.”

The instructions then directed the targets to a link to a new source script on “paste[.]sh” to be added to the plugin (“https[:]//paste[.]sh/dQfdExjo#AqjB4BBt]lwLt2NKrlC0x8J9O”). The plugin would activate the script whenever the user visited “SimpleSwap[.]io”. 

Reeling them in: First-stage scripts 

Aside from their means of being executed by the user, both versions of this campaign’s first-stage loader script contained strings that do essentially the same thing. They both connected to the same Google-hosted spreadsheet via the Visualization API, retrieving blocks of code from different pairs of cells within the sheet.

The initial version of the script depended on direct user execution of the script within Chrome. Targets were instructed by the lure to copy the script from the “paste[.]sh” URL and paste it directly into Chrome’s navigation bar preceded by “javascript:” which results in the execution of the script within the context of the current web page.

The address of the Google Docs spreadsheet is plainly visible in the sample, and the JavaScript containing it swaps it for the fictitious vulnerable API’s URL to construct the Visualization API URL to retrieve the second stage script from:

This results in the Visualization API call to:

The script used in the second version of the lure was intended to be pasted into the Tampermonkey browser plugin’s configuration. It was somewhat more obfuscated than the first:

In this script, the URL needed for the visualization API call is hidden within the fake SimpleSwap API address, encoded in Base64:

When decoded, the block of text after “bonus” becomes:

In both cases, the generated request for the site comes from the Chrome browser and would have appeared as legitimate web traffic. Both scripts then concatenate the retrieved blocks of JavaScript, reconstruct the full second-stage JavaScript payload, and inject into the browser session.

In the first version, this is a bit more involved: The script explores the document object model of the web page looking specifically for script elements associated with browser extensions. If it finds more than one script associated with Chrome extensions, it picks a random one to inject the payload into. If no extensions are found, it picks a random <script> object within the page as the location for injection of the payload.

The method used in the second lure makes injection much easier for the attacker, as it uses an installed browser extension (Tampermonkey) to host the injected code. The script added to the extension assembles the payload code and appends it to the SimpleSwap web page’s code when the site is loaded. This also provides persistence, as the code will be loaded every time the target navigates to the SimpleSwap site.

Landing the catch: The second-stage payload 

Over the course of our investigation, Talos saw the actors behind this campaign change the the payload delivered by the first stage script multiple times, which all did essentially the same thing: They decoded JavaScript code to inject into the targeted web pages to create the “exploits” they used to steal Bitcoin from victims.

We collected 21 unique samples of the second-stage payload from the spreadsheet referenced by the API call. These included scripts for both versions of the lure, as the scripts for SwapZone were left in the spreadsheet after the actors shifted to SimpleSwap.

To conceal the contents of the spreadsheet from casual analysis, the actors hid the text containing payload data by formatting the text as white on a white background. As revisions were  made to the sheets to add new versions of the script, more rows were added to push the hidden cells further down the sheet, making them only discoverable through a text search or with the API query.

The payload within each pair of rows was heavily obfuscated JavaScript. Except in two cases, all the obfuscation was based on converting the functional script in the payload into arrays of hexadecimal pairs that had been XOR encoded. The rest of the script was math functions to render the code and additional garbage math to conceal the actual XOR key used. One sample used Base64 encoding along with conversion of characters to Unicode values (e.g., converting “A” to “\x41”). Another paired XOR with the use of Unicode escaping for the apostrophes used to contain each array element, further impeding analysis.  

Revisions of the scripts over the course of Talos’ observation used different XOR keys each time to change the signature of the code and used different random strings for variable and function names, consistent with output from “Obfuscator[.]io” and similar Javascript obfuscation tools. However, it was clear that there were no actual changes to the scripts being obfuscated after they were initially coded for each targeted site.

Both the SwapZone and SimpleSwap scripts targeted cryptocurrency transaction interfaces of the sites, creating new user interface elements and altering existing functions. The scripts performed the following key actions: 

• DOM and UI manipulation: The script monitors the page using MutationObserver and dynamically replaces displayed deposit addresses in the interface. The malware script also modifies displayed transaction amounts to deceive targeted users into believing that they have received a “bonus” on their transactions. HTML rendered by both versions of the script presents counterfeit “hidden functionality” within the site, as shown in Figure 12.

• Network interception: The script overrides the browser’s fetch API to inspect and modify responses related to wallet or deposit endpoints. When JSON responses containing cryptocurrency deposit addresses are returned, the script replaces legitimate addresses with attacker‑controlled addresses. Based on decoded UI selectors and identifiers found in the script, the malware intercepts the contents of elements in forms on the targeted websites such as:
  • data-testid="recipientAddressContainer" 
  • data-testid="depositAddress" 
  • data-testid="currencyList" 
  • data-testid="cryptoExchangeTab"
  • data-testid="mainExchangeForm"
• Clipboard hijacking: The malware monitors UI elements used to copy cryptocurrency addresses. When a user attempts to copy a deposit address, the script replaces the copied value with an attacker’s wallet address. A rotating list of Bitcoin Bech32 wallet addresses was embedded in the code of each script; these were selected randomly for substation. 
• Persistence: The script conducts periodic DOM scanning to ensure the malicious replacements persist during page updates. Further persistence is provided in the second version of the attack using the Chrome extension-based loader.

Cashing in 

Talos identified 49 BTC wallet addresses that were used in the campaign. The majority of the samples we de-obfuscated (from April to the end of June) used an identical set of 30 addresses. Of these, 24 received funds from victims of the scheme, totalling 0.159 BTC (approximately $10,000 at early August 2026 valuations of Bitcoin). There was likely more collected, as we were unable to find samples from prior to April and may not have captured the wallets used by all variants. 

It’s difficult to tell how much the actors behind this campaign have been able to extract from their scam-driven malware. Funds were routed out through 30 more wallets, sometimes using multiple counterparties within the set.  From there the funds were moved in highly complex transactions involving a total of over 3,000 additional addresses—likely related to a Bitcoin “mixing” operation to hide the funds’ destinations. 

Still, this has been a very lucrative operation for relatively little investment for the actors behind it. And it has been difficult to disrupt the operation. In April, we shared information about the campaign with both the targeted websites and with Google, and the documents acting as lure and C2 components were blocked; a week later, the campaign was back with a new Google sheet and another paste[.]sh script, and back in business at a slightly reduced rate of victim recruitment. 

Paste[.]sh’s administrator took action to automatically detect new scripts matching the first-stage script’s signature in July. The actor then updated the lure and added a Google Docs document containing the script. As of August 11, the Google documents have again been reported but are still active. 

Beyond dishonor among thieves 

It is hard to be sympathetic to victims of crimes that preyed on their own willingness to essentially be criminals, but the capabilities demonstrated in this campaign could have a much more widespread and damaging impact in the wrong hands.

The techniques used here are a variation on classic web skimming approaches. Other web skimming attacks, such as Magecart web skimming campaigns, depend largely on supply chain compromise to infect legitimate sites, injecting code through compromised dependencies — third party services, Node Package Manager distributions, and other external sources integrated into web pages to add features or provide application support.

With a rise in supply-chain attacks by groups such as TeamPCP, which has sold access to other cybercriminals, the methods used in this cryptocurrency-stealing campaign could be used to leverage stolen access across large numbers of web applications dependent on compromised code bases.

An actor leveraging this sort of access to gain additional targeted access or steal other information from web-based applications could use the techniques used in this campaign to selectively alter functionality of user interfaces with code from what would look to defenders to be legitimate application traffic. Malicious and fraudulent browser plugins already alter content in web pages and redirect traffic; a subtle corruption of an existing browser plugin update using a Google document for C2 could make detecting malicious activity from network telemetry much more difficult for defenders.

Additionally, other social engineering attacks could leverage the Visualization API for opportunistic attacks on organizations much in the way other ClickFix-style attacks have managed to convince users to execute malicious code.

To defend against these real and potential threats, organizations should take the following steps: 

• Manage users’ browsers, limiting use of developer-level functionality and deployment of browser extensions based on role. 
• Monitor for HTTP requests to “docs.google[.]com” from unknown applications or in the context of browser sessions that do not include other Google Docs activity. 
• Test and sanitize third-party dependencies in employee and customer-facing web applications regularly, checking for obfuscated script that appears out of place in JavaScript-based web and mobile applications. 
• Warn employees and customers of social engineering techniques and the dangers of modifying browser behavior through copy-and-paste code. 

Coverage 

The following ClamAV signature detects and blocks this threat:  

• Win.Backdoor.BadDav-10060502-0
• Win.Backdoor.GoProxShell-10060503-0
• Win.Malware.AmateraStomper-10060507-0
• Win.Backdoor.BadNetSup-10060508-0
• Js.Downloader.ClickFix-10060510-0
  

IOCs 

The IOCs for this threat are available at our GitHub repository here. 

Microsoft researchers observed a high-volume phishing campaign using invisible Unicode tag characters, a technique popularized in AI prompt injection research as ASCII Smuggling. Instead of using these characters to hide instructions from people while exposing them to AI models, the attacker used them to split financial lure words such as ‘funding’ to prevent email filters from parsing them.

The finding emerged from Microsoft Defender for Office 365 prompt injection protection research, showing how AI-era evasion techniques can surface in traditional phishing campaigns. In Microsoft telemetry, hits on a hunting signature designed to detect ASCII-smuggling increased sharply beginning February 9, 2026, and remained elevated on weekdays for approximately three months. Microsoft Defender for Office 365 telemetry showed that the majority of messages were flagged by layered protections rather than by reliance on a single Unicode-specific signal.

What is ASCII smuggling?

“ASCII smuggling” refers to the use of invisible or non-rendering Unicode characters to hide content inside text that looks normal. The most abused range is the Unicode Tags block, U+E0000 to U+E007F. This block contains a shadow copy of the printable ASCII characters (for example, U+E0041 mirrors ‘A’, U+E0061 mirrors ‘a’). The block was originally intended for language tagging and is now largely deprecated.

The important property for an attacker is this: most of these code points are not rendered by typical fonts and user interfaces. A string can therefore carry a message that is not readable to a human but will be processed by any language model or other software that receives a copy of the email content.

Why the AI-security world made it famous

Over the past year, ASCII smuggling became a recurring technique in the prompt injection and cross-prompt injection (XPIA) literature. The attack pattern is straightforward:

1. An attacker hides instructions inside invisible tag characters embedded in a web page, document, email, or other content.
2. A human (and many user interfaces) sees nothing unusual.
3. An AI assistant that ingests the raw text does “see” the hidden characters, decodes them as text, and may be induced to follow threat actor-controlled instructions, potentially including data exposure or unauthorized actions depending on the assistant’s permissions and safeguards.

Because this technique cleanly demonstrates the gap between what the human sees and what the model reads, it appeared frequently in AI red-teaming write-ups, conference talks, and tooling throughout 2025. That attention put a spotlight on the U+E0000-U+E007F range.

Because tag characters are invisible to humans but exist at the text-processing level, the same property that makes them useful for smuggling instructions into a model also makes them useful for obfuscating keywords before a detector evaluates them. The intent is inverted, but the mechanism is similar and a user’s suspicions are not raised.

Writing a practical ASCII-smuggling signature

As part of work on Microsoft Defender for Office 365 prompt injection protection, we built hunting logic for email-borne XPIA and prompt obfuscation patterns: content that looks harmless to users but may carry hidden instructions for an AI system that ingests the raw message. The same hunt designed to identify prompt injection risk in email became the starting point for this phishing-evasion discovery.

One practical way to hunt for ASCII smuggling is to look for messages carrying characters from the Unicode tags block (U+E0000-U+E007F), the hallmark of attempts to hide instructions from, or for, an AI model. That broad signature is a useful starting point, but it needs enough Unicode context to avoid mistaking legitimate tag-character sequences for abuse.

The first version simply flagged any code point in that range, which proved too blunt. It kept firing on a small subset of perfectly legitimate messages – which, on inspection, all contained one of three subdivision flag emojis: the flags of England, Scotland, and Wales – because those emojis are encoded using tag characters.

After those exclusions, remaining hits were mostly benign artifacts from email-security gateways, mailbox providers, and security or AI researchers forwarding or testing messages that contained tag characters. This provided a good baseline where any spikes would indicate abuse of this technique by attackers.

Figure 1. The three subdivision flag emojis – England, Scotland, and Wales – that tripped the naive signature. Each is encoded as a sequence of invisible Unicode tag characters (U+E0000-U+E007F).

Figure 2. The Wales flag emoji pasted into the ASCII Smuggler tool from Embrace The Red. What renders as a single flag is actually a base flag code point (U+1F3F4) followed by an invisible tag-character sequence spelling gbwls (U+E0067 U+E0062 U+E0077 U+E006C U+E0073) and a terminating tag (U+E007F) – the same U+E0000-U+E007F range the signature watches for.

What we observed: ASCII smuggling repurposed for phishing

New activity emerges in telemetry

The tuned ASCII-smuggling signature began as an AI-security hunt for hidden prompt injection content in email. Instead, it surfaced finance-themed phishing messages using the same Unicode range for filter evasion.

On February 9, 2026, signature hits increased sharply. The following chart reflects Microsoft Defender for Office 365 telemetry for the hunting signature over the measured period:

The day before onset (February 8) the signature fired on roughly 21,000 messages; the next day it fired on more than 1.3 million. Most of the emails can be formed into a cluster of roughly 150 finance-themed sender domains.

Observed over three months with a weekly rhythm

Continuing to track the clustered sender domains forward in time, we measured messages matching the activity described every day. The high-volume phase persisted for roughly three months after February 9 and dropped sharply after May 15, 2026. These dates bound the observed use of the specific technique in our telemetry, not the broader campaign, which started earlier without it and continued without it.

Two characteristics stand out:

• A strict weekly cadence. The campaign ran hard on weekdays and went almost completely silent every weekend. Sundays’ volume collapsed to a near-zero and then back to full volume the next day. This on/off pattern is typical of scheduled bulk-sending infrastructure.
• A long, gradual decline. After an intense first phase, with weekday volumes of 1 to 2.37 million messages, peaking on February 26, the numbers stepped down slowly to roughly 80% less per weekday by late March. The high-volume usage of the technique dropped sharply after May 15, with lower residual activity through mid-June and occasional smaller spikes.

After identifying the activity through this technique-specific signal, we connected it to a broader ActiveCampaign-delivered SBA-themed phishing campaign that Fortra had documented earlier. That earlier reporting indicates the campaign predated the adoption of Unicode tag characters; our analysis focuses on the period and messages in which this method was present, not the full lifetime of the broader campaign.

Not instruction smuggling, but filter evasion

Observed obfuscation pattern

When we looked at a sampling of the flagged messages, the surprise was there were no smuggled instructions to an AI assistant. Instead, the invisible tag characters were inserted inside common financial keywords, splitting them apart so that a literal signature or keyword match would fail.

For example, a finance lure term that appeared normal to the recipient could be transmitted with an invisible tag character in the middle:

funding

became:

fun⟨U+E0020⟩ding

Here, ⟨U+E0020⟩ represents the invisible Unicode TAG SPACE inserted between letters. In the messages we examined, the campaign did not encode a hidden ASCII message in the tag block; it used a single invisible tag character as a separator sprinkled inside high-signal words. Strictly speaking, this is invisible-character insertion using a code point from the ASCII-smuggling tag block, rather than full message smuggling.

Why it can affect detection

To a recipient, and to parsing pipelines that drop or normalize these characters, the word still reads as funding. To a detector matching the literal string funding, or a regex that does not account for interleaved invisible code points, the byte sequence no longer contains the contiguous keyword. Whether real-world detectors behave that way depends on their normalization step, which is examined below.

The bigger prize for the attacker, though, is not preventing the literal string matches; it is the ML- and NLP-based models that increasingly drive modern spam and phishing classification. Unless a filtering system takes a picture of a message and does OCR extraction over the visual image, it may miss this type of attack. A standard email classifier may not reason over whole words exactly as a human sees them; for efficiency, they can first split text into tokens or sub-word pieces. A clean lure term such as funding may be represented as a familiar token or a familiar sequence of sub-tokens. Insert an invisible U+E0020 into the middle, however, and the tokenizer may no longer see that same familiar unit. It might split the text into fun, an unexpected tag character, and ding; it might emit rare or unknown sub-tokens; or, if normalization runs first, it simply removes the U+E0020 character, leaving funding.

Why it can help defenders

There is also a defensive opportunity. Since this kind of manipulation appears so seldom in normal traffic, its presence becomes a high-confidence signal. A technique meant to make messages look more benign to ML models can instead give defenders a low-false-positive indicator to detect on.

What is known and what is new

Inserting invisible or look-alike characters to break keyword and signature matching is a long-standing evasion technique used in spam and phishing: defenders have for years seen zero-width spaces (U+200B), zero-width non-joiners, the no-break space (U+00A0), soft hyphens, and homoglyph substitutions used to fracture words so naive string matchers fail.

What is new is the specific characters and scale of the campaign:

• The character choice. Instead of the usual zero-width space or NBSP, this campaign reached for the Unicode Tags block. That block went from forgotten to famous over the past year because of AI security research into ASCII smuggling and prompt injections.
• The scale and discipline. At its peak in Microsoft telemetry, the campaign generated multi-million message daily volume.
• A possible detection blind spot. Because the Unicode Tags block is less commonly abused than zero-width spaces or NBSP, defenders should verify that normalization and tokenization pipelines handle tag characters consistently.

Financially themed sending domains

The campaign ran on hundreds of disposable, finance-themed sender domains with lures that resembled business loan, line-of-credit, and advance-funding phishing patterns often associated with fraud or credential-harvesting funnels. This pattern accounted for roughly 96% of the volume flagged by the hunting signature. The signature also fired on other domains, but those were unrelated senders – chiefly email-security gateways and personal mailbox providers – not part of the campaign.

A partial sample of sender domains counts from February 9, 2026 alone illustrates both the naming pattern and the per-domain volume:

Table 1. Top 20 (by signature hits) of the 148 finance-themed campaign sender domains seen on February 9, 2026, illustrating the naming convention and per-domain volume.

Every domain is just a recombination of the same small vocabulary. The 20 domains above are built from only 28 word-tokens:

advance · boost · business · capital · catalyst · choice · digital · direct · elevate · express · finance · funding · growth · guardian · harbor · loan · loans · loc · online · our · pulse · rocket · rush · the · united · wave · way · your

Sent through a legitimate email-marketing platform

The finance-themed domains in Table 1 are the brand (header / P2) domains the recipient sees, but the actual mail was relayed through infrastructure associated with the legitimate email-marketing platform ActiveCampaign. The platform, which is used widely for marketing, rewrites every outbound link in the message body to route through its own click-tracking domains (acemlnd[.]com and activehosted[.]com), so the URLs the recipient clicks do not point at the brand domain at all – they look like:

hxxps://＜account-id＞.acemlnd[.]com/＜tracking-token＞
hxxps://＜brand-subdomain＞.activehosted[.]com/＜tracking-token＞

Most of the flagged messages carried links associated with the platform’s tracking domains rather than direct links that point directly to the sender-branded domains. The envelope (P1) senders were platform subdomains of the form em-<id>.<brand-domain>.

ActiveCampaign response

Before we published this information, we shared our findings with ActiveCampaign to help them with this abuse, and they wanted us to share the following statement on their work to detect it:

“We appreciate Microsoft’s research and welcome collaboration with the security community to combat this activity. We take abuse, fraud, and security extremely seriously. We tested the specific technique described in this research against our content-moderation systems: messages containing invisible Unicode characters receive the same moderation verdicts as their unobfuscated equivalents, and heavy use of the technique is itself treated as a suspicious signal. We continually invest in improving our detection and prevention capabilities, including expanding our use of AI and machine learning to identify abusive sending behavior earlier in the account lifecycle.” — ActiveCampaign spokesperson

As with any shared sending service, attacker abuse of customer accounts or workflows can complicate reputation-based filtering. By originating from a reputable marketing platform with established IP reputation and authentication, the activity may appear more similar to legitimate marketing traffic and can complicate reputation-based filtering.

Most observed volume also originated from cloud-hosting ranges consistent with the platform’s outbound infrastructure, with the vast majority coming froma single network block, 173.236.20[.]0/24. This indicator helped us cluster the campaign more precisely but note that this is a legitimate segment that belongs to the abused service, and not an IOC on its own.

Identifying the campaign

Content and infrastructure remained consistent for a long time span, providing an effective way to easily fingerprint this phase of the campaign:

• Unicode content (primary). Invisible Unicode tag characters in the range U+E0000-U+E007F – specifically U+E0020 – spliced inside keywords. Legitimate mail rarely ever carries these code points: the one routine exception, the England/Scotland/Wales flag emojis, is easily excluded.
• Lure and brand pattern. Sender (header / P2) domains assembled from a small finance vocabulary – capital, fund/funding, loan, loc, lend, finance, business, express, growth, solutions, choice, hedge, pillar – recombined into fresh, disposable domains and rotated.
• Envelope (P1) pattern. The bulk of mail is relayed through a single email-marketing platform, recognizable by envelope shape rather than any one name:
  • per-account subdomains shaped em-<digits>.<brand-domain> (regex em-\d+\.), where a small set of reused account numbers fans out across hundreds of brand domains; and
  • the platform’s shared sending pool, shaped acems<N>[.]com and emsd<N>[.]com (e.g. emsd4[.]com, s9.acems10[.]com). Across the measured activity, ~98.5% of messages matched this envelope pattern, and ~99.8% matched the envelope pattern or the platform’s tracking-URL pattern (below).
• Tracking-URL pattern. Click/tracking links on the platform’s domains activehosted[.]com and acemlnd[.]com.
• Sending-origin pattern. The bulk of daily volume – about 92% across two measured weeks – originated from a single /24 network block, 173.236.20[.]0/24.

For a high-precision rule, look for the Unicode content pattern combined with the finance-brand pattern, using the sender infrastructure patterns as corroboration.

However, this is just a phase in a long-running broader campaign, that keeps adapting and evolving. The campaign was observed months earlier following a different set of behaviors and continued even after the usage of the specific technique was dropped. During these shifts in behavior, one signature may no longer describe the campaign, while another still matches.

Is there a detection gap?

The potential gap for mail-defense pipelines is whether Unicode tag characters are normalized or flagged before content detections run. In Defender, our filter stack can take a picture of message contents, extract visible text through OCR, and run analysis over that extracted text to avoid these types of tricks. Implementations vary, so defenders should test how these characters are handled in their own pipelines. For MDO protection, over 99% of messages were flagged by layers that did not depend on catching the tag characters directly, including sender, IP, URL and domain reputations, ML spam/phishing classification, brand-impersonation detection, authentication checks and more.

Emerging techniques don’t stay in one domain

ASCII smuggling earned its reputation as an AI attack, hiding instructions from people while leaving them visible to models. This campaign shows the same technique being repurposed for a different objective: obscuring phishing content from detection systems while remaining readable to the intended target.

The broader lesson is that security techniques rarely stay confined to a single domain. As AI-era attack methods become better understood, threat actors may adapt them for use in more traditional threats such as phishing and spam. This case illustrates how techniques that emerge in AI security research can quickly cross over into established attack ecosystems, reinforcing the need for defenders to view emerging threats through a cross-domain lens.

Mitigation and protection guidance

The core defensive principle is simple: normalize before you match. Any content that will be evaluated by keyword, signature, or regex logic should first have invisible and non-rendering Unicode code points stripped or folded, so that splicing them into a word no longer defeats the match.

Recommended controls

• Strip or normalize Unicode tag characters (U+E0000-U+E007F) – and other zero-width / invisible code points – from email subject and body text before applying spam and phishing content signatures.
• Treat the presence of tag-block characters as a strong anomaly signal. Outside known legitimate tag-sequence uses such as certain subdivision flag emojis, these code points are rare in ordinary mail and can be a high-value anomaly signal.
• Look for the behavioral fingerprint. The observed activity had a distinctive shape: bulk volume from churning, finance-themed disposable domains, on a strict weekday-on / weekend-off schedule. A sudden spike of tag-block characters concentrated on finance-themed senders, switching on and off weekly, is a high-confidence campaign indicator.
• Apply the same normalization upstream of AI ingestion. The same control that defeats this evasion also reduces XPIA / ASCII-smuggling exposure for AI assistants that ingest email content.

Microsoft protections

Microsoft Defender for Office 365 has heuristic detections in place to flag these the tactics employed in this type of campaign. The detection that first surfaced the spike continues to flag messages carrying Unicode tag-block characters, and the financially themed sending domains are being tracked and blocked as they rotate. Microsoft uses layered email protections, including standard and OCR content analysis, sender and domain reputation, URL detonation and reputation, bulk-mail detection, and anti-phishing models, to reduce reliance on any single signal that an attacker can try to evade.

Microsoft Defender for Office 365 prompt injection protection further helps protect against emails that contain prompt injection attempts, including cases where invisible characters are used to hide instructions from users while exposing them to AI systems. The same normalization and detection principles that reduce ASCII-smuggling-based prompt injection risk also help blunt this email-borne reuse of the technique for phishing evasion. Investments in AI security and traditional email security increasingly reinforce one another.

Coverage depends on product licensing, configuration, and telemetry.

Advanced hunting

These queries run against the EmailEvents Advanced Hunting table (and EmailUrlInfo for URL joins). They hunt the campaign by its infrastructure fingerprint – the finance-vocabulary brand senders and the marketing-platform envelope shape – rather than by the invisible tag characters, as the mail body is not exposed through the table’s columns. These queries are starting points and may require environment-specific tuning. The proactive defense is implemented with multiple layers of the enterprise mail-filtering pipeline.

1. Infrastructure pattern – finance-vocabulary senders relayed with the campaign’s envelope shape. Combines the brand-domain pattern (a header sender built from three or more adjacent finance/brand keywords, e.g. digital+capital+boost) with the envelope (MAIL FROM) shape em-<digits> / acems<digits> / emsd<digits> – the durable fingerprint that held across the entire period we measured.

// Finance/brand vocabulary the operator recombines into disposable domains.
let kwds = @"(capital|fund|hedge|express|solutions|choice|lend|growth|loan|loc|finance|business|pillar|advance|boost|catalyst|digital|direct|elevate|guardian|harbor|online|pulse|rocket|rush|united|wave|way|surge|swift|elite)";
EmailEvents
| where Timestamp > ago(30d)
| where EmailDirection == "Inbound"
// Header sender domain made of 3 or more adjacent finance/brand tokens.
| where SenderFromDomain matches regex strcat("(?i)", kwds, kwds, kwds)
// Envelope (MAIL FROM) shape: em-[digits] | acems[digits] | emsd[digits].
| where SenderMailFromDomain matches regex @"(?i)(em-|acems|emsd)\d"
| sort by Timestamp desc

For extra corroboration you can scope to the single dominant /24 that carried the bulk of this campaign’s volume, 173.236.20[.]0/24, by adding | where ipv4_is_in_range(SenderIPv4, “173.236.20.0/24”). Like the tracking URLs, that network block is shared platform space (it also carries unrelated legitimate newsletters), so use it to scope, never as a standalone filter.

2. Pivot on the platform tracking URLs. Start from the click/tracking links and join back to the mail events. Useful for scoping, but treat it as corroboration, not a verdict: the tracking domains activehosted[.]com and acemlnd[.]com are shared by every legitimate customer of the same marketing platform, so the URL on its own is not a malicious indicator. The finance-brand filter is what keeps this on the campaign; drop it only if you deliberately want a wider search.

let kwds = @"(capital|fund|hedge|express|solutions|choice|lend|growth|loan|loc|finance|business|pillar|advance|boost|catalyst|digital|direct|elevate|guardian|harbor|online|pulse|rocket|rush|united|wave|way|surge|swift|elite)";
EmailEvents
| where Timestamp > ago(30d)
| where EmailDirection == "Inbound"
| where SenderFromDomain matches regex strcat("(?i)", kwds, kwds, kwds)
| join kind=inner (
    EmailUrlInfo
    | where Timestamp > ago(30d)
    | where UrlDomain endswith "activehosted.com" or UrlDomain endswith "acemlnd.com"
    | distinct NetworkMessageId
  ) on NetworkMessageId 
| sort by Timestamp desc

3. Filter for prompt injection detection in emails

The feature used in the query below is available for Microsoft Defender for Office 365 Plan 2 or Microsoft 365 E5 customers.

EmailEvents
| where DetectionMethods has "Prompt Injection Protection"

MITRE ATT&CK techniques observed

This campaign exhibits the following MITRE ATT&CK® techniques. The table includes MITRE ATT&CK for phishing/evasion behavior and MITRE ATLAS for the AI-security technique class related to prompt obfuscation.

Indicators and hunting pivots

References

• Unicode Standard Annex #45 – Tags (U+E0000-U+E007F) – the deprecated Unicode tags block abused for ASCII smuggling
• ASCII Smuggler Tool: Crafting Invisible Text and Decoding Hidden Codes | Embrace The Red – public write-up showing how Unicode tag characters can hide text from users while remaining present to language models
• LLM Prompt Obfuscation | MITRE ATLAS™ – the AI-security technique class this evasion crossed over from
• Attackers exploit ActiveCampaign to deliver thousands of AI-generated SBA phish | Fortra – earlier reporting on the broader ActiveCampaign-delivered SBA-themed phishing campaign, before the Unicode tag-character evolution examined here

Learn More

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.  

• Learn more about securing Copilot Studio agents with Microsoft Defender 
• Evaluate your AI readiness with our latest Zero Trust for AI workshop.
• Microsoft 365 Copilot AI security documentation
• How Microsoft discovers and mitigates evolving attacks against AI guardrails

• Manipulating AI memory for profit: The rise of AI Recommendation Poisoning | Microsoft Security Blog – a related example of an AI-era technique observed in email traffic
• Defending the inbox against prompt injection attacks | Microsoft Defender for Office 365 Blog – feature announcement introducing prompt injection protection in Microsoft Defender for Office 365.
• Prompt injection protection in Microsoft Defender for Office 365 – official documentation of the prompt injection protection in Microsoft Defender for Office 365.
• How Microsoft discovers and mitigates evolving attacks against AI guardrails | Microsoft Security Blog

The post ASCII smuggling crosses over from AI prompt injection to phishing evasion appeared first on Microsoft Security Blog.

A company can have strong firewalls, modern endpoint protection, and carefully controlled access—and still find its brand being used as a weapon against customers, employees, and partners. 

That is the new reality of digital impersonation. Attackers can register lookalike domains, clone websites, create fake executive profiles, publish fraudulent job advertisements and imitate customer-support accounts without ever breaking into the legitimate organization. 

The objective is pretty simple. Borrow the credibility that a trusted brand has already built and use it to make a scam look legitimate. For professional services, financial, legal, and consulting organizations, that risk can be particularly damaging because trust is central to the business model. 

The Numbers Show Why Speed Matters 

The scale of digital fraud makes slow brand-abuse response difficult to justify. 

The FBI's 2025 Internet Crime Report recorded 1,008,597 complaints, marking the first time the Internet Crime Complaint Center (IC3) exceeded 1 million in a year. Reported losses reached $20.877 billion, up 26% from 2024. Phishing and spoofing were among the most frequently reported complaint types. 

Business email compromise was even more costly, producing approximately $3.05 billion in reported losses from 24,768 complaints. 

The Federal Trade Commission provides another measure of the impersonation problem. Consumers reported $3.5 billion in losses to imposter scams during 2025, with nearly one in three fraud reports involving impersonation. People reported losing nearly $1 billion to business impersonators alone. 

These figures represent reported losses, not the full economic impact. Fraudulent domains and profiles can disappear quickly, victims may never report incidents, and reputational damage is difficult to quantify. 

Professional Services Have More Than a Brand to Protect 

Consulting and professional services firms often handle sensitive client information, financial models, strategic plans, legal documents and confidential communications. That makes their identities valuable to criminals. 

The legal sector provides a useful comparison. The American Bar Association's cybersecurity research has previously found that 29% of surveyed lawyers reported that their firms had experienced a security breach. 

Impersonation adds another layer because the attacker may never enter the firm's network. A counterfeit website can steal credentials. A fake executive can request a payment. A fraudulent recruiter can collect applicant information. A fake support account can redirect customers to a malicious login page. 

The brand becomes the attack surface. 

Why Traditional Takedowns Become a Whack-a-Mole Exercise 

Conventional brand protection is often reactive. Someone discovers a suspicious domain, reports it to the registrar, contacts the hosting provider or social platform, and waits. 

That process can work—but it does not scale well against automated adversaries. 

By the time one fraudulent domain is removed, another may have appeared. A fake executive account can be recreated under a slightly different name. A phishing kit can be deployed against several brands simultaneously. Fraudsters can also move between websites, social networks, advertisements, application stores and messaging platforms. 

Counting the number of takedowns therefore tells only part of the story. A more meaningful measurement is the time from discovery to verification and from verification to removal. 

The shorter that window, the fewer opportunities an attacker has to reach victims. 

What AI Changes 

Artificial intelligence has made impersonation faster, cheaper, and more convincing. 

Attackers can generate polished phishing messages, translate campaigns for different markets, create synthetic personas, clone websites and produce increasingly convincing voice or video content. The FBI has also warned about scams involving AI-generated videos and spoofed websites used to create false legitimacy. 

Europol's 2025 Internet Organised Crime Threat Assessment similarly described a cybercrime economy increasingly powered by stolen data, which can support fraud, ransomware, extortion and other criminal activity. 

That means defenders face an uncomfortable imbalance: criminals can create fraudulent content almost instantly, while organizations may still investigate abuse manually. 

Brand security consequently must become faster without becoming careless. 

The Most Common Brand-Abuse Tactics 

Security teams should watch for a broad range of impersonation signals, including: 

• Typosquatting: domains using misspellings or visually similar characters. 

• Combosquatting: brand names combined with words such as “login,” “support” or “secure.” 

• Fake social profiles: cloned executive, employee, or company accounts. 

• Account takeovers: legitimate accounts hijacked and used to exploit an existing audience. 

• Cloned websites: replicas designed to collect credentials or payment information. 

• Fake mobile applications: counterfeit apps using familiar names, icons, or branding. 

• Fraudulent marketplace listings: fake products or services presented as legitimate. 

• Malicious QR codes: QR-based redirects leading victims to phishing infrastructure. 

• AI-generated impersonation: synthetic voices, images, video, and written communications. 

• Business email compromise: messages designed to trigger payments or sensitive disclosures. 

• Fake customer-support accounts: fraudulent profiles responding to real customer complaints. 

• Malicious search advertisements: paid placements directing users toward counterfeit sites. 

• Fake recruitment campaigns: fraudulent jobs used to collect personal or financial information. 

• Fake press releases: fabricated announcements intended to mislead customers, investors or the public. 

• Dark-web brand abuse: stolen credentials, data, and brand-specific fraud resources circulating in criminal communities. 

Conclusion 

Brand impersonation is no longer just a reputation issue—it can quickly become a pathway to phishing, fraud, credential theft, and customer harm. As AI enables attackers to create convincing fake websites, domains, social profiles, and campaigns at unprecedented speed, organizations need equally fast detection and response.  

Cyble’s brand monitoring and takedown services help organizations detect impersonation, validate malicious activity, and coordinate the removal of fraudulent assets before they can cause greater damage.  

With continuous visibility and managed takedown support, Cyble helps security teams stay protected from brand threats and protect customer trust.

See Cyble’s brand monitoring and takedown capabilities in action—request a demo today. 

Frequently Asked Questions (FAQs)  

1. What is brand impersonation in cybersecurity? 

Brand impersonation occurs when attackers imitate a legitimate company, executive, employee or digital channel to deceive customers, employees or business partners. Common examples include fake websites, lookalike domains, fraudulent social profiles, counterfeit applications and phishing emails. 

2. Why is AI making brand impersonation more dangerous? 

AI allows attackers to create convincing emails, websites, social profiles, synthetic identities, voice messages and other fraudulent content much faster and at greater scale. This makes it harder for organizations to rely on manual monitoring and reactive investigations. 

3. What brand impersonation tactics should security teams monitor? 

Security teams should monitor for typosquatting and lookalike domains, fake executive profiles, cloned websites, counterfeit apps, fraudulent job postings, fake customer-support accounts, malicious advertisements, phishing campaigns, AI-generated impersonation, and brand abuse on underground platforms. 

4. Why is rapid takedown important for brand protection? 

A fraudulent website or social profile can cause harm within minutes by stealing credentials, collecting personal information, or redirecting payments. Faster verification and takedown reduce the amount of time attackers have to reach potential victims. 

5. Can smaller and mid-sized organizations also be targeted? 

Yes. Attackers are not limited to globally recognized brands. Smaller and mid-sized organizations can also be attractive targets because they may have fewer resources dedicated to continuous brand monitoring and digital risk management. 

6. How can Cyble help with brand impersonation? 

Cyble’s brand monitoring and digital risk protection capabilities help organizations identify suspicious domains, fake profiles, fraudulent websites and other forms of digital brand abuse across the online ecosystem. By bringing detection and threat intelligence together, Cyble can help security teams investigate impersonation faster and take action before fraudulent assets cause greater damage. 

References 

• FBI 2025 Internet Crime Report 

• FTC Data Show People Reported Losing $3.5 Billion to Imposter Scams in 2025 

Media Disclaimer: This blog was compiled from publicly available government advisories and open-source security reporting. It is provided for reference purposes only; readers bear full responsibility for their reliance on it. 

The post When the Attacker Wears Your Logo: Detecting and Taking Down Impersonation at AI Speed appeared first on Cyble.

Unit 42 details how attackers exploit enterprise collaboration tools for identity phishing and credential theft. Discover key defense strategies.

The post Identity Abuse Through Trusted Communication Channels appeared first on Unit 42.

MacSync Stealer is a macOS-focused information stealer that relies on changing infrastructure to deliver payloads, communicate with compromised devices, and exfiltrate data. Earlier reporting by RST Cloud identified the threat through a limited set of domains and documented rapid command-and-control (C2) replacement after public disclosure.

Microsoft Defender Experts expanded that view by correlating recurring endpoints and network behaviors across the activity. This behavior-led approach connected more than 30 domains and showed that the infrastructure supported more than C2 communication, extending into active collection, staging, and exfiltration. The findings demonstrate that although domains may rotate quickly, repeated execution patterns, request characteristics, staging behavior, and upload methods provide defenders with more durable opportunities to investigate MacSync Stealer activity. 

Activity overview 

Microsoft Defender Experts reviewed endpoint and network telemetry to determine which MacSync Stealer behaviors persisted as infrastructure changed. The investigation followed the activity from C2 communication through collection, staging, and exfiltration, using recurring technical traits to connect activity across rotating domains. Execution began from an interactive shell session consistent with ClickFix social engineering, where users are tricked into pasting or running commands in Terminal. The shell session used curl to retrieve attacker-controlled payload content, followed by script-driven execution and outbound communication. 

After execution, the malware communicated with attacker-controlled infrastructure using recurring URI paths, macOS User-Agent strings, API-key headers, and curl command-line options. These request traits became durable behavioral pivots because they remained consistent even as domains changed. The activity then progressed into collection behavior targeting macOS Keychain material, browser data, locally stored credentials, cloud and Secure Shell (SSH) credentials, and sensitive files from common user directories. 

The investigation also confirmed active data exfiltration, not just beaconing. Collected data was staged under temporary paths, compressed into an archive, split into chunks, and uploaded through HTTP PUT requests using curl with the –data-binary argument. Upload parameters such as upload_id, chunk_index, and total_chunks provided additional hunting opportunities that could be correlated with process, command-line, file, and network telemetry across the attack chain. 

Discovery of additional rotating infrastructure 

To identify related MacSync Stealer infrastructure, Microsoft Defender Experts required multiple endpoint and network behaviors to align before treating a domain as connected. Correlation focused on recurring traits across payload retrieval, C2 check-in, and exfiltration, including process ancestry, command-line patterns, request paths, headers, and upload parameters. Applying this standard linked more than 30 domains, making the domain count an outcome of the behavioral methodology rather than the primary finding. 

The strongest pivots combined network request shape with endpoint execution context. Related infrastructure shared recurring URI patterns such as /curl/, /dynamic?txd=, and /gate?buildtxd=; curl command lines using -k, -s, –max-time, and –data-binary; macOS User-Agent strings; API-key headers; and HTTP PUT uploads that included upload_id, chunk_index, and total_chunks parameters. RST Cloud used recurring URI patterns to surface eleven additional candidate domains and found a static API-key value shared across four confirmed C2 domains while the build token rotated per deployment. Domains were treated as related when multiple behavioral traits aligned across process, command-line, and network telemetry, reducing reliance on any single domain indicator. 

This finding reinforces a practical defender lesson: rotating infrastructure can weaken static domain blocking and retrospective IOC matching, but repeated request patterns and process behaviors create durable hunting opportunities. Figure 1 shows representative defanged command-line patterns used as pivots across payload retrieval, C2 check-in, and chunked upload activity. 

Figure 1. Representative behavioral pivots associated with MacSync Stealer payload retrieval, C2 check-in, and chunked HTTP PUT exfiltration. 

The same behavioral patterns used to identify additional infrastructure also map to the broader end-to-end activity observed on affected macOS devices. 

Attack chain overview

The observed MacSync Stealer activity followed a fast, script-driven attack chain designed to execute quickly on macOS, collect high-value local data, stage the results, and exfiltrate the archive through rotating web infrastructure. This sequence matters because each phase produces telemetry that can be correlated across processes, command-line, file, and network events. Rather than relying on any individual domain, defenders can track the chain through recurring execution tools, URI paths, staging locations, and upload parameters. 

Figure 2. MacSync Stealer attack chain showing payload retrieval, AppleScript-assisted execution, collection, staging, chunked exfiltration, and cleanup mapped to behavioral hunting opportunities. 

Phase 1: Initial access and payload execution

Observed execution began from an interactive zsh terminal session, where curl retrieved payload content over a /curl/ path before the payload was decoded or unpacked using native utilities such as Base64 and gunzip. This phase is useful for hunting because the combination of user-facing shell activity, curl retrieval, and unpacking behavior is more durable than any single download domain. 

Phase 2: AppleScript-assisted execution

The payload used osascript to run AppleScript-assisted shell commands, blending macOS scripting with Unix command-line tooling. Observed activities included sh, cp, rm, curl, mkdir, and killall operations. This phase creates hunting value when osascript launches shell activity that quickly chains into network communication, staging, or cleanup behavior. 

Phase 3: Discovery and data collection

After execution, the malware collected host and user information, enumerated running processes and system details, and checked for cryptocurrency wallet applications, including Ledger and Trezor-related local artifacts. It then targeted macOS Keychain material, browser Safe Storage keys, browser credentials, cookies, login databases, session data, IndexedDB, LevelDB, extension storage, Safari data, Apple Notes, SSH keys, AWS credentials, Kubernetes configurations, browser profiles, browsing history, and sensitive files from common user directories. The hunting value comes from correlating sensitive data access with the later staging and upload sequence. 

Phase 4: Data staging and compression

Collected data was staged under /tmp/sync* paths and compressed into /tmp/osalogging.zip before uploading. The archive was split into multiple chunks, creating a repeatable staging and transfer pattern that defenders can correlate with preceding collection behavior and subsequent outbound curl traffic. 

Phase 5: Exfiltration over rotating infrastructure

The staged archive was uploaded through rotating infrastructure using curl and HTTP PUT requests. Observed requests included –data-binary, API-key headers, macOS User-Agent string, upload_id values, chunk_index values, and total_chunks parameters. These upload traits confirmed active data exfiltration and provided durable hunting pivots even when domains rotated. 

Phase 6: Cleanup and evidence removal

After exfiltration, the malware removed temporary archives, staging folders, lock files, and other artifacts. Although this cleanup reduced on-disk evidence, the sequence of archive creation, chunked upload, and deletion can still provide a useful behavioral correlation for defenders. 

Mitigation and protection guidance

The attack chain findings point to three mitigation priorities.

1. Organizations should reduce the risk of user-initiated Terminal execution by educating users and using platform controls that interrupt suspicious paste-and-run workflows. Microsoft’s ClickFix reporting recommends educating users not to run commands from untrusted sources and monitoring suspicious Terminal or shell activity associated with these lures. 

2. Defenders should monitor post-execution behavior when initial prevention does not stop activity, including suspicious shell usage, AppleScript-assisted commands, curl-based payload retrieval, credential-store access, temporary staging paths, and archive creation.  

3. Detection should include exfiltration monitoring for HTTP PUT uploads, –data-binary usage, upload identifiers, chunk indexes, total chunk counts, and recurring /gate URI patterns that can reveal active data theft even when C2 domains rotate. 

In macOS 26.4 and later, Apple introduced protections designed to disrupt ClickFix-style attacks, including warnings that can block potentially malicious Terminal pastes and XProtect checks that can prevent detected malicious scripts from running.

When a user attempts to paste a potentially malicious command into Terminal, macOS displays a warning that blocks the paste and explains that scammers may use Terminal instructions to compromise the Mac or the user’s privacy. 

“Possible malware, Paste blocked” 

“Your Mac has not been harmed. Scammers often encourage pasting text into Terminal to try and harm your Mac or compromise your privacy. These instructions are commonly offered via websites, chat agents, apps, files, or a phone call.” 

Organizations can also follow these recommendations to mitigate threats associated with this threat: 

• Reduce Terminal execution risk. Educate users not to paste or run Terminal commands from untrusted websites, chat messages, apps, files, or phone-based instructions. 

• Monitor suspicious Terminal usage. Alert on unusual Terminal, zsh, or shell sessions that retrieve payloads, decode content, or execute commands shortly after user interaction. 

• Detect native tool abuse. Flag unusual sequences of macOS utilities such as curl, Base64, gunzip, osascript, cp, rm, mkdir, and killall. 

• Hunt for post-execution behavior. Correlate AppleScript-assisted shell activity, curl-based payload retrieval, credential-store access, temporary staging paths, archive creation, and cleanup behavior. 

• Protect credential stores. Detect unauthorized access to Keychain material, browser credential stores, SSH keys, cloud credentials, and sensitive files in common user directories. 

• Monitor data staging. Alert on sensitive artifact collection followed by compression, archive creation, or staging under temporary paths such as /tmp/sync*. 

• Monitor exfiltration patterns. Identify curl-based HTTP PUT uploads that use –data-binary, API-key headers, upload_id, chunk_index, total_chunks, or recurring /gate URI patterns. 

• Restrict suspicious outbound traffic. Block or investigate connections to suspicious, newly registered, or behaviorally related domains while continuing to hunt on request patterns that may persist after domains rotate. 

Microsoft also recommends the following mitigations to reduce the impact of this threat. 

• Turn on cloud-delivered protection in Microsoft Defender Antivirus or the equivalent for your antivirus product to cover rapidly evolving attacker tools and techniques. Cloud-based machine learning protections block a majority of new and unknown threats. 

• Enable network protection and web protection to help prevent connections to malicious websites, phishing pages, and attacker-controlled infrastructure used for malware delivery, command-and-control communication, and data exfiltration. 

• Enable tamper protection to help prevent unauthorized changes to Microsoft Defender security settings and reduce the risk of attackers disabling or weakening endpoint protections. 

Microsoft Defender XDR detections 

Microsoft Defender XDR customers can refer to the list of applicable detections below. Microsoft Defender XDR coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog. 

Customers with provisioned access can also use Microsoft Security Copilot in Microsoft Defender to investigate and respond to incidents, hunt for threats, and protect their organization with relevant threat intelligence. 

 Threat intelligence reports

Microsoft customers can use the following reports in Microsoft products to get the most up-to-date information about the threat, malicious activity, infrastructure, and techniques discussed in this blog. These reports provide intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer environments. 

Microsoft Defender XDR Threat analytics

From ClickFix to code signed: the quiet shift of MacSync Stealer malware. 

Microsoft Security Copilot customers can also use the Microsoft Security Copilot integration in Microsoft Defender Threat Intelligence, either in the Security Copilot standalone portal or in the embedded experience in the Microsoft Defender portal to get more information about this threat. 

Advanced hunting queries

The following advanced hunting queries can help identify MacSync Stealer behaviors observed with this threat. Use these queries as starting points and tune the time range, device scope, and allowlists for your environment. 

Hunting objective: Identify rotating infrastructure by request shape

This query looks for curl-initiated network activity that matches recurring MacSync Stealer URI paths and upload parameters across domains. 

DeviceNetworkEvents 
| where InitiatingProcessFileName =~ "curl" 
| where RemoteUrl has_any ("/curl/", "/dynamic?txd=", "/gate?buildtxd=", "upload_id=", "chunk_index=", "total_chunks=")

Hunting objective: Detect payload retrieval over /curl/ 

This query focuses on initial payload retrieval behavior where curl reaches a /curl/ path, helping identify delivery activity without relying on a specific domain. 

DeviceNetworkEvents 
| where InitiatingProcessFileName =~ "curl" 
| where RemoteUrl has "/curl/" 

Hunting objective: Detect chunked exfiltration over curl HTTP PUT 

This query targets active exfiltration behavior by looking for curl HTTP PUT uploads that use –data-binary and chunked upload parameters. 

DeviceNetworkEvents 
| where InitiatingProcessFileName =~ "curl" 
| where InitiatingProcessCommandLine has_all ("-X PUT", "--data-binary") 
| where RemoteUrl has_any ("upload_id=", "chunk_index=", "total_chunks=", "/gate?buildtxd=") 

Hunting objective: Find curl command lines with MacSync infrastructure traits 

This query searches endpoint process telemetry for curl command lines containing the headers, URI paths, and upload parameters used as durable behavioral pivots. 

DeviceProcessEvents 
| where FileName =~ "curl" 
| where ProcessCommandLine has_any ("api-key", "/curl/", "/dynamic", "/gate", "--data-binary", "upload_id=", "chunk_index=", "total_chunks=", "%{http_code}") 

Hunting objective: Identify AppleScript-launched shell activity 

This query looks for osascript activity that launches shell commands or native utilities commonly seen in the observed post-execution chain. 

DeviceProcessEvents 
| where FileName =~ "osascript" 
| where ProcessCommandLine has_any ("sh -c", "cp ", "rm ", "curl ", "mkdir ", "killall", "dscl") 

MITRE ATT&CK techniques observed

The following MITRE ATT&CK mappings reflect behaviors observed during the MacSync Stealer investigation. The mapping emphasizes the same behavioral pivots used throughout this blog, including shell and AppleScript-assisted execution, payload retrieval, credential and browser data theft, sensitive file collection, staging, chunked exfiltration, cleanup, and rotating infrastructure. 

Execution 

• T1059.004 Command and Scripting Interpreter: Unix Shell | An interactive zsh terminal session was used to run curl commands, decode or unpack payload content with base64 and gunzip, and execute shell commands. 

• T1105 Ingress Tool Transfer | curl downloaded payload content from attacker-controlled infrastructure using recurring payload retrieval paths. 

Discovery 

• T1082 System Information Discovery | The malware collected host and user information during environment discovery. 

• T1057 Process Discovery | The malware enumerated running processes and system configuration before continuing collection and credential-access activity. 

• T1518 Software Discovery | The malware checked for cryptocurrency wallet applications such as Ledger and Trezor. 

Credential Access 

• T1555.001 Credentials from Password Stores: Keychain | The malware created a temporary keychain-grabbing script, attempted to extract browser Safe Storage keys, and accessed or attempted to unlock the macOS Keychain. 

• T1555.003 Credentials from Password Stores: Credentials from Web Browsers | The malware collected browser credentials, cookies, login databases, session data, IndexedDB, LevelDB, and extension storage from Chrome, Brave, Edge, Opera, Vivaldi, Arc, Chromium, and other browsers. 

Collection 

• T1005 Data from Local System | The malware searched Downloads, Documents, and Desktop and collected sensitive file types including PDF, DOCX, TXT, KEY, PEM, KDBX, OVPN, WALLET, and SEED files. 

• T1552.001 Unsecured Credentials: Credentials in Files | The malware harvested SSH keys, AWS credentials, Kubernetes configurations, browser profiles, Apple Notes, Safari data, and other locally stored secrets. 

• T1560.001 Archive Collected Data: Archive via Utility | Collected data was staged under /tmp/sync* and compressed into /tmp/osalogging.zip before upload. 

Command and Control 

• T1071.001 Application Layer Protocol: Web Protocols | C2 communication used web protocols with recurring paths such as /dynamic?txd= and /gate?buildtxd=, macOS User-Agent strings, API-key headers, and rotating domains. 

Exfiltration 

• T1041 Exfiltration Over C2 Channel | Collected data was uploaded to attacker-controlled infrastructure using recurring /gate URI patterns and chunked HTTP PUT requests. 

• T1020 Automated Exfiltration | The malware automated upload activity using curl with HTTP PUT, –data-binary, upload identifiers, chunk_index, and total_chunks parameters. 

• T1030 Data Transfer Size Limits | The archive was split into multiple chunks before upload, as shown by repeated chunk_index and total_chunks parameters in exfiltration requests. 

Defense Evasion 

• T1070.004 Indicator Removal: File Deletion | Temporary archives, staging folders, lock files, and other artifacts were removed after exfiltration. 

• T1140 Deobfuscate/Decode Files or Information | Payload content was decoded or unpacked using base64 and gunzip before execution. 

Behavioral Hunting Pivots 

The following command-line patterns, URL paths, and URL parameters were observed in activity consistent with MacSync Stealer. Use these durable behavioral pivots with process and network context to investigate related activity as infrastructure rotates; then use the point-in-time domain indicators in the IOC section to enrich and validate those findings. 

Indicators of compromise (IOC)

The following domain indicators were observed in activity consistent with MacSync Stealer. Treat them as point-in-time evidence: use them to enrich and validate matches from the behavioral pivots above, and correlate any hits with process and network context because related infrastructure may rotate quickly. 

References

References used for external context and related defensive guidance: 

• MacSync Stealer: C2 Infrastructure Rotation – RST Cloud. RST Cloud, May 8, 2026. 

• ClickFix campaign uses fake macOS utilities lures to deliver …. Microsoft, May 6, 2026. 

• Protecting against malware in macOS – Apple Support  December 19, 2024 

Learn more

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.   

• Learn more about securing Copilot Studio agents with Microsoft Defender  
• Evaluate your AI readiness with our latest Zero Trust for AI workshop.
• Microsoft 365 Copilot AI security documentation 
• How Microsoft discovers and mitigates evolving attacks against AI guardrails 

The post Hunting MacSync Stealer infrastructure through behavioral pivots appeared first on Microsoft Security Blog.

Manual brand impersonation takedowns fail because attackers move faster than ticket-based abuse reports can resolve — phishing pages and fake executive profiles often do their damage within hours of going live, while manual removal can take days. A managed takedown program pairs continuous, verified monitoring with pre-authorized removal (in-certain cases), cutting the exposure window from days to hours. This matters most for consulting and professional services firms, where a spoofed domain or fake executive profile can compromise the client trust the business is built on.

How UNC3753 targeted US professional services firms in 2026

Between January and May of 2026, Google's Mandiant threat intelligence team tracked a financially motivated extortion campaign — attributed to a group known as UNC3753, or "Luna Moth," or "Silent Ransom Group" — working its way through dozens of professional, legal, and financial services organizations across the United States. The approach was almost old-fashioned. A benign-looking email about a data migration or an unpaid invoice, a follow-up phone call from someone posing as IT support, and a request to install "remote monitoring" software to fix the problem. No exploit. No malware dropped on day one. Just a firm's own trust in its brand and its people, turned against it.

It's a useful — if unsettling — reminder of why brand and executive impersonation isn't a side issue for professional services firms. It's often the entry point.

How much does phishing and impersonation actually cost US businesses

The scale of the problem, in dollar terms, is no longer subtle. The FBI's Internet Crime Complaint Center logged just over one million complaints in 2025 — the highest volume in the program's history — with phishing and spoofing making up roughly a fifth of all reports. Losses tied to phishing alone roughly tripled year-over-year, and business email compromise, which almost always starts with an attacker impersonating someone the victim trusts, accounted for over $3 billion in reported losses on its own. The mechanics of that damage matter too: the overwhelming majority of BEC losses move through wire transfer or ACH, rails that are fast, largely irreversible, and unforgiving of a slow response.

Put those two facts together and a pattern emerges. Impersonation attacks — of a brand, a partner, an executive, a vendor invoice — aren't rare or exotic. They're the default opening move. And once the fraudulent domain, profile, or listing is live, the clock the defender is racing isn't measured in days. It's measured in hours, sometimes less, before money moves or credentials are harvested.

Why are consulting and professional services firms specifically targeted?

Professional services firms occupy a strange position in the threat landscape. They're rarely the most technically fortified target, but they're consistently one of the most valuable ones. A consulting firm doesn't just protect its own data — it holds engagement records, financial models, and confidential strategy documents belonging to dozens of clients across industries. About 29% of U.S. law firms reported having experienced a security breach at some point, according to the ABA's most recent Legal Technology Survey — up from 25% just two years earlier. The same dynamic applies to consultancies. The firm is a single point of entry into a much larger web of client relationships.

That's precisely the exposure described in Cyble's case study of a U.S. consulting organization managing highly sensitive engagement data, confidential client information, and a large, distributed workforce operating across the country. As the case study describes it, the firm's brand, executives, and digital infrastructure were frequent targets specifically because of the trust clients placed in them as an advisor. Senior partners were likely of being impersonated through fake social profiles and spoofed domains. Fraudulent job postings and phishing campaigns leaned on the firm's own credibility to look legitimate. The attacker doesn't need to breach the firm's network if a client can be convinced, through a look-alike domain or a cloned executive profile, to simply hand over what the attacker wants.

That's the mechanism UNC3753 exploited nationally in 2026, and it's the exact exposure this consulting firm was trying to close.

Also read: Ransomware Threats in the Americas H1 2026: Dissecting the Regional Attack Patterns and Dominant Actors

What is the "whack-a-mole" problem in brand protection?

Here's where most brand protection programs quietly fail, and it isn't a detection problem — it's a speed problem.

A typical manual takedown workflow looks something like this: someone on the security or marketing team spots a phishing page or a fake LinkedIn profile impersonating a partner. They file an abuse report with the registrar or the platform. They wait. Maybe they follow up. Eventually, the page comes down — but by then, a new one has often already gone live, sometimes registered by the same actor under a slightly different domain.

This was exactly the challenge the consulting firm faced before its engagement with Cyble. Identifying and removing phishing pages, fraudulent job postings, and impersonating domains was, in the case study's own words, reactive and resource-intensive, leaving the brand exposed for longer than the firm considered acceptable. It's a program that looks active — tickets filed, pages eventually removed — while the actual window of exposure, the hours where a client or job candidate could act on the fake page, stays wide open. Volume of takedowns filed is an easy number to report. Speed of resolution is the number that actually protects anyone.

What does managed takedown response actually involve

The shift the case study describes isn't just "faster takedowns" — it's a change in the operating model, from reactive point-solution to continuous, managed coverage. Three pieces work together in the deployment:

• Brand and Executive Monitoring continuously scans for phishing domains, fraudulent job postings, and impersonation attempts using the firm's name, alongside dedicated monitoring of senior leadership profiles across social platforms — catching the fake partner LinkedIn account or spoofed domain before it's had time to circulate.
• Verification before escalation means the security team isn't drowning in unconfirmed alerts. Threats are validated as genuine before they ever reach someone's desk, which is what separates consolidated intelligence from just another noisy dashboard.
• Managed Takedown Services then handle the actual removal — confirmed phishing pages, impersonating domains, and fraudulent listings — without the internal team having to individually chase registrars and platforms one abuse ticket at a time.

The outcome is a meaningfully shortened window between detection and removal — turning a slow, manual, ticket-by-ticket grind into something closer to continuous coverage. That's the real distinction between a takedown service and a takedown program: one reacts when someone happens to notice a fake page; the other is built to notice, verify, and resolve on a timeline that assumes attackers move fast, because they do.

Why client trust is the real asset at risk

For a consulting firm, the financial cost of an impersonation attack is rarely the headline risk. The deeper cost is what it does to the relationship a firm's entire business is built on. When a client, a job candidate, or a prospective hire can't tell the difference between a legitimate email from the firm and a spoofed one, the firm's advisory credibility — the thing it's actually selling — starts to erode. That's a slower, quieter kind of damage than a wire fraud loss, but for a professional services firm, it may be the more expensive one.

The lesson from both the national threat data and this specific engagement is the same – brand and executive impersonation isn't a marketing nuisance to be cleaned up occasionally. It's a live attack surface, moving at a speed that manual, ad hoc takedown processes were never built to match. Firms that treat it that way — with continuous monitoring, verified alerts, and managed resolution — are the ones that keep the exposure window measured in hours instead of days.

Frequently asked questions (FAQs)

What is a brand impersonation takedown service?

A brand impersonation takedown service identifies fraudulent domains, phishing pages, fake social media profiles, and impersonating job listings that misuse a company's name or logo, then works with registrars, hosting providers, and platforms to have that content removed.

How long does it take to take down a phishing site?

Timelines vary by registrar and hosting provider, but manual, ticket-based takedown requests commonly take days to resolve. Managed takedown programs that pre-verify threats and maintain direct relationships with providers can shorten that window to hours.

Why do manual takedown processes fail against brand impersonation?

Manual processes fail because they're reactive: a person has to notice the fake page, file a report, and wait for a third party to act, while attackers can register replacement domains faster than any single report gets resolved. The volume of tickets filed can look productive even while the actual exposure window stays open.

What's the difference between takedown volume and takedown speed?

Takedown volume measures how many fraudulent pages were reported or removed over time. Takedown speed measures how quickly a live threat is detected, verified, and taken down after it appears. Speed is the metric that actually limits damage, since most harm from a phishing page happens in its first hours online.

How can consulting and professional services firms protect executives from impersonation?

Dedicated executive monitoring tracks senior leaders' names and likenesses across social platforms and the web to catch fake profiles, spoofed communications, and impersonation attempts early, ideally paired with managed takedown so confirmed threats are removed without requiring the executive or internal team to handle it themselves.

Sources:

FBI Internet Crime Complaint Center, 2025 Internet Crime Report;
Cyble, "How Cyble Delivered Unified Multi-Layered Threat Intelligence to a U.S. Consulting Organization";
Google/Mandiant, "Ongoing Targeted Campaign Against US Law Firms" (2026);
American Bar Association Legal Technology Survey.

The post Brand Impersonation Takedown: From Whack-a-Mole to Managed Response appeared first on Cyble.

The Dutch National Cyber Security Centre (NCSC) issued a warning after being notified of several incidents where a vulnerability in Apple’s Screen Sharing feature was exploited to install Monero cryptominers.

The vulnerability, tracked as CVE-2026-65400, was patched by Apple on August 6. It is an authentication-bypass flaw in macOS Screen Sharing that can let an attacker on the network connect without valid credentials.

macOS’s built-in Screen Sharing service is a remote-control feature commonly associated with port 5900. Successful exploitation can allow a remote attacker on a reachable network to authenticate to the service without legitimate credentials.

Apple said the bug was fixed through “improved state management,” which suggests an authentication-flow or session-state validation failure rather than a cryptographic break.

The patch was issued for macOS Tahoe 26.6.1, Sequoia 15.7.9, and Sonoma 14.8.9. Practical exposure requires Screen Sharing to be enabled, so the highest-risk systems are those where port 5900 is internet-accessible, typically through a router port-forward, public IP assignment, or hosting-provider setup. Hosts reachable only from an internal network are still potentially exposed, but attackers would have to gain a position on that network.

An attacker could view and control the Mac remotely because that is the function Screen Sharing provides. NCSC says active cases involved attackers gaining root access and installing cryptomining software, specifically for Monero mining.

The criminals likely chose Monero mining because it does not depend on heavily specialized, application-specific integrated circuits (ASICs), but can be done with any CPU or GPU.

Cryptomining isn’t necessarily the worst an attacker could do. With a root-level compromise an attacker could enable persistence, data theft, credential and key harvesting, deployment of additional malware, and lateral movement.

How to stay safe

Install the update

The best way to protect your Mac is to install the update.

To update macOS on any supported Mac, use the Software Update feature, which Apple designed to work consistently across all recent versions. Here are the steps:

• Click the Apple menu in the upper-left corner of your screen.
• Choose System Settings (or System Preferences on older versions).
• Select General in the sidebar, then click Software Update on the right. On older macOS, just look for Software Update directly.
• Your Mac will check for updates automatically. If updates are available, click Update Now (or Upgrade Now for major new versions) and follow the on-screen instructions. Before you upgrade to macOS Tahoe 26, please read these instructions.
• Enter your administrator password if prompted, then let your Mac finish the update (it might need to restart during this process).
• Make sure your Mac stays plugged in and connected to the internet until the update is done.

Make sure Screen Sharing is disabled

If you can’t update immediately, check whether Screen Sharing is enabled and turn it off if you don’t use it.

1.  Click the Apple menu in the top-left corner of the screen.
2. Select System Settings.
3. In the left sidebar, click General.
4. Click Sharing on the right; you may need to scroll down.
5. Find Screen Sharing:
   • If the switch is off/grey, it is disabled.
   • If the switch is on/colored, click it to switch it off.

Also check Remote Management on that same Sharing page. It provides another remote-control route and should be off unless the owner knowingly uses it for work or IT support.

Macs need protection too

Malwarebytes Premium Security for Mac stops threats and protects your Mac and personal files from hackers and cybercriminals.

The Americas carried the heaviest ransomware burden of any region on the planet in the first half of 2026. According to Cyble Research and Intelligence Labs (CRIL), North and South America combined experienced 2,188 documented ransomware attacks between January and June 2026.

That single figure — 2,188 attacks — represents more than 57% of the 3,836 ransomware incidents CRIL tracked worldwide, making the Americas the undisputed center of gravity for global ransomware operations.

But the Americas is not a single threat theatre — it is two. North America alone absorbed 1,981 attacks, driven by a mature, multi-group Ransomware-as-a-Service (RaaS) economy competing for market share. South America, by contrast, recorded 207 attacks concentrated around a much smaller set of operators, with one group — The Gentlemen — claiming nearly a quarter of all regional incidents outright. Understanding the Americas means understanding both halves of that story: a saturated northern market and a consolidating southern one.

North America vs. South America: Two Distinct Ransomware Landscapes

Security leaders operating across the hemisphere cannot apply a single threat model to both sub-regions. The data shows meaningfully different attacker behavior, concentration, and monetization strategy north and south of the equator.

Why the split matters: North America's threat landscape is a genuine marketplace — dozens of RaaS operators compete for affiliate loyalty, and no single group commands more than a fifth of total volume. South America's landscape is more consolidated, with three groups controlling well over half of all attacks.

For defenders, that means North American organizations need broad-spectrum threat intelligence covering a long tail of active groups, while South American organizations can build highly specific defenses against a short list of named adversaries.

The Five Dominant Ransomware Groups Targeting Americas

Across both sub-regions combined, five ransomware operators account for the overwhelming share of documented activity: Qilin, Akira, INC Ransom, Dragonforce, and The Gentlemen. Together, these five groups are linked to roughly 1,148 of the Americas' 2,188 attacks — approximately 52.5% of all regional ransomware activity.

1. Qilin: The Biggest Ransomware Threat in the Americas

Attack Volume: 410 documented incidents across the Americas (370 in North America, 40 in South America) — 18.7% of the regional total.

Qilin is the single most prolific ransomware actor operating in the hemisphere, and its dominance is not evenly spread — it is concentrated hardest in the United States.

Geographic Concentration:

• United States: 323 attacks (the single largest country-level concentration of any group, anywhere)
• Canada: 33 attacks
• Argentina: 13 attacks
• Broader South America: 40 attacks

Worldwide Sectoral Targeting: Qilin's targeting logic is deliberate rather than opportunistic:

• Construction: 108 incidents (primary focus)
• Professional Services: 90 incidents (legal, accounting, consulting firms)
• Manufacturing: 67 incidents
• Healthcare: 53 incidents
• IT & ITES: 43 incidents

Operational Characteristics:

Qilin's affiliate model is built for scale. Initial access brokers handle reconnaissance and compromise, mid-tier operators manage lateral movement, and dedicated crews execute encryption and exfiltration. This compartmentalization lets Qilin run dozens of concurrent operations across the United States without any single point of failure. The group's near-total dominance of the American ransomware market (323 of 1,721 US attacks) suggests either an unusually large affiliate roster or a payout structure attractive enough to pull operators away from competing platforms.

Why Qilin Dominates:

• Affiliate Loyalty: Competitive payout splits keep operators recruiting and retaining talent
• Rapid Exploit Weaponization: Fast turnaround from vulnerability disclosure to active exploitation
• Sector Fluency: Deep understanding of which industries face the highest downtime cost
• Established Data Brokerage Ties: Exfiltrated data reliably reaches monetization channels

Americas Security Implications: Any organization in construction, professional services, manufacturing, or healthcare operating in the US or Canada should treat Qilin as a primary named threat, not a generic ransomware risk.

2. Akira: North America's Persistent Operator

Attack Volume: 268 documented incidents, almost entirely concentrated in North America — 12.2% of the regional total

Akira is the second most active group in the Americas, and unlike Qilin, its footprint is almost exclusively North American. CRIL's data shows Akira's South American presence is negligible to date.

Geographic Concentration:

• United States: 247 attacks (92% of Akira's total Americas volume)
• Canada: Remaining North American activity
• South America: Minimal to no confirmed activity

Worldwide Sectoral Targeting:

• Construction: 57 incidents
• Manufacturing: 54 incidents
• Professional Services: 47 incidents
• Consumer Goods: 19 incidents
• IT & ITES: 16 incidents

Operational Characteristics:

Akira has built a reliable playbook around compromising small-to-medium-sized businesses through unpatched public-facing network devices, then pivoting into construction and manufacturing environments where downtime tolerance is lowest. The group's consistency — rather than explosive growth — is its defining trait; it has neither the explosive scale of Qilin nor the geographic diversification of The Gentlemen, but it reliably executes against the same target profile month after month.

Americas Security Implications: North American SMBs in construction, manufacturing, and professional services should assume Akira is actively scanning for exposed remote access infrastructure. Its South American absence should not be mistaken for permanence — RaaS groups expand geographically once North American markets saturate.

3. INC Ransom: The Law-Firm Specialist

Attack Volume: 171 documented incidents (164 in North America, 7 in South America) — 7.8% of the regional total

INC Ransom distinguishes itself through sector specialization rather than volume. The group shows a clear, repeated preference for Professional Services organizations — particularly law firms — leveraging the sensitive, high-stakes nature of legal client data.

Geographic Concentration:

• United States: 154 attacks
• Canada: 6 attacks
• Brazil: 4 attacks

Worldwide Sectoral Targeting:

• Professional Services: 58 incidents (primary focus, with a documented preference for law firms)
• Construction: 27 incidents
• Manufacturing: 26 incidents
• Healthcare: 21 incidents
• Organisation/Non-profit: 12 incidents

Operational Characteristics:

INC Ransom's rapid operational pace and consistent targeting of law firms, healthcare providers, and transportation/energy operators reflects a strategy built entirely around double-extortion leverage. The sensitivity of the data matters more than the size of the victim. A regional law firm holding privileged client communications is, to INC Ransom, a more valuable target than a much larger manufacturer with less sensitive data.

Americas Security Implications: Law firms, accounting practices, and consulting shops across the US, Canada, and Brazil should assume INC Ransom is actively targeting client confidentiality as leverage — not just encrypting file servers for disruption.

4. Dragonforce: The Cross-Border Supply-Chain Operator

Attack Volume: 153 documented incidents (148 in North America, 5 in South America) — 7.0% of the regional total

Dragonforce maintains an aggressive operational tempo focused heavily on the United States, with a strategy that suggests supply-chain-aware targeting rather than random opportunism.

Geographic Concentration:

• United States: 135 attacks
• Canada: 11 attacks
• South America: 5 attacks

Worldwide Sectoral Targeting:

• Construction: 48 incidents
• Manufacturing: 31 incidents
• Professional Services: 28 incidents
• IT & ITES: 18 incidents
• BFSI: 17 incidents

Operational Characteristics:

Dragonforce's manufacturing and construction focus mirrors Qilin's and Akira's playbooks, but its concentration in the US combined with limited-but-present South American activity hints at interest in transnational manufacturing supply chains. North American organizations with manufacturing partners or subsidiaries in Latin America should treat this as a lateral-access risk, not just a direct-targeting one.

Americas Security Implications: Manufacturers and construction firms with cross-border operations — a common structure across USMCA supply chains — should extend Dragonforce-specific monitoring to subsidiaries and vendors, not just headquarters networks.

5. The Gentlemen: South America's Dominant Threat

Attack Volume: 146 documented incidents (100 in North America, 46 in South America) — 6.7% of the regional total, but the single most active ransomware group in South America specifically.

While The Gentlemen rank fifth across the combined Americas, they are the #1 threat actor in South America on their own — responsible for roughly 22% of every ransomware attack recorded in that sub-region.

Geographic Concentration:

• United States: 77 attacks
• North America: 100 attacks
• Brazil: 15 attacks
• South America: 46 attacks (largest single-group share in the sub-region)

Worldwide Sectoral Targeting:

• Manufacturing: 56 incidents
• Construction: 45 incidents
• Healthcare: 37 incidents
• IT & ITES: 36 incidents
• Consumer Goods: 34 incidents

Operational Characteristics:

The Gentlemen are a relatively new operator that has achieved outsized scale in a short window, and their South American concentration is the most important regional signal in this dataset. Unlike Qilin or Akira — which built North American dominance first and are only beginning to diversify — The Gentlemen appear to have prioritized South America as a primary theatre from early in their operational life, an unusual strategic choice that may reflect lower defensive maturity, less aggressive law enforcement cooperation, or simply less competitive pressure from other RaaS operators in the sub-region.

Americas Security Implications: South American organizations — especially in healthcare, manufacturing, and IT services — should treat The Gentlemen as their single highest-priority named adversary. North American organizations should not discount them either; 100 US-focused attacks is a substantial footprint for a group still building its brand.

Other Notable Threats: Play, LockBit, and CL0P

Three additional groups warrant inclusion in any Americas threat model:

• Play (144 attacks, North America only): Continues its "Big Game Hunting" approach layered with high-volume SMB attacks via unpatched public-facing network devices, concentrated almost entirely on US and Canadian construction, professional services, and manufacturing targets.
• LockBit (80 attacks combined — 47 in North America, 33 in South America): Despite sustained international law enforcement pressure and repeated takedown attempts, LockBit remains operationally resilient across both sub-regions, notably compromising Chile's Clínica Dávila in South America.
• CL0P (93 attacks combined — 91 in North America, 2 in South America): Operated differently from its peers, executing a large-scale campaign concentrated in January and February 2026 that exploited a single zero-day vulnerability across hundreds of organizations at once — reminiscent of the group's historical MOVEit campaign.

Also read: The Most Active Threat Actors of H1 2026

The Five Most Targeted Nations in the Americas

United States: The Global Ransomware Epicenter

Attack Volume: 1,721 ransomware attacks — 78.7% of all Americas ransomware activity, and roughly 45% of every ransomware attack recorded worldwide.

No other country on Earth comes close to the volume of ransomware activity absorbed by the United States in H1 2026. The country functions as the default target for nearly every major RaaS operator active today.

Threat Actor Concentration:

• Qilin: 323 attacks
• Akira: 247 attacks
• INC Ransom: 154 attacks
• Dragonforce: 135 attacks
• Play: 134 attacks

Sectoral Breakdown: Manufacturing, Professional Services, Construction, and Healthcare bear the brunt, consistent with the broader North American pattern of operationally sensitive, low-downtime-tolerance industries.

Why the United States Faces Maximum Pressure

The scale of the US economy, its dense concentration of mid-market manufacturers, law firms, and healthcare providers, and its comparatively high ransom-payment history combine to make it the most economically rational target for every major ransomware operator. US organizations also frequently anchor cross-border supply chains stretching into Canada, Mexico, and South America — meaning a US compromise can cascade into lateral access against hemispheric partners.

Defensive Priority: US organizations across construction, manufacturing, professional services, and healthcare should assume Qilin, Akira, INC Ransom, Dragonforce, Play, and The Gentlemen are all actively scanning for exploitable entry points into their networks simultaneously — not sequentially.

Canada: The Cross-Border Extension

Attack Volume: 179 ransomware attacks — 8.2% of the regional total.

Canada's threat profile closely tracks the United States, reflecting deep economic integration and shared supply chains rather than a distinct targeting logic of its own.

Sectoral Breakdown: Manufacturing, professional services, and construction dominate, mirroring the US pattern almost directly.

Why Canada Faces Sustained Pressure

Canadian organizations are frequently subsidiaries, suppliers, or joint-venture partners of US enterprises, which means the same RaaS groups saturating the US market extend naturally northward. Cross-border manufacturing in particular creates lateral access opportunities that Dragonforce and Akira appear well-positioned to exploit.

Defensive Priority: Canadian organizations should not assume distance from US headquarters provides insulation — the same threat actors, exploiting the same vulnerability classes, are already active on both sides of the border.

Brazil: The Financial Malware and Ransomware Convergence Point

Attack Volume: 71 ransomware attacks — 3.2% of the regional total, but the largest single concentration in South America.

Brazil represents South America's most complex threat environment, combining traditional ransomware pressure with a maturing, sophisticated financial malware ecosystem.

Threat Actor Concentration: The Gentlemen (15 attacks), LockBit, and a fragmented tail of smaller operators.

Sectoral Breakdown: Government & Law Enforcement, BFSI, and Healthcare are the most consistently targeted sectors.

Why Brazil Faces a Dual Threat

Beyond ransomware, Brazil emerged in H1 2026 as a focal point for new Android banking trojan families — TCLBANKER and BTMOB RAT — which use self-propagation, evasion techniques, and Malware-as-a-Service (MaaS) distribution models to target banking and cryptocurrency users directly. Brazil also suffered an alleged 250-million-record breach of Serasa, one of the country's largest credit bureaus, alongside an access sale allegedly targeting the Central Bank of Brazil — a listing that, if genuine, represents one of the most significant initial-access offerings tracked anywhere in the report.

Defensive Priority: Brazilian financial institutions should treat mobile banking malware and ransomware as converging risks rather than separate problems — the same underground economy is monetizing both. Government and BFSI entities should assume access-broker listings referencing critical national infrastructure require immediate incident-response-level validation, not routine monitoring.

Mexico: The Emerging Nearshoring Risk

Attack Volume: 39 ransomware attacks — 1.8% of the regional total.

Mexico's attack volume is meaningfully lower than the US, Canada, or Brazil, but its position within North American manufacturing supply chains — accelerated by ongoing nearshoring trends — makes it a nation to watch closely rather than dismiss.

Why Mexico Warrants Increased Attention

As global manufacturers continue relocating production closer to the US market, Mexican facilities increasingly sit inside the same supply chains that Dragonforce, Akira, and Qilin already target aggressively north of the border. Lower current attack volume may reflect earlier-stage targeting rather than lower risk — a pattern security teams should not mistake for durable safety.

Defensive Priority: Manufacturers with Mexican operations should extend the same OT/IT segmentation and vulnerability management discipline applied to US and Canadian facilities to their Mexican sites, rather than treating them as lower priority.

Colombia: Where Hacktivism Meets Cybercrime

Attack Volume: 33 ransomware attacks — 1.5% of the regional total.

Colombia's ransomware volume is modest, but the country stands out for the density of ideologically motivated activity layered on top of financially driven attacks.

Why Colombia Faces a Blended Threat

Groups such as Anonymous Colombia (#OpColombia) ran active campaigns throughout H1 2026 blending website defacement, DDoS attacks, and data leak activity — consistent with the broader South American pattern in which hacktivist-branded channels frequently overlap with financially motivated cybercrime infrastructure.

Defensive Priority: Colombian government and law enforcement entities — the most frequently targeted sector across South America overall — should treat hacktivist claims as credible threat intelligence signals rather than dismissing them as purely ideological noise.

Where Americas Organizations Face Maximum Risk: A Sectoral Analysis

Professional Services: One of the Top Targets

Attack Volume: The second most heavily impacted sector in North America.

Professional services firms — law, accounting, and consulting practices — are one of the top jobs on North America's ransomware target list, driven overwhelmingly by INC Ransom and AiLock's aggressive targeting of client-confidential data.

Why Professional Services Are Targeted

1. Privileged Data Concentration: Legal privilege and client confidentiality create existential regulatory and reputational exposure that threat actors exploit for maximum ransom leverage.
2. Regulatory Pressure: Breach notification requirements incentivize rapid ransom payment to avoid compounding disclosure penalties.
3. Trust-Based Business Model: A single confirmed breach can permanently damage client relationships built entirely on confidentiality.
4. Documented Actor Preference: INC Ransom has shown a specific, repeated preference for law firms — this is not incidental targeting.

Notable Incident Pattern: AiLock's activity stood out for a coordinated wave of victim disclosures on a single day — March 3, 2026 — a pattern consistent with mass-exploitation of a shared vulnerability rather than individually researched targeting.

Defensive Recommendations:

• Segregate client data on separate network segments with distinct, audited access controls
• Deploy data loss prevention (DLP) with aggressive egress monitoring for client-data exfiltration
• Maintain comprehensive access logs for all sensitive client-data touchpoints
• Evaluate ransomware-specific cyber insurance addressing confidentiality exposure

Construction and Manufacturing: The Downtime Economy

Attack Volume: Construction and Manufacturing rank first and third in North America; combined, they represent the largest share of Qilin, Akira, Dragonforce, and The Gentlemen's worldwide targeting.

Constructions and manufacturing share a common vulnerability across the Americas: both operate on tight, contractually enforced timelines where downtime translates directly into cascading financial penalties.

Why Construction and Manufacturing Are Targeted

1. Time-Sensitive Financial Exposure: Missed construction deadlines trigger contractual penalties; halted production lines trigger lost revenue and breached delivery commitments.
2. OT/IT Convergence: Modern factories and job sites increasingly integrate operational technology with corporate IT, creating exploitation bridges unavailable in pure-IT industries.
3. Supply-Chain Complexity: Both industries depend on dense webs of subcontractors and suppliers — compromising one upstream partner can provide lateral access into prime contractors.
4. Cross-Border Exposure: US-Canada-Mexico manufacturing integration (and increasingly, US-Brazil trade relationships) means a single compromise can propagate across national borders.

Defensive Recommendations:

• Implement airgapped network segmentation between OT and corporate IT environments
• Prioritize vulnerability patching for network appliances and identity systems over blanket patch cycles
• Maintain fully offline, immutable backups of critical project and production data
• Extend third-party risk assessments to subcontractors, suppliers, and cross-border subsidiaries

Healthcare: South America's Critical Infrastructure Threat

Attack Volume: One of the top four most heavily impacted sectors in South America.

Healthcare organizations across the Americas — but particularly in South America — face a threat dynamic distinct from financial pressure alone: ransomware attacks against hospitals directly endanger patient safety.

Why Healthcare Is Targeted

1. Patient Safety Leverage: Downtime in diagnostic systems, pharmaceutical dispensing, and patient records directly threatens continuity of care, creating existential pressure to pay quickly.
2. Documented Regional Incidents: The Gentlemen's claimed attack on Primero Medicina Privada and LockBit's compromise of Chile's Clínica Dávila both illustrate ransomware groups' willingness to target hospital networks directly.
3. Data Value: Patient medical records and clinical data command premium prices on dark web marketplaces.
4. System Complexity: Healthcare IT environments blend legacy diagnostic equipment, electronic health records, and connected medical devices — each with distinct security postures.

Defensive Recommendations:

• Implement complete network isolation between clinical systems and corporate IT
• Deploy redundant diagnostic and pharmaceutical systems capable of manual fallback operation
• Encrypt all patient medical records end-to-end
• Build healthcare-specific incident response plans addressing patient notification and continuity of care

Agriculture & Livestock: The Americas' Emerging Supply-Chain Target

Attack Volume: 33% of all North American initial access listings — the second-most targeted sector in the region's access brokerage market.

A distinctive Americas finding: initial access brokers targeting the region show unusually strong interest in Agriculture & Livestock, second only to Technology.

Why Agriculture & Livestock Is an Emerging Target

North America's food supply chain increasingly depends on connected logistics, cold-chain monitoring, and precision agriculture technology — creating an attack surface that did not meaningfully exist a decade ago. Access brokers appear to be positioning themselves ahead of ransomware operators, selling footholds into agricultural operations before ransomware crews weaponize them. This mirrors a pattern seen elsewhere globally but is particularly pronounced in North America's access brokerage data.

Defensive Recommendations:

• Treat agricultural technology platforms (precision ag, cold-chain IoT) with the same security rigor as manufacturing OT
• Monitor initial access broker markets specifically for agriculture and food-sector listings
• Build incident response plans accounting for food-supply-chain continuity, not just data confidentiality

Geopolitical and Ideological Dimensions: Hacktivism Across the Hemisphere

SOLDADOS DIGITALES – UNIÓN AMERICANA: A Hemispheric Hacktivist Collective

Unlike most hacktivist channels tracked in this report, SOLDADOS DIGITALES – UNIÓN AMERICANA operates across both North and South America, making it one of the few genuinely hemispheric threat actors identified in H1 2026 — a significant finding given how regionally siloed most hacktivist activity tends to be.

Combined Hacktivism Metrics (North + South America):

• ~140 confirmed data leak and dump posts across both sub-regions
• At least 932 unique domains impacted (360 in North America, 572 in South America)
• Primary targets: Government & LEA, Technology, BFSI, Telecommunication, Education

Notable Collectives by Sub-Region:

• North America: SOLDADOS DIGITALES – UNIÓN AMERICANA, Anonymous #FreeTurtleIsland, KERALA HACKERS, LYSTIC TEAM #ID
• South America: SOLDADOS DIGITALES – UNIÓN AMERICANA, Anonymous Colombia (#OpColombia) Y.A.N, BLAZER TEAM ATTACK

The Convergence Problem: As with hacktivist activity documented elsewhere in CRIL's global dataset, several Americas-based channels marketed as ideological collectives function as hybrid operations — logging DDoS attacks and defacement claims alongside stolen-data brokerage and DDoS-for-hire services. Security teams should treat these channels as credible threat intelligence sources rather than dismissing their claims as purely political theater.

Regional Threat Actor Summary: Who Targets Your Americas Organization

If You're in Professional Services:

• Primary Threat: INC Ransom, Qilin
• Secondary Threat: AiLock, The Gentlemen
• Vulnerability: Client data exfiltration, regulatory breach-notification pressure
• Defensive Focus: DLP, client data segregation, ransomware-specific cyber insurance, cyber threat intelligence

If You're in Manufacturing or Construction:

• Primary Threat: Qilin, Akira, Dragonforce
• Secondary Threat: The Gentlemen, Play
• Vulnerability: OT/IT convergence, cross-border supply-chain exposure, contractual downtime penalties
• Defensive Focus: OT segmentation, immutable backups, cross-border third-party risk management

If You're in Healthcare:

• Primary Threat: The Gentlemen (South America), Qilin (North America)
• Secondary Threat: LockBit
• Vulnerability: Patient-safety leverage, legacy medical device integration
• Defensive Focus: Clinical system isolation, redundant critical systems, patient-notification-ready incident response

If You're in BFSI:

• Primary Threat: Data exfiltration actors, mobile banking malware (Brazil)
• Secondary Threat: Qilin, The Gentlemen
• Vulnerability: Financial data value, mobile malware convergence, regulatory exposure
• Defensive Focus: DLP with aggressive egress controls, mobile threat monitoring, data encryption

If You're in Agriculture & Livestock:

• Primary Threat: Initial access brokers
• Secondary Threat: Downstream ransomware operators exploiting sold access
• Vulnerability: Precision agriculture and cold-chain IoT exposure
• Defensive Focus: OT-equivalent segmentation for agricultural technology, access-broker monitoring

If You're in Government & Law Enforcement (South America specifically):

• Primary Threat: RALord/Nova, CoinbaseCartel, hacktivist-branded channels
• Secondary Threat: LockBit, The Gentlemen
• Vulnerability: Public-sector data value, hybrid ideological/financial targeting
• Defensive Focus: Treat hacktivist claims as credible intelligence, harden citizen-data repositories

Strategic Defense Recommendations for Americas Organizations

Based on CRIL's H1 2026 regional data, Americas security leaders should prioritize defensive investment in the following sequence.

Phase 1: Critical Infrastructure Protection (30 days)

• Inventory Network Appliances: Document every internet-facing firewall, VPN, and security gateway
• Patch Critical CVEs: Prioritize Ivanti, Fortinet, Cisco, SolarWinds, and Palo Alto Networks appliances — the vendors repeatedly appearing in both the CISA KEV catalog and active exploitation campaigns
• Harden Remote Access: Enforce phishing-resistant MFA on all administrative and remote access paths
• Deploy Behavioral Monitoring: Watch for anomalous activity on network appliances specifically

Phase 2: Data Protection (60 days)

• Data Inventory: Catalog sensitive holdings — client data, financial records, patient records, intellectual property
• DLP Implementation: Deploy data loss prevention with aggressive egress monitoring
• Encryption Standards: Enforce encryption in transit and at rest across all sensitive data stores
• Access Auditing: Maintain comprehensive logs for every access event touching sensitive data

Phase 3: Operational Resilience (90 days)

• Immutable Backups: Establish offline, immutable backup infrastructure isolated from production networks
• Sector-Specific Incident Response: Build playbooks addressing construction project continuity, manufacturing downtime, and healthcare patient-safety scenarios specifically
• Cross-Border Continuity Planning: For organizations with US-Canada-Mexico or US-Brazil operations, extend continuity plans across all connected facilities
• Recovery Testing: Conduct quarterly backup restoration drills to verify actual recovery capability

Phase 4: Threat Hunting and Detection (Ongoing)

• Named-Actor Threat Intelligence: Subscribe to intelligence feeds tracking Qilin, Akira, INC Ransom, Dragonforce, and The Gentlemen specifically
• Access-Broker Monitoring: Track listings for organizational exposure
• Supply-Chain Monitoring: Continuously assess vendor and subsidiary security posture across borders
• Mobile Malware Awareness (Brazil-specific): Financial institutions should monitor for TCLBANKER- and BTMOB RAT-style Android banking trojan activity targeting customers

Conclusion: The Americas Ransomware Reality

The Americas is not just the largest ransomware theatre in the world by volume — it is two distinct threat environments operating under a single regional label. North America hosts a saturated, competitive RaaS marketplace where no single group dominates outright. South America is consolidating around a smaller set of operators, led decisively by The Gentlemen.

Key Takeaways:

1. The Americas carries the global center of gravity: 2,188 of the world's 3,836 documented ransomware attacks (57%) struck North or South America in H1 2026.
2. Five groups anchor the threat: Qilin (410), Akira (268), INC Ransom (171), Dragonforce (153), and The Gentlemen (146) collectively account for over half of all Americas ransomware activity — but their dominance splits sharply by sub-region.
3. North America and South America require different playbooks: North America's threat model demands broad coverage against a long tail of competing operators; South America's demands deep, specific defense against The Gentlemen, Qilin, and LockBit.
4. The United States remains the world's single largest target: 1,721 attacks — nearly 45% of global ransomware volume — makes the US the default target for virtually every major RaaS operator active today.
5. Brazil's threat is compounding, not singular: ransomware, mass data breach, and mobile banking malware are converging in the same underground economy targeting the same financial institutions.
6. Sector risk follows economic logic, not chance: Professional Services, Manufacturing, Construction, Healthcare, and — distinctively for the Americas — Agriculture & Livestock face targeting because threat actors have identified specific, exploitable economic pressure points in each.
7. Access brokers are a leading indicator: a small number of sellers control the region's initial access market and routinely precede ransomware deployment by weeks.

For security leaders across North and South America, the strategic imperative is the same even where the tactical details diverge: know which named actors are active in your specific country and sector, prioritize risk-based patching over blanket cycles, treat data exfiltration as inevitable rather than optional, and build recovery infrastructure that assumes an attack will happen — not one that hopes it won't. The data confirms the Americas will remain the world's most heavily targeted ransomware region through the remainder of 2026. The only open question is how prepared each organization chooses to be.

Frequently Asked Questions (FAQs)

How many ransomware attacks hit the Americas in H1 2026?

2,188 documented ransomware attacks were observed across North and South America in H1 2026, according to Cyble Research and Intelligence Labs (CRIL) findings.

Which ransomware group is most active in the Americas in H1 2026?

Qilin is the most active group across the combined Americas, with 410 documented attacks (370 in North America, 40 in South America). Within South America specifically, however, The Gentlemen — not Qilin — is the dominant actor.

How many ransomware attacks hit North America in H1 2026?

CRIL recorded 1,981 ransomware attacks in North America during H1 2026, representing roughly 52% of all ransomware activity tracked worldwide.

How many ransomware attacks targeted the US in H1 2026? Is it the highest?

Yes. CRIL observed 1,721 ransomware attacks targeted at the US — which is 78.7% of the American continent (North and South, both), and nearly 45% of every ransomware attack recorded worldwide.

Which sector was the most targeted in South America?

IT & ITES remained the most targeted sector in South America for H1 2026.

Ransomware actors targeted which country the most in South America?

Brazil. With 71 attacks, it was the prime target of ransomware actors in H1 2026.

Is Brazil a significant ransomware target?

Yes. Brazil recorded 71 ransomware attacks — the highest total in South America — and additionally faced an alleged 250 million record breach at credit bureau Serasa, an access sale allegedly targeting the Central Bank of Brazil, and new Android banking trojan families (TCLBANKER, BTMOB RAT) targeting financial and cryptocurrency users.

What is the most targeted industry in the Americas?

Construction tops North America's target list, while IT & ITES, Healthcare, and Professional Services top South America's. Across the whole Americas, Construction and Manufacturing remain consistently high-risk due to their low tolerance for operational downtime.

The post Ransomware Threats in the Americas H1 2026: Dissecting the Regional Attack Patterns and Dominant Actors appeared first on Cyble.

• Cisco Talos recently identified an undocumented phishing framework, internally branded "JWR" by its developer, built to convincingly impersonate checkout and login pages across major payment and shopping platforms. 
• The client engine of the JWR phishing framework is a real-time, operator-driven system that, rather than merely logging form submissions like a static credential-stealing page, keeps an AES-CTR encrypted WebSocket open to the threat actor so they can steer each victim's session live. 
• The victim data targeted by the actor using JWR extends well beyond payment data, encompassing identity documents, Social Security numbers, passport and driver's license images, website and PayPal credentials, 2FA codes, and full device fingerprints, all committed to the actor's server once a session ends.  
• Talos assesses with medium confidence that the JWR phishing framework is a variant of "The Outsider," a phishing-as-a-service (PhaaS) platform, based on several similarities in the client engine scripts and functionalities of the two PhaaS platforms. 
• Talos observed a real-world campaign delivering the JWR client via SMS lures impersonating toll authorities, and postal and courier services of several countries in Southeast Asia and the Middle East.

JWR phishing framework, a likely variant of the Outsider 

JWR is a phishing framework capable of harvesting complete payment card data, login credentials, and personally identifiable information (PII) documents and images in real time. The client-side engine of the framework impersonates login, and checkout flows of several payment gateways, including Shopify, PayPal, Apple, Klarna, and banks, while allowing the operator to stealthily control the victim session through an AES-CTR encrypted WebSocket channel. The client engine architecture is divided into a Host Bridge module that relays commands into a phishing inline frame (iframe) and a Vue.js victim application that renders across 44 phishing pages, streams the victim's keystrokes to the actor as they are typed, and carries out more than 40 distinct instructions issued from the command-and-control (C2) console. The data exfiltration schema is a cvvform object that includes fields such as credit card number, CVV, PIN, expiry date, Social Security Number (SSN), passport or ID images, two-factor authentication (2FA) codes, website logins, PayPal credentials, and device fingerprint.  

Talos discovered that the JWR client engine shares significant code and functional similarities with the client of The Outsider PhaaS platform operated by the Chinese-speaking actor “Outsider Enterprise,” which was reported by external researchers. 

JWR client architecture and workflow

The execution starts when the parent phishing webpage loads and executes the client's engine. It checks a single global flag, window.__HOST_MODE, which is set by the parent phishing page, and selects one of two execution modes. If the flag is set, the script enters Host Mode, and control passes to the Host Bridge module, an immediately invoked function expression (IIFE) that operates within the parent page, typically a replica of a legitimate checkout or account login page, relaying received details into a child iframe that contains the actual phishing form. It establishes a persistent WebSocket connection to the actor’s C2 server. 

If the flag is not set, the page enters Content Mode, and control passes to the Vue.js Application, an interactive front end that renders the phishing pages, collects victim input, manages the flow across 44 HTML files, and handles the actor’s instructions from the C2 server, ultimately redirecting to a custom error page after sending the data to the C2. The Content Mode of execution has three communication modes: standalone, pluginIframe, and hostIframe. 

• In standalone mode, the application fully owns its WebSocket connection. 
• In pluginIframe mode, it has no direct link to the network at all and instead sends everything upward to an embedding plugin frame. 
• In hostIframe mode, it defers entirely to a parent page already running as the relay bridge. 

Regardless of which of these three modes or through the Host Bridge is used, the data is either sent to C2 as plain text in JSON format with the DEV_MODE flag set, or it is passed to the JwrCrypto module, which encrypts it with a newly generated key before sending it to the C2 server.  

The script engine includes a background worker module that maintains the connection with C2, keeping it alive independently of page navigation for the remainder of the session. In a live session activity, the script continuously streams the victim’s keystrokes to the actor's C2 server as captured data, while that the actor continuously sends the next instruction to be executed from the C2 server. Each incoming instruction is checked by the client engine against a brief history to ensure that nothing already executed runs twice, then routed by the Instruction Handling module to one of two outcomes including, redirecting the victim to a different phishing page or updating the current page's state and displayed status, awaiting the actor’s next instruction. This execution loop repeats until the actor decides to keep the session alive, and when the actor chooses to close the session, the accumulated data is transmitted to the C2 one last time, and the victim is redirected. 

JWR Client’s host bridge mode  

In host bridge mode, the IIFE establishes a persistent WebSocket connection to the actor's server, manages the victim's session identity, excludes repeating incoming instructions, and proxies all communication between the server and the phishing child iframe. 

Every victim is assigned a unique session token the moment the bridge initializes. It first checks persistent storage for an existing JWRCID value if the victim has visited the page before, and if true, the same token is reused, allowing the actor to correlate multiple visits from the same device. If none exists, a new token is generated in the format JWRCVV-{Date.now()}-{random1}-{random2}, with both random segments being 13-character base-36 strings, and this token becomes the victim's permanent identifier for the entire C2 communication. 

The module then spawns a Web Worker from a separate script located at static/js/ws-worker.js, which isolates the WebSocket from the main JavaScript context, allowing the connection to persist during navigation within the phishing flow. The WebSocket connection path is constructed as webSocket/QT/{sessionId}/khkjsahfjkwhakjlsdwdddddd88, where the alphanumeric suffix is likely a server-side authentication token that ensures the connection originates from a deployed kit instance. 

The host bridge incorporates an anti-analysis check, which serves as a one-time execution guard that performs a self-referential .toString().search() call against a backtracking regex. This check detects whether a debugger has attached the function to modify its apparent source. Additionally, a decoy variable is scattered throughout the code to mislead static-analysis tools. 

Moreover, it maintains a JSON array named JwrExecutedInstructions in sessionStorage to prevent the same operator instruction from executing more than once. Before relaying any instruction into the phishing iframe, it verifies the instruction ID against a list. If a match is found, it discards the repeating instructions. If it is a new instruction, it sends an acknowledgment back to the C2 server in the format {type:"instructionAck", instruction_id:, cvv_id:}. The list is limited to 50 entries and is trimmed to retain the most recent 30. 

Content Mode operation (Vue.js application), the real-time capture 

The Vue.js victim application developed by the JWR developer is a single Vue 2.X instance, window.vm = new Vue ({el: ‘#app’, ...}), mounted on a Document Object Model (DOM) element with the id “#app”. This application serves as the phishing page that the victim sees and interacts with. It is responsible for rendering the checkout forms, collecting and streaming input to the C2, executing the actor’s instructions, and performing the exfiltration function. 

When the Vue instance is constructed, the created function is executed, processing the data passed from the fake webpage the victim visited, but without attaching the page. It generates the session ID and clears any sensitive fields leftover from a prior page visit if the victim had previously accessed the same fake page. It also restores any previously saved session state from “sessionStorage” if it exists. Then, it redirects the victim from any page other than index/login/home that lacks a session ID to a_index.html, ensuring the victim enters the phishing flow. Finally, the Vue takes the rendered output and attaches it to the #app element in the page's DOM, making the interface visible and interactive to the victim. 

Once the DOM is ready, Vue executes the mounted function asynchronously, at which point the victim becomes visible to the actor. It determines the engine’s execution mode and then executes two functions: getIPInfo() to geolocate the victim’s IP address and getSyncSettings() to pull the actor’s configuration from the C2 server. Next, it initializes the communication channel, captures the victim's action, and creates a CVV form with the victim's device fingerprint data. This includes the victim's current form of state, such as device type, browser, language, time zone, and geolocation, which are encrypted and sent to the actor's C2 server. 

One of the key features of the JWR kit is its near-real-time input streaming. Each input element in the phishing form is transmitted to the actor’s console, allowing the actor to view partial card numbers, partial passwords, and partial verification codes as the victim types, without needing to wait for the victim to click any submit button. This mechanism enables the actor to see the victim's data and determine which instruction to send to the client's engine from the C2 before the victim even submits the form. 

Before the Vue instance is created, the client engine establishes an instruction mapping table that correlates over 40 actor command names with specific HTML page filenames, thereby granting the actor remote control over the victim browser session. 

The JWR client script includes a C2 command dispatcher. When the actor sends an instruction, the client receives, decrypts, and forwards it to the dispatcher function, which routes it to the appropriate handler based on the instruction type. The table below displays the actors' instructions from C2, facilitated by the JWR client kit. 

The JWR client engine has a data exfiltration schema. Its scope extends well beyond payment data, and includes full identity information (name, gender, date of birth, Social Security Number, passport, driver's license, medical record number), address, email and email password, up to three sets of website credentials, PayPal login, complete card data (PAN, expiry, CVV, PIN, brand, issuer, issuing country), front and back card images, photos of identity documents, and an automatically captured browser fingerprint, including IP, device, language, time zone, user agent, cookies, and geolocation. 

Upon submission, the client normalizes the submission types, triggering a full-screen non-interactive overlay over the page. For credit card submissions, a Lottie animation is displayed that corresponds to the card brand detected from the first two BIN digits. After exfiltration, when the actor closes the WebSocket, terminate the worker and POST the entire cvvformobject to the C2 endpoint at api/open/the_final_interface. Once the actor confirms, the victim is redirected to the actual site. 

Talos discovered that the primary mode of C2 communication for the JWR kit is via a binary WebSocket connection. The WebSocket path follows the format shown below, where the JWRCID and JWRCVV segments encode the victim’s unique session token, and the trailing alphanumeric suffix is likely a server-side authentication token. 

Alongside the WebSocket, the JWR client registers five Representational State Transfer (REST) endpoints which are used as an alternate communication method, between the C2 and the victim browser. In this case, a session opens with api/open/addClick, executed once from within the mounted function after the phishing page becomes visible to the victim. It reports the victim's IP address, country, the specific phishing page they landed on, the referring or storefront URL, and a bundle of device and operating system (OS) metadata to the actor's console with a live "new visitor" entry before a single instruction has even been sent by the actor from the C2 server. Running alongside it is api/open/getSyncSettings, which pulls inbound configuration from the actor's server rather than exfiltrating anything, letting the actor change error messages, default contact placeholders, currency display, and other behavior on the fly without redeploying the client engine. For the victim’s environments where a persistent WebSocket connection is unavailable or blocked, api/open/pollInstruction provides an HTTP long poll fallback that delivers the same operator instruction objects the socket would otherwise push, keeping the actor's remote control functional even under restrictive network conditions. The session closes with api/open/the_final_interface, the client engine terminal exfiltration call. Once the actor issues a release instruction, the WebSocket connection and background worker are closed, and the entire accumulated cvvform object, every field collected across the full victim session — card data, identity documents, credentials, and fingerprint alike — is sent via HTTP POST to the C2 endpoint. 

The below table represents the endpoints and the purpose.  

The JWR client has purpose-built integrations for two major e-commerce platforms Shopify and WooCommerce. For Shopify deployments, the client reads the cart_data URL parameter which is a signed JSON blob that Shopify passes between checkout steps and extracts the checkout domain to use as the WebSocket base URL. This makes the WebSocket connection seem to originate from a legitimate Shopify domain. The initShopifyProductInfo() and initWordPressProductInfo() functions reconstruct the victim's shopping cart from the Shopify cart data, populating the phishing page with accurate product names, quantities, unit prices, and order totals making the fake checkout indistinguishable from the real one. 

The operator facing status messages of the JWR framework are entirely written in Simplified Chinese and read as a professional admin dashboard notification feed phrases like "正在填写PayPal登录账号" (filling in PayPal login account), "进入2FA验证页, 请发送验证, 等待用户提交" (entering 2FA verification page, please send verification, waiting for user submission), and "均失败" (all failed), indicating that a Chinese-speaking actor is operating this scam campaign. 

JWR phishing framework’s card stealing scenario 

When the victim lands on the fake page, their browser sends an arrival beacon, indicating to the actor that a new visitor is present. From there, the actor takes over, sending a to_info instruction that directs the victim to a personal details page. While the victim types, the actor sends no further instructions but monitors the data stream live. Once the actor has assessed the victim's personal information, they issue a to_card instruction, moving the victim to the card entry page, where the same stealth live streaming occurs as the card number is typed in digit by digit. 

If the actor isn't keen on the typed card details, tip_fail or tip_change_card instructions are sent, which deliver a fake "your card was declined" message to the victim and returns them to the card page to try a different one. This loop can repeat as many times as the actor wants, each attempt aimed at harvesting another card from the same victim. If the card is accepted instead, the operator sends one of the instructions: to_sms, to_2fa, to_pin, or to_app, directing the victim to a verification page to confirm their identity with a one-time code. For the rejected code, the actor sends the tip_fail instruction, which prompts the victim to re-enter it, while an accepted one leads to the final instruction, to_success, which redirects the victim to the real website, concluding the session with the actor now having the victim’s data that was typed.  

The ongoing scam campaign  

Cisco Talos observed an attacker utilizing an SMS phishing technique, sending SMS related to toll or road-pricing fees, postal or courier fees lures that contain a malicious URL targeting potential victims. When victims click on the URL, it opens a fake webpage that executes embedded JavaScript, which then renders and loads the client-side JavaScript engine of the JWR phishing framework. 

The victimology of this scam campaign illustrates a broad, multi-country SMS phishing (smishing) operation rather than a single targeted campaign. Most of the malicious URLs impersonate a national land transport authority and its vehicle services or road toll payment portal, consistent with an "unpaid toll or road pricing fine" lure in Singapore. A second set of malicious URLs impersonates a national postal service, aligned with a "parcel held pending a customs or delivery fee" lure, alongside a smaller cluster mimicking an electronic toll collection system in the UAE. The third set of URLs impersonates a regional courier brand utilized across several Southeast Asian countries, again centered around the undelivered parcel or cash on delivery fee theme. 

Talos discovery of the similarities in the client engine script of the JWR framework used in the current campaign with that of the Outsider PhaaS platform and additionally, we observed that in June 2026, the FBI had announced the technical takedown operation against Outsider platform (PhaaS) that has been in operation since 2023, through a joint operation “Ghost Hook.” However, the Outsider PhaaS was sold as a self-servicing product in the actor’s Telegram channels, according to the external researcher report, indicating the likely existence of variants of the Outsider PhaaS kit employed and operated by other Chinese-speaking threat actors.  

Comparing JWR with other Chinese PhaaS platforms 

Following the discovery of several similarities in the client-side scripts of the JWR and The Outsider kit, Talos conducted a comparative assessment of the JWR client script against other phishing kits operating within the Chinese-speaking criminal ecosystem. 

Talos found that JWR shares no code-level implementation with Lucid, Darcula, or Lighthouse. Its C2 communication protocol, encryption module, and message envelope are all independently engineered. At the behavioral level, JWR aligns closely with those kits. All four share the operational signature that defines this PhaaS lineage including live operator puppeteering, card capture paired with OTP/2FA interception, and multi-brand templating at scale. Several additional characteristics place JWR within the same family, highlighting a tradecraft consistency across the developers of the phishing kits embedded in the Chinese-speaking criminal ecosystem. 

Coverage 

The following ClamAV signature detects and blocks this threat:  

• Js.Phishing.JwrFramework-10060456-0 

The following Snort2 and Snort3 (SIDs) rules detect and block this threat: 

• 66924
• 66925
• 66926
• 66927
• 66928  

IOCs  

The IOCs for this threat are also available at our GitHub repository here. 

Identity-based attacks drive 90% of incidents. Learn how modern attackers exploit identities and what SOC leaders can do to respond.

The post Inside the Modern SOC: The Identity Front Door appeared first on Unit 42.

Europe faced a ransomware onslaught in the first half of 2026 that sets a troubling precedent for the remainder of the year. According to Cyble Research and Intelligence Labs (CRIL), the region experienced 866 documented ransomware attacks, 51 confirmed data breach incidents, and 7 initial access sales between January and June 2026. These figures represent not just a volume problem, but a fundamental shift in how threat actors are organizing, targeting, and monetizing their operations within European territory.

What distinguishes the ransomware threats in Europe from other global regions is the concentration of power among a small number of highly sophisticated threat actors. While the threat ecosystem encompasses dozens of groups, five dominant ransomware operators account for approximately 55% of all documented activity. This concentration creates predictability—European security leaders can now identify, profile, and build specific defensive strategies against known adversaries.

The Five Dominant Ransomware Groups Targeting Europe

1. Qilin: The Biggest Ransomware Threat in Europe

Attack Volume: 158 documented incidents (18.2% of regional total)

Qilin stands as the dominant ransomware threat actor targeting Europe, commanding operational superiority through sophisticated affiliate management, rapid exploit weaponization, and industry-specific targeting intelligence.

Geographic Concentration:

• Germany: 32 attacks (highest single-country targeting)
• France: 28 attacks
• United Kingdom: 26 attacks
• Spain: 20 attacks
• Italy: 19 attacks

Worldwide Sectoral Targeting: Qilin demonstrates deliberate sectoral selection rather than opportunistic targeting:

• Construction: 103 incidents (primary focus)
• Professional Services: 90 incidents (legal, accounting, consulting firms)
• Manufacturing: 67 incidents (industrial operations)
• Government & Law Enforcement: 19 incidents
• Technology: 22 incidents

Operational Characteristics:

Qilin's dominance stems from understanding European organizational economics. Construction projects operate under time-sensitive contracts with contractually-defined penalties for delay. A single day of downtime on a €50 million construction project can trigger cascading costs exceeding €100,000. This economic reality translates directly into ransom payment likelihood, making Qilin's targeting strategy rational and highly effective.

The group maintains an extensive affiliate network capable of concurrent operations across multiple European nations. Evidence suggests Qilin has compartmentalized its operations: initial access brokers handle reconnaissance and network compromise, mid-tier operators manage lateral movement and privilege escalation, and final-stage operators execute encryption and exfiltration. This division of labor enables rapid scaling and reduces attribution risk.

Why Qilin Dominates:

• Industry Expertise: Deep understanding of construction project timelines and financial exposure
• Affiliate Loyalty: Competitive payout structures (estimated 70-80% to affiliates) ensure consistent operator recruitment
• Exploit Library: Rapid weaponization of both known and zero-day vulnerabilities
• Data Monetization: Established data brokerage partnerships ensure exfiltrated data reaches buyers

European Security Implications: Organizations in construction, professional services, and manufacturing should treat Qilin as their primary threat actor concern. Defensive strategies must prioritize data exfiltration prevention, network segmentation, and immutable backup infrastructure.

2. The Gentlemen: The Rising European Threat

Attack Volume: 144 documented incidents (16.6% of regional total)

The Gentlemen represent an emerging threat actor that has achieved remarkable scale in a relatively short operational window. Unlike established groups that evolved from other cybercriminal operations, The Gentlemen appear purpose-built for ransomware-as-a-service operations.

Geographic Concentration:

• Europe: 144 attacks (primary focus)
• United States: 100 attacks (secondary focus)
• Thailand: 35 attacks (supply-chain targeting)
• South Asia: 40 attacks

Worldwide Sectoral Targeting:

• Construction: 45 incidents
• Manufacturing: 56 incidents
• Healthcare: 37 incidents
• IT & ITES: 36 incidents
• Professional Services: 29 incidents

Operational Characteristics:

The Gentlemen's rapid emergence and sustained growth suggest significant operational funding and technical sophistication. The group's geographic diversification—maintaining European dominance while aggressively expanding into Asia-Pacific—indicates either organizational scale or partnerships with regional threat actors.

Notably, The Gentlemen's Thailand targeting (35 incidents) suggests supply-chain attack sophistication. By compromising manufacturing and logistics operations in Thailand, the group can leverage these beachheads for downstream attacks against Western European organizations. This cross-continental supply-chain targeting represents a significant evolution in ransomware operational sophistication.

Key Distinction: While Qilin focuses on maximizing ransom payments from individual targets, The Gentlemen appear to prioritize operational scale and geographic expansion. This suggests the group may be building toward either:

1. A mega-RaaS platform rivaling LockBit's historical dominance
2. Preparation for potential acquisition or partnership with state-sponsored actors
3. Geographic arbitrage—leveraging lower prosecution risk in developing nations while maintaining European operations

European Security Implications: The Gentlemen's emergence signals market competition is intensifying. Organizations should monitor this group's operational evolution closely, as aggressive growth often precedes operational mistakes that create defensive opportunities.

3. LockBit: The Persistent Legacy Threat

Attack Volume: 61 documented incidents (7.0% of regional total)

LockBit's presence in European targeting represents a significant finding given sustained law enforcement pressure and multiple platform disruption attempts. Despite being targeted by coordinated international takedown operations, LockBit maintained operational capability throughout H1 2026.

Geographic Concentration:

• Europe: 61 attacks (Primary operations)
• North America: 47 attacks (Secondary operations)
• Distributed: Global presence indicating resilient infrastructure

Worldwide Sectoral Targeting:

• Construction: 22 incidents
• Manufacturing: 22 incidents
• Government & LEA: 12 incidents
• Healthcare: 19 incidents
• Professional Services: 13 incidents

Operational Resilience:

LockBit's continued operations despite international enforcement actions demonstrate several critical lessons:

1. Affiliate Compartmentalization: By maintaining separate operational cells, LockBit can continue operations even when core infrastructure is disrupted
2. Rapid Rebranding: The group has adopted multiple identities and platform variants, complicating attribution
3. Infrastructure Redundancy: Multiple command-and-control server locations across jurisdictions with varying law enforcement cooperation levels
4. Operator Recruitment: Continuous recruitment of new affiliates from emerging cybercriminal talent pools

The group's continued viability suggests that law enforcement actions, while disruptive, are insufficient to eliminate established RaaS operations. Organizations cannot rely on law enforcement intervention as a defensive strategy; they must assume LockBit and similar groups will remain operational threats indefinitely.

European Security Implications: LockBit should remain on European security teams' active threat monitoring lists. The group maintains technical sophistication, access to critical zero-day exploits, and demonstrated willingness to target European critical infrastructure.

4. Akira: The Opportunistic European Operator

Attack Volume: 59 documented incidents (6.8% of regional total)

Akira represents a secondary-tier ransomware group with focused European operations. The group demonstrates strong preference for Manufacturing and Construction sectors, suggesting industry-specific expertise or targeted affiliate recruitment.

Geographic Concentration:

• Europe & UK: 59 attacks (Secondary focus)
• North America: 268 attacks (Primary focus)
• Secondary: Limited operations in other regions

Worldwide Sectoral Targeting:

• Manufacturing: 54 incidents
• Construction: 57 incidents
• Professional Services: 47 incidents
• Consumer Goods: 34 incidents
• Healthcare: 13 incidents

Operational Profile:

Akira's disproportionate North American presence (268 attacks) with lower European activity (59 attacks) suggests the group may have established affiliate networks in North America with secondary capacity for European operations. The strong manufacturing and construction focus mirrors Qilin's strategy, indicating these sectors offer superior ransom payment likelihood across multiple geographic markets.

European Security Implications: While not as immediately threatening as Qilin or The Gentlemen, Akira's persistent operations warrant inclusion in threat modeling exercises. European manufacturing and construction organizations should monitor Akira's affiliate recruitment channels and tactical innovations.

5. Dragonforce: The Supply-Chain Specialist

Attack Volume: 54 documented incidents (6.2% of regional total)

Dragonforce rounds out the top-five European threat actors with apparent specialization in Manufacturing and Technology sectors, suggesting possible supply-chain attack capabilities.

Geographic Concentration:

• North America: 135 attacks (Primary focus)
• Europe & UK: 54 attacks (Secondary focus)
• Secondary: Limited global operations

Worldwide Sectoral Targeting:

• Manufacturing: 31 incidents
• Construction: 48 incidents
• Professional Services: 28 incidents
• Food & Beverages: 9 incidents
• Healthcare: 9 incidents

Operational Pattern:

Dragonforce's heavy US focus with secondary European operations suggests the group may be leveraging North American-based supply chains to gain access to European targets. Manufacturing supply chains are deeply interconnected across transatlantic partners; compromising US manufacturers could provide lateral access into European operations.

European Security Implications: European manufacturing organizations should implement aggressive third-party risk management programs, particularly for US-based suppliers. Dragonforce's supply-chain sophistication suggests the group may bypass direct targeting in favor of compromising upstream vendors.

Also read: The Most Active Threat Actors of H1 2026

The Five Most Targeted European Nations

Germany: The Manufacturing Battleground

Attack Volume: 155 ransomware attacks (17.9% of regional total)

Germany's position as Europe's manufacturing powerhouse places it at the center of ransomware targeting campaigns. The nation's industrial sector—encompassing automotive, machinery, chemicals, and precision manufacturing—represents the most valuable ransomware target set in Europe.

Threat Actor Concentration:

• Qilin: 32 attacks (20.6% of German total)
• The Gentlemen: 32 attacks
• LockBit: 18 attacks
• Akira: 32 attacks
• Dragonforce: 9 attacks

Sectoral Breakdown:

• Manufacturing: 67 incidents (significant concentration)
• Construction: 38 incidents
• Professional Services: 28 incidents
• Technology: 15 incidents
• Healthcare: 12 incidents

Why Germany Faces Maximum Pressure

German organizations represent an optimal target combination: high asset value, supply-chain criticality, strong operational technology integration, and proven willingness to pay ransoms to maintain production schedules. Additionally, Germany's federal structure creates jurisdictional complexity that may slow law enforcement response.

The nation's Mittelstand (mid-market manufacturing firms) are particularly vulnerable—large enough to justify ransom payments, but sometimes lacking enterprise-grade security infrastructure.

Defensive Priority: German manufacturing organizations should assume Qilin, The Gentlemen, Akira, and Dragonforce all maintain active operations targeting their sector. Network segmentation between IT and operational technology (OT) environments should be elevated to critical priority.

United Kingdom: The Financial Services Crosshairs

Attack Volume: 138 ransomware attacks (15.9% of regional total)

The UK faces a different threat profile than Germany, driven primarily by London's position as a global financial services hub. While manufacturing is targeted, Banking, Financial Services, and Insurance (BFSI) organizations command disproportionate attention.

Threat Actor Concentration:

• Qilin: 26 attacks
• The Gentlemen: 26 attacks
• LockBit: 18 attacks
• Akira: 13 attacks
• Dragonforce: 11 attacks

Sectoral Breakdown:

• BFSI: 38 incidents (concentrated targeting)
• Technology: 32 incidents
• Retail: 26 incidents
• Professional Services: 24 incidents
• Government & LEA: 16 incidents

Why the UK Is Targeted

London's financial services ecosystem manages trillions in assets, making it extraordinarily valuable to data-exfiltrating threat actors. BFSI organizations hold customer financial data, internal financial records, and strategic information that commands premium prices on dark web marketplaces.

Additionally, regulatory requirements (FCA, PRA, etc.) create pressure for rapid ransom payment to avoid breach notification delays that could trigger regulatory sanctions.

Data Exfiltration Risk: The UK's status as a financial services hub makes it particularly vulnerable to data-centric attack strategies. Organizations should assume that successful breach attempts will include aggressive data exfiltration alongside encryption deployment.

Defensive Priority: UK BFSI organizations must implement robust data loss prevention (DLP), encryption for data in transit and at rest, and aggressive monitoring for unauthorized data access or exfiltration attempts.

France: The Balanced Threat

Attack Volume: 119 ransomware attacks (13.7% of regional total)

France experiences balanced threat distribution across multiple sectors, reflecting both its manufacturing capacity and significant professional services sector.

Threat Actor Concentration:

• Qilin: 28 attacks
• The Gentlemen: 28 attacks
• LockBit: 15 attacks
• Akira: 14 attacks
• Dragonforce: 8 attacks

Sectoral Breakdown:

• Professional Services: 26 incidents
• Manufacturing: 24 incidents
• Construction: 19 incidents
• Technology: 14 incidents
• Healthcare: 10 incidents

Why France Faces Distributed Threat

As Europe's second-largest economy, France is attractive to ransomware operators across multiple sectors. The nation's professional services sector (legal, accounting, consulting) is particularly valuable for data exfiltration, while manufacturing remains a consistent target.

Defensive Priority: French organizations should implement sector-specific defensive strategies: professional services firms should prioritize client data protection and DLP, while manufacturing organizations should focus on OT segmentation and operational resilience.

Italy: The Construction and Manufacturing Hub

Attack Volume: 115 ransomware attacks (13.3% of regional total)

Italy faces concentrated targeting in construction and manufacturing sectors, with particular pressure on small-to-medium enterprises in industrial regions.

Threat Actor Concentration:

• Qilin: 19 attacks
• The Gentlemen: 18 attacks
• LockBit: 12 attacks
• Akira: 16 attacks
• Dragonforce: 8 attacks

Sectoral Breakdown:

• Construction: 48 incidents (concentrated)
• Manufacturing: 38 incidents
• Professional Services: 18 incidents
• Retail: 14 incidents

Why Italy Faces Sector-Specific Pressure

Italy's construction industry is particularly vulnerable to ransom attacks due to tight project timelines and significant financial exposure. The nation's manufacturing sector, while sophisticated, sometimes operates with legacy infrastructure that creates exploitation opportunities.

Defensive Priority: Italian construction and manufacturing organizations should prioritize incident response readiness, backup infrastructure resilience, and supply-chain risk management.

Spain: The Emerging Risk

Attack Volume: 87 ransomware attacks (10.0% of regional total)

Spain experiences lower absolute attack volume than Germany, UK, France, or Italy, but faces concentrated pressure in manufacturing and professional services sectors.

Threat Actor Concentration:

• Qilin: 20 attacks
• The Gentlemen: 18 attacks
• LockBit: 8 attacks
• Akira: 12 attacks
• Dragonforce: 7 attacks

Sectoral Breakdown:

• Manufacturing: 28 incidents
• Professional Services: 19 incidents
• Construction: 16 incidents
• Technology: 10 incidents

Regional Observation: Spain's lower attack volume may reflect either lower overall ransomware targeting or more effective defensive implementations. Spanish security teams should not interpret lower numbers as reduced threat but rather as a baseline for future comparison.

Where European Organizations Face Maximum Risk: A Sectoral Analysis

Construction: The Ransomware Goldmine

Attack Volume: 107 documented incidents (58% of all sector targeting across regions – not just in Europe – analyzed)

Construction organizations face disproportionate ransomware targeting across the entire European region. This concentration reflects understood economic vulnerabilities that threat actors exploit with precision.

Why Construction Is Targeted

1. Time-Sensitive Financial Exposure: Construction projects operate under contractually-defined timelines. Each day of delay triggers cascading costs, financial penalties, and potential contract termination. Organizations facing potential loss of €50-100 million contracts will prioritize rapid recovery over law enforcement involvement.
2. Operational Technology Integration: Modern construction increasingly relies on Building Information Modeling (BIM), cloud-based project management, and real-time equipment tracking. This IT/OT convergence creates exploitation pathways unavailable in purely IT-based industries.
3. Supply-Chain Complexity: Construction projects depend on dozens of subcontractors and suppliers. Compromising a single upstream supplier can provide lateral access into prime contractors.
4. Financial Pressure: Construction firms often operate with tight cash flow, making ransom negotiation essential to preserve solvency.
5. Accessibility: Many construction firms, particularly smaller regional players, operate with basic security infrastructure, creating easy exploitation opportunities.

European Construction Risk Mapping:

• Germany (14 attacks): Heavy machinery and precision manufacturing integration
• Switzerland (10 attacks): Legacy infrastructure vulnerabilities
• Spain (13 attacks): Emerging targeting activity
• France (10 attacks): Balanced threat across major metropolitan areas
• UK (21 attacks): Infrastructure project concentration (rail, utilities, etc.)

Defensive Recommendations for Construction:

• Network Segmentation: Isolate operational technology (project equipment, heavy machinery) from corporate IT networks
• Access Control: Implement strict authentication for remote project management tools (Autodesk Forge, Procore, etc.)
• Immutable Backups: Maintain offline, immutable backups of critical BIM files and project documentation
• Incident Response Readiness: Develop construction-specific response playbooks addressing project continuity
• Supply-Chain Due Diligence: Implement security requirements for subcontractors and equipment suppliers

Professional Services: The Data Exfiltration Target

Attack Volume: 86 documented incidents

Professional services firms (law, accounting, consulting) face sophisticated targeting driven by data exfiltration opportunities rather than operational disruption pressure.

Why Professional Services Are Targeted

1. Client Confidentiality Risk: Legal privilege and client confidentiality create existential regulatory and reputational exposure. Threat actors leverage this to demand premium ransoms.
2. Sensitive Data Concentration: Professional services firms accumulate client financial records, litigation strategies, tax information, and corporate secrets—all commanding premium dark web prices.
3. Regulatory Exposure: GDPR breach notification requirements create pressure for rapid response and ransom payment to avoid regulatory sanctions.
4. Supply-Chain Position: Professional services firms advise major corporations; compromising advisors provides indirect access to clients.
5. Trust-Based Business Model: Client relationships depend on confidentiality. A single breach can destroy long-term client relationships and firm reputation.

European Professional Services Risk:

• France (16 attacks): Concentrated targeting of Paris-based firms
• Germany (16 attacks): Heavy focus on Frankfurt financial advisory firms
• UK (17 attacks): London-based legal and accounting partnerships
• Italy (6 attacks): Milan and Rome-based advisory firms
• Spain (7 attacks): Barcelona and Madrid professional services sector

Key Finding: Professional services firms experience disproportionate data breach incidents (exfiltration with confirmed leak activity) compared to other sectors. Of the 51 total data breach incidents across Europe and UK, professional services represents a concentrated target.

Defensive Recommendations:

• Client Data Segregation: Isolate client data on separate network segments with distinct access controls
• Data Loss Prevention (DLP): Deploy DLP solutions with aggressive egress controls monitoring client data exfiltration
• Encryption Standards: Implement client-facing encryption for all sensitive communications
• Access Auditing: Maintain comprehensive logs of all access to sensitive client data
• Ransomware-Specific Insurance: Consider cyber insurance with specific ransomware coverage addressing confidentiality exposure

Manufacturing: The Supply-Chain Critical Target

Attack Volume: 123 documented incidents

European manufacturing organizations face sophisticated, supply-chain-aware threat actors who understand production dependencies and downtime economics.

Why Manufacturing Is Targeted

1. Operational Technology Integration: Modern factories integrate IT and OT systems. Ransomware deployment can halt production lines, creating catastrophic financial exposure.
2. Supply-Chain Criticality: Manufacturing downtime cascades through dependent enterprises. A single organization's compromise can impact dozens of downstream customers.
3. Export Dependency: European manufacturers serve global markets. Production delays translate directly into lost revenue and market share.
4. Legacy Infrastructure: Many manufacturing facilities operate aging, unpatched systems integrated with newer IT infrastructure, creating exploitation bridges.
5. Financial Pressure: Manufacturing organizations face razor-thin margins; production downtime can drive solvency crises.

Geographic Manufacturing Risk Concentration:

• Germany (27 attacks): Automotive, machinery, precision manufacturing
• Italy (21 attacks): Fashion, machinery, chemical manufacturing
• France (15 attacks): Automotive, aerospace, industrial manufacturing
• Spain (10 attacks): Automotive, machinery, manufacturing
• UK (14attacks): Aerospace, automotive, precision manufacturing

Critical Vulnerability Pattern: Manufacturing organizations are disproportionately targeting known, exploitable vulnerabilities in critical infrastructure appliances (network appliances, security tools, identity systems). Rather than deploying zero-days, threat actors exploit patched vulnerabilities that organizations have not implemented.

Defensive Recommendations:

• OT/IT Segmentation: Implement airgapped network separation between operational technology and corporate IT
• Vulnerability Management Prioritization: Focus patching efforts on network appliances, security tools, and identity systems
• Industrial Control System (ICS) Monitoring: Deploy behavioral monitoring for unusual activity on manufacturing control systems
• Immutable Backup Strategy: Maintain completely offline backups of critical manufacturing configurations
• Supply-Chain Security Program: Implement tier-1 and tier-2 supplier security assessments and vulnerability scanning
• Incident Response Scenario Planning: Develop detailed playbooks for production-line ransomware scenarios

Healthcare: The Critical Infrastructure Threat

Attack Volume: 35 documented incidents

Healthcare organizations face a unique threat dynamic where ransomware directly endangers patient safety, creating existential operational pressure distinct from financial threats.

Why Healthcare Is Targeted

1. Patient Safety Risk: Ransomware disables critical medical systems (diagnostic equipment, pharmaceutical dispensing, patient records). Unlike other industries, downtime directly threatens life.
2. Regulatory Pressure: GDPR, HIPAA-equivalent regulations, and national privacy laws create breach notification requirements that incentivize ransom payment.
3. Data Value: Patient medical records, pharmaceutical research data, and clinical trial information command premium dark web prices.
4. Continuous Operation Requirement: Unlike manufacturing or services, healthcare cannot delay critical procedures. The operational pressure to pay ransoms is existential.
5. System Complexity: Healthcare IT environments integrate numerous legacy systems (PACS, EHR, medical devices) with varying security architectures.

European Healthcare Risk Distribution:

• Germany (14 attacks): Concentrated in Berlin, Munich, and Frankfurt urban medical centers
• Austria (2 attacks): private healthcare sector
• France (5 attacks): Concentrated in Paris and Lyon region hospitals
• Switzerland (3 attacks): medical centers
• Spain (3 attacks): Barcelona and Madrid hospital networks

Critical Finding: Healthcare organizations experience disproportionately high data breach incident rates, suggesting organized threat actors specifically target health information exfiltration.

Defensive Recommendations:

• Clinical System Isolation: Implement complete network separation between clinical systems and corporate IT
• Redundant Critical Systems: Deploy redundant diagnostic and pharmaceutical systems capable of manual operation
• Patient Data Encryption: Implement end-to-end encryption for all patient medical records
• Breach Response Planning: Develop healthcare-specific incident response plans addressing patient notification and continuity of care
• Medical Device Security: Implement inventory and monitoring for all connected medical devices
• Supply-Chain Assessment: Assess security of medical device manufacturers and pharmaceutical distributors

The Data Exfiltration Reality: Beyond Encryption

Confirmed Data Breaches: 51 Incidents Across Europe and UK

While ransomware attacks total 866, only 51 incidents resulted in confirmed data breaches and leaks (5.9% confirmation rate). This apparent low percentage masks a critical operational truth: organizations cannot distinguish between encryption-only attacks and data exfiltration scenarios until exfiltration attempts or threats emerge.

Data Breach Distribution by Sector:

Critical Observation: BFSI and Telecom sectors experience disproportionate data breach incidents, suggesting these industries are specifically targeted for data exfiltration rather than operational disruption. The strategic implication is clear: threat actors targeting financial and telecommunications organizations prioritize data monetization over ransom payment.

Most Active Threat Actors in Data Exfiltration: The Leak Economy

Primary Exfiltration Actors:

Strategic Finding: While Qilin, The Gentlemen, and LockBit dominate ransomware attack volume, data exfiltration is fragmented across numerous smaller actors, including tanaka (6 posts), kazutlg (4 posts), and dozens of single-incident operators. This suggests a mature data brokerage ecosystem where extracted data is resold to specialized exfiltration actors.

Dark Web Data Marketplace Activity:

• 916 unique domains impacted by data leaks
• Approximately 86 distinct leak posts across dark web channels
• Data types: Financial records, customer PII, medical records, intellectual property, trade secrets

Implication: Organizations can no longer assume encrypted data is "lost forever" if backups are restored. Exfiltrated data will be monetized regardless of whether organizations pay ransoms. Data loss prevention becomes as critical as ransomware detection.

Geopolitical and Ideological Dimensions: The Activism-Cybercrime Convergence

Pro-Russian Hacktivism: Blurred Lines Between Ideology and Profit

H1 2026 witnessed increasing overlap between geopolitically motivated hacktivism and financially motivated cybercrime, particularly among pro-Russian collectives targeting NATO-aligned European nations.

Key Threat Actors to Monitor

NoName057(16) - The Pro-Russian DDoS Coalition

• Primary Activity: Large-scale DDoS attacks against NATO-aligned governments and Ukrainian supporters
• Secondary Activity: Data exfiltration for monetization
• Geographic Targets: Estonia, UK, Ukraine, Italy, Spain, France, Poland, Norway, Denmark, Lithuania, Latvia, Czech Republic, Germany, Moldova
• Operational Pattern: Coordinated DDoS campaigns often accompanied by data theft and subsequent leak activity

Operational Evolution: NoName057(16) began as a purely activist collective claiming ideological motivation (anti-NATO, pro-Russia). By H1 2026, the group had evolved to include data exfiltration and monetization—suggesting either organizational evolution or infiltration by financially motivated threat actors.

Strategic Implication: European organizations cannot compartmentalize threat modeling. A geopolitically motivated attack that begins as a DDoS campaign can transition into ransomware deployment when exfiltration opportunities present themselves.

Strategic Defense Recommendations for European Organizations

Prioritized Defensive Roadmap

Based on CRIL's H1 2026 regional data, European security leaders should prioritize defensive investments in the following sequence:

Phase 1: Critical Infrastructure Protection (30 days)

1. Inventory Network Appliances: Document all network appliances (firewalls, SD-WAN platforms, security gateways, VPNs)
2. Patch Critical CVEs: Prioritize patches for Cisco, Ivanti, Palo Alto, Fortinet, and Microsoft appliances
3. Access Control Hardening: Implement MFA for all remote administrative access to network infrastructure
4. Monitoring Deployment: Deploy behavioral monitoring on network appliances for anomalous activity

Phase 2: Data Protection (60 days)

1. Data Inventory: Identify and catalog sensitive data holdings (customer data, financial records, intellectual property)
2. DLP Implementation: Deploy data loss prevention solutions with egress monitoring
3. Encryption Standards: Implement encryption for data in transit (TLS 1.3+) and at rest (AES-256)
4. Access Logging: Enable comprehensive audit logging for all sensitive data access

Phase 3: Operational Resilience (90 days)

1. Immutable Backups: Establish offline, immutable backup infrastructure isolated from network access
2. Incident Response Planning: Develop organization-specific incident response playbooks addressing ransomware scenarios
3. Business Continuity: Identify critical business functions and develop continuity strategies
4. Disaster Recovery Testing: Conduct quarterly backup restoration testing to verify recovery capabilities

Phase 4: Threat Hunting and Detection (Ongoing)

1. Threat Intelligence Integration: Subscribe to European threat intelligence feeds focusing on Qilin, The Gentlemen, LockBit, Akira, and Dragonforce
2. Behavioral Detection: Deploy endpoint detection and response (EDR) solutions with behavioral analytics
3. Supply-Chain Monitoring: Implement continuous monitoring of vendor and supplier security posture
4. Insider Threat Program: Develop insider threat detection capabilities focusing on data exfiltration attempts

Regional Threat Actor Summary: Who Targets Your European Organization

Sector-Specific Threat Actor Mapping

If You're in Construction:

• Primary Threat: Qilin, The Gentlemen
• Secondary Threat: Akira, Dragonforce
• Vulnerability: Network segmentation gaps, supply-chain vulnerabilities, legacy OT systems
• Defensive Focus: OT/IT segmentation, immutable backups, supplier security assessment

If You're in Professional Services:

• Primary Threat: Qilin, The Gentlemen
• Secondary Threat: LockBit, Akira
• Vulnerability: Client data exfiltration, regulatory exposure, ransomware payment pressure
• Defensive Focus: DLP, client data encryption, ransomware-specific insurance

If You're in Manufacturing:

• Primary Threat: Qilin, The Gentlemen
• Secondary Threat: Akira, Dragonforce
• Vulnerability: OT/IT integration, supply-chain exploitation, operational downtime pressure
• Defensive Focus: OT segmentation, vulnerability prioritization, continuity planning

If You're in BFSI:

• Primary Threat: Qilin, The Gentlemen, LockBit
• Secondary Threat: Data exfiltration actors (tanaka, kazutlg)
• Vulnerability: Financial data value, regulatory breach notification pressure, customer trust exposure
• Defensive Focus: Data encryption, DLP with aggressive egress controls, cyber insurance

If You're in Healthcare:

• Primary Threat: Qilin, The Gentlemen, LockBit
• Secondary Threat: Data exfiltration operators
• Vulnerability: Patient safety risk, critical operational pressure, medical device security
• Defensive Focus: Clinical system isolation, redundant critical systems, incident response for operational continuity

Conclusion: The European Ransomware Reality

Europe and the UK face a mature, organized ransomware ecosystem dominated by five sophisticated threat actors who have developed deep understanding of regional economic vulnerabilities. The threat is not random or opportunistic—it is strategic, targeted, and evolved.

Key Takeaways:

1. Five groups dominate: Qilin (158 attacks), The Gentlemen (144), LockBit (61), Akira (59), and Dragonforce (54) collectively account for 476 of 866 documented attacks (55%). European security leaders can build specific defensive strategies against known adversaries.
2. Geography matters: Germany, UK, France, Italy, and Spain face distinct threat profiles. Security strategies must be regionally and sector-specific, not generic.
3. Sectors are targeted deliberately: Construction, Professional Services, and Manufacturing are not randomly selected—they face extraordinary pressure due to economic vulnerabilities that threat actors systematically exploit.
4. Data exfiltration is the primary leverage: Of 866 attacks, only 51 resulted in confirmed breaches—but this understates the risk. Organizations must assume all breaches involve data exfiltration and cannot rely on backup restoration alone.
5. Patch management is the primary defense: Nearly 90% of exploited vulnerabilities had patches available. Disciplined patch management, particularly for network appliances, would prevent the vast majority of successful attacks.
6. Known vulnerabilities are the current threat: Despite awareness of zero-day sophistication, threat actors continue exploiting known vulnerabilities because patches lag adoption. This creates a predictable exploitation window that defensive teams can close.

For European security leaders, the path forward is to understand your regional threat actors, prioritize critical infrastructure protection, implement robust data protection measures, and establish resilient backup and recovery infrastructure. The threat is severe, but it is also understood and defensible. The question is not whether European organizations will face ransomware attacks in the remainder of 2026 and beyond—the data confirms they will. The question is whether they will be prepared.

The post Ransomware Threats in Europe H1 2026: A Deep Dive into Regional Attack Patterns and Dominant Threat Actors appeared first on Cyble.

Threat actors are increasingly exploiting legitimate cloud services to evade detection and streamline the deployment of their scam infrastructure. Cloud hosting services and decentralized networks have become primary platforms for hosting phishing pages and sites. Throughout 2025 and 2026, we have observed phishing operators steadily migrate toward platforms like Cloudflare Workers, Vercel, Netlify, GitHub Pages, and IPFS. This post analyzes the mechanics of a real-life adversary-in-the-middle (AitM) attack in a cloud environment and presents detailed statistics on the platforms and domains phishers abuse most frequently.

The cloud as a safe haven for phishers

Threat actors select platform-as-a-service (PaaS) offerings and distributed cloud environments to host phishing sites for much the same reasons legitimate software developers do:

• Inherent trust and reputation. Phishing pages hosted on reputable platforms appear trustworthy, reducing suspicion among potential victims.
• Most platforms offer generous free-tier developer plans. The onboarding process takes minutes and rarely requires Know Your Customer (KYC) identity verification. This enables a single operator to create hundreds of malicious accounts.
• Evasion and anonymity. Attackers leverage native security features to obscure their true origin server IP address behind a CDN, which complicates detection for security vendors.

Additionally, these platforms allocate shared subdomains hosting millions of legitimate projects and websites. Security teams cannot simply block the parent domain or its subdomains without inflicting collateral damage on bona fide users – a limitation that malicious actors take advantage of. To counter this tactic, security vendors must advance content-based analysis methodologies.

Multi-stage AitM attack

Consider a modern AitM phishing campaign that leverages Cloudflare Workers, a widely adopted cloud platform. The attackers execute the operation through multiple HTML pages distributed across a compromised website and the cloud platform. Each page serves a specific function: harvesting target email addresses, initializing the reverse-proxy infrastructure, or spoofing the login form to capture multi-factor authentication (MFA) sessions.

Stage 1. Contact harvesting and network monitoring evasion

The attack typically begins with a phishing email that uses a plausible pretext – such as a request from a coworker to review documents – to entice the target into clicking a malicious link.

Upon clicking the link, the user is redirected to a fake CAPTCHA landing page hosted on a compromised legitimate website. This specific campaign used the https://t[REDACTED]e.com website, but any other variations are possible. In this scenario, the compromised page served as a disposable relay — vendor detection mechanisms typically block phishing links delivered directly via email much faster — to prevent the early discovery of the core phishing content hosted on Cloudflare.

If the user entered their email address and clicked Continue, the pseudo-CAPTCHA marked them as a human user and initiated a redirect. The primary objective of this stage is to harvest target email addresses, filter out bots, and route legitimate users to a subdomain of workers.dev. Such subdomains are generated automatically and free of charge by Cloudflare Workers. The victim’s email address was embedded in the URL hash (the part of the URL following the # character), allowing the page at [REDACTED].workers.dev to extract the email without issuing a request to the attacker’s server, thereby avoiding detection.

Stage 2. Initializing a transparent proxy

The user’s browser then loaded a [REDACTED].workers.dev page with #user@business.com at the end of the URL. At this point, the page presented the victim with a genuine CAPTCHA challenge. This step ensured that an actual user was interacting with the page rather than a security sandbox.

Another CAPTCHA, this time a legitimate one

Once the user successfully completed the challenge, a service worker was registered in their browser. This is a special JavaScript file capable of running in the background and intercepting all network requests generated by the current tab. As this type of script was designed as a core component of progressive web apps (PWAs) to optimize load times and support offline functionality, browsers treat service workers as standard site feature and execute them without prompting for user consent as long as the website uses an HTTPS connection.

The attackers leveraged the service worker to deploy Ultraviolet, a legitimate open-source web proxy library, to dynamically rewrite all links and forms on the page. This forced every outgoing request – including those for Microsoft login credentials – to route through the attackers’ server rather than directly to the legitimate services.

Immediately upon loading, the page extracted the victim’s email address from the URL hash and stored it in the browser’s sessionStorage property so it would not be overwritten when the CAPTCHA loaded. This step also allowed the script to pre-fill the username field in the form automatically. A pre-populated login field enhanced the page’s credibility and bolstered user trust. Once the CAPTCHA was passed, the malicious script constructed a redirect URL for the third stage, appending the email retrieved from sessionStorage back to the hash. By passing the email via the URL hash across three consecutive stages, the attackers successfully kept it hidden from network attack detection systems.

Registering a service worker to intercept traffic

Establishing a transparent proxy via an external library

Stage 3. Session hijacking and browser window spoofing

The final stage unfolded on a third page, combining adversary-in-the-middle (AitM) traffic interception with a browser-in-the-browser (BitB) UI spoofing technique. BitB attacks operate by rendering a block inside a legitimate webpage that visually mimics a native browser pop-up window.

In this case, the script hosted on the attacker’s page generated a pop-up visually identical to a native browser window, complete with window controls and a spoofed address bar showing a trusted Microsoft URL. Within this simulated window, an iframe loaded the authentic login interface, routed dynamically through the service worker reverse proxy created in Stage 2. When the victim entered their credentials and MFA code into the BitB window, the proxy script intercepted both the credentials and the session tokens. Combining BitB with AitM significantly increases the threat: BitB provides a convincing, trusted visual wrapper (displaying a legitimate URL and branding), while the hidden AitM proxy quietly handles traffic interception and session hijacking behind the scenes.

Upon successful login, the proxy instructs the interface to close the pop-up and redirect the victim to a generic system error page, such as SessionExpired. This minimizes suspicion: the victim assumes a technical glitch occurred and attempts to log in again, unaware that the attacker already has full access to the session.

Cloud platform phishing attack statistics

We analyzed phishing URLs hosted across popular cloud platforms – including Cloudflare, Netlify, and GitHub Pages – over a 12-month period spanning August 2025 to July 2026. The data below outlines trends in unique third-level domains exploited to deliver phishing content. In total, our security solutions blocked 224,984 unique third-level domains on cloud and decentralized services used in phishing attacks within that timeframe.

Number of unique third-level domains
(download)

Based on this telemetry, we compiled a list of the TOP 10 cloud domains most frequently abused in phishing campaigns over the specified period.

Number of phishing links

Unsurprisingly, Cloudflare and Vercel emerged as the undisputed leaders: both offer free tiers, automated SSL certificate issuance, and global CDNs. GitHub Pages ranked third. The widespread legitimate use of the github.io domain complicates bulk blocking efforts, as security teams risk limiting access to non-malicious projects.

Decentralized networks also warrant close attention – we posted on this subject in 2023. The ipfs.io and dweb.link domains function as IPFS gateways. The principal risk associated with these platforms is content persistence: even if a specific gateway gets blocked, the phishing page remains accessible via alternative nodes across the network.

The visual website builders Wix and Webflow also ranked among the TOP 10 (eighth and ninth, respectively). These platforms allow low-skilled individuals to build phishing pages rapidly without advanced coding expertise, which significantly lowers the barrier to entry for less capable malicious actors.

In total, we identified and neutralized over 390,000 phishing pages hosted across legitimate cloud platforms and decentralized networks (IPFS) over the past 12 months. This data confirms that threat actors actively exploit the implicit trust associated with legitimate PaaS providers (such as Cloudflare Workers, Vercel, Netlify, and GitHub Pages) and IPFS gateways. High domain reputation, generous free tiers, and built-in evasion capabilities enable phishers to deploy multi-stage AitM attacks designed to hijack MFA sessions.

Recommendations

Traditional security controls, such as relying on HTTPS lock icons or reputation-based domain denylists, are inadequate against these attacks. The cloud provider’s apex domain maintains a positive reputation score, while attackers generate malicious subdomains programmatically and at scale.

Effective defense against these threats calls for a layered security posture:

• Exercise caution with unexpected requests, even if they are served from reputable domains or secured with valid SSL/TLS certificates.
• Treat any CAPTCHA interface requiring personal data input as a possible scam. Legitimate CAPTCHA challenges rarely request personally identifiable information, such as email addresses.
• Inspect the URL in the address bar at the very top of the browser window. In BitB attacks, threat actors can render a fake browser pop-up displaying any target URL, even a legitimate one. However, the true address bar – located at the top of the main browser window alongside native navigation controls (Back, Forward, Refresh) – will continue to display the actual attacker-controlled domain.
• Avoid entering credentials in pop-ups you did not expect to see. If a login or MFA form appears without your explicit action, close the tab immediately. Navigate to the intended service manually by entering its address directly into the browser.
• Additional protection can be provided by Kaspersky Secure Mail Gateway for enterprise environments and Kaspersky Premium for personal correspondence. These robust email security solutions neutralize phishing links at the delivery stage before they reach the inbox.

Since early May 2026, Microsoft Threat Intelligence has observed Storm-2945, a sub-cluster of Midnight Blizzard, conducting widespread but targeted traffic manipulation attacks involving hospitality sector networks served by captive portals worldwide. Despite some tactic, technique, and procedure (TTP) similarities to the Forest Blizzard DNS hijacking operation that we publicly disclosed in April 2026, we attribute this campaign, which we call CaptiveCrunch, to Storm-2945. As reported by ReliaQuest on July 23, a portion of this activity leverages doppelganger domains mimicking Microsoft online services to conduct follow-on adversary-in-the-middle (AitM) phishing operations that abuse the device code authentication flow in Microsoft Entra ID. Microsoft Threat Intelligence has also identified active traffic manipulation attacks leading to the delivery of malware on impacted systems. Microsoft has observed Storm-2945 leveraging AI to support a significant portion of these operations.

Today, we are sharing our findings on these ongoing intrusions to raise awareness of this threat and enable customers to protect their devices, especially while traveling. We provide our assessment of Storm-2945’s relationship to Midnight Blizzard and analysis of the CaptiveCrunch campaign, detailing the malware and tradecraft used in these operations. We also provide mitigation, detection, and hunting guidance to help organizations identify and defend against Storm-2945 and related activity.

Microsoft Threat Intelligence would like to thank our partners at Anthropic and OpenAI for their collaboration and support during this investigation.

The CaptiveCrunch campaign

Since February 2026, Storm-2945 has conducted AI-augmented operations including targeted device code and OAuth code phishing campaigns leading to Entra device registration and subsequent data collection from Microsoft 365. Since early May 2026, Microsoft Threat Intelligence has observed Storm-2945 manipulating DNS and HTTP traffic from networks served by captive portals to redirect user traffic through actor-controlled infrastructure. Although our investigation into the initial compromise vector for the captive portal networks is ongoing, we have observed notable commonalities in the equipment and management systems used across multiple affected networks. These similarities suggest that the activity might not be limited to isolated compromises of individual venues and could reflect access to shared services within portions of the captive portal ecosystem.

As part of the CaptiveCrunch campaign, Storm-2945 has leveraged their AitM position to redirect users through actor-controlled phishing infrastructure and has also delivered malware purporting to be browser or operating system updates in response to automated connectivity checks issued by users’ browsers. Multiple variants have been delivered, including fully-featured Windows remote access trojans (RAT) in compiled Golang, with functionality to conduct system enumeration, collect files and keystrokes, steal credentials and session tokens, conduct audio and video surveillance, monitor for removable media, and provide the threat actor a remote shell on infected systems.  

The threat actor infrastructure leverages a variety of ClickFix techniques to elicit the user into downloading and executing the malware:

In addition to variants of malware targeting Windows systems, Microsoft Threat Intelligence is also aware of indications that the threat actor might be targeting Android devices with similar techniques as the ClickFix landings also include instructions for Android devices to download and install an APK file.

To date, Microsoft has identified widespread compromise of Wi-Fi networks at hospitality-related organizations and other networks serviced by captive portal equipment in several countries. ReliaQuest has identified this activity not only at hotels, but also conference centers and other shared venues, and assesses that the goal of this activity is to access the accounts of corporate travelers.

Storm-2945 and Midnight Blizzard

Microsoft Threat Intelligence assesses that Storm-2945 is an operational sub-cluster of Midnight Blizzard based on distinctive technical and operational overlaps. These include technical similarities to Storm-2372, a Midnight Blizzard initial access operations sub-cluster, also notable for their device code and OAuth code phishing operations tracked throughout 2025, Microsoft Graph-based email exfiltration, social engineering delivered via commercial messaging apps, and significant similarities in victimology.

Midnight Blizzard is a Russia-based threat actor attributed by the US and UK governments to the Foreign Intelligence Service of the Russian Federation, also known as the SVR. This threat actor is known to primarily target governments, diplomatic entities, non-governmental organizations (NGOs), and information technology (IT) service providers, primarily in the US and Europe. Midnight Blizzard is consistent and persistent in their operational targeting, and their objectives rarely change. Their focus is to collect intelligence through longstanding and dedicated espionage in support of Russian foreign policy interests.

Midnight Blizzard operations often involve compromise of valid accounts and, in some highly targeted cases, advanced techniques to compromise authentication mechanisms within an organization to expand access and evade detection. They utilize diverse initial access methods, and Midnight Blizzard is also adept at identifying and abusing OAuth applications to move laterally across cloud environments and for post-compromise activity, such as email collection.

CaptiveCrunch tradecraft and tooling

CornFlake: Remote access and infostealer implant

CornFlake is a full-featured Windows RAT written in Go that serves as Storm-2945’s primary persistent implant. Microsoft has observed the threat actor rapidly iterating on this malware layer, which features customizable capabilities from the social engineering user interface and data collection capabilities to anti-detection and evasion techniques.

On initial execution, CornFlake operates in dropper mode: it displays a convincing fake progress window designed to occupy the victim’s attention while the binary copies itself to %APPDATA%\svchost32\svchost32.exe and establishes persistence.

Fake window options configurable by the threat actor at build time:

• winupdate — A Windows Update screen displaying “Working on updates… Don’t turn off your computer”
• defender — A Windows Security virus scan
• directx — A DirectX End-User Runtime Web Installer
• vcredist — A Microsoft Visual C++ 2015-2022 Redistributable installer
• sysopt — A disk optimization utility
• netfix — A Windows Network Diagnostics tool
• browser — A browser update prompt
• pdfview — A document viewer installer

CornFlake registers as a Windows service named svchost32 with the display name “Cloud Sync Service” and description “Synchronizes files with the cloud storage provider”, deliberately mimicking the legitimate svchost.exe process. It establishes redundant persistence mechanisms: Windows service registrations, Registry Run keys, named scheduled tasks, and a persistence watchdog routine that runs continuously to restore any persistence mechanism that is removed by defenders or endpoint protection.

For command and control (C2), CornFlake performs an Elliptic Curve Diffie-Hellman (ECDH) P-256 ephemeral key exchange with the C2 server, derives a session key via SHA-256, and communicates over a custom JSON protocol framed within the encrypted channel. This provides an encrypted channel to the C2 server, with each C2 session using a unique ephemeral key, making decryption of captured traffic impossible without the session-specific private key. The runtime configuration file sync.dat supports hot reconfiguration of C2 servers, watched directories, file targeting patterns, and Transport Layer Security (TLS) settings without requiring redeployment.

Once established on a victim system, CornFlake provides the operator with a comprehensive collection toolkit, gated by configuration flags that allow selective activation post-deployment:

CornFlake also exposes a localhost HTTP API server (/upload, /reload, /status) that transforms the RAT into a modular platform: companion or next-stage payloads such as ChocoShell could task file exfiltration, trigger configuration hot reloads or check C2 connectivity using the pre-established secure C2 channel for communication.

ChocoShell: PowerShell infostealer

ChocoShell is the campaign’s Powershell-based infostealer, delivered and executed entirely in-memory. Its primary objective is the high-volume theft of browser session cookies, saved passwords, Microsoft 365 Single Sign-On (SSO) tokens, and Wi-Fi credentials from compromised systems. Where CornFlake provides the operator with a persistent, long-running foothold on the device, ChocoShell is designed to extract the most operationally valuable credentials, giving the operator access to victim cloud environments.

The ChocoShell script was authored with full developer comments that reveal the operator’s intent behind each code decision, including explicit references to Microsoft detection signatures and the reasoning behind specific evasion choices. The consistent coding standard and descriptive commentary suggest the author might have leveraged AI-assisted code generation.

Defense evasion. Upon execution, ChocoShell beacons to a hardcoded C2 server at 213.145.86[.]112 and implements several evasion techniques in sequence. It disables the Antimalware Scan Interface (AMSI) via .NET reflection to prevent ScriptBlock scanning and evades Microsoft behavioral detection that triggers on suspicious PowerShell web request cmdlets. A timing-based sandbox detection check is also employed as a virtual machine (VM) detection mechanism, silently exiting without performing any collection if detected.

C2 communication. ChocoShell communicates with its C2 server using HTTPS with URI paths designed to blend in with legitimate web traffic. Beacons use /t/pixel.gif?m=<status>, mimicking an image tracking pixel. Additional tooling is fetched from /cdn/chunks/polyfill-7e2b.min.js, disguised as a JavaScript polyfill file. This downloaded module is Base64-decoded and executed in memory via [ScriptBlock]::Create(), providing browser encryption key extraction capabilities, SYSTEM token impersonation, and Defender signature locking. Exfiltrated data is sent by POST to /t/event as GZip-compressed, Base64-wrapped JSON.

Privilege escalation. ChocoShell requires administrative privileges for its most impactful capabilities: SYSTEM token impersonation for Chrome ABE decryption, Volume Shadow Copy Service (VSS) shadow copy creation, Defender signature locking. It implements three silent UAC bypass techniques with ordered fallback:

1. SilentCleanup task hijack: Writes a malicious command to HKCU\Environment\windir, then triggers the built-in SilentCleanup scheduled task, which resolves %windir% from the user’s environment, executing the threat actor’s command at elevated privilege. The registry value is cleaned up after two seconds to avoid cloud detection.
2. wsreset.exe COM hijack: Creates a COM handler key in HKCU\Software\Classes and launches the auto-elevating Windows Store reset tool.
3. sdclt.exe folder hijack: Hijacks HKCU\Software\Classes\Folder\shell\open\command and launches the Windows Backup utility with the /KickOffElev flag.

If none of the silent bypasses succeed (for example, the user is not a local administrator), ChocoShell falls back to a visible UAC prompt via Start-Process -Verb RunAs. Notably, the script also contains a variant designed to execute within the WinGet Desired State Configuration (DSC) host process (ConfigurationRemotingServer), suggesting an attack vector through malicious WinGet DSC configuration used in Windows machine provisioning.

Credential and session theft. Once running with elevated permissions, ChocoShell locks Defender signature updates and systematically harvests data from multiple sources. For Chromium-based browsers (Chrome, Edge, Brave, Opera, Opera GX, Vivaldi), it extracts the master encryption key from the browser’s Local State file, handling both the modern ABE scheme (Chrome v127+) and the legacy data protection API (DPAPI)-only scheme. ABE decryption requires SYSTEM-level DPAPI access, which the malware obtains by impersonating a SYSTEM process token borrowed from winlogon.exe, wininit.exe, or services.exe. Locked browser SQLite databases are accessed through three strategies: shared file access, Volume Shadow Service snapshots, and direct copy as a fallback.

As a parallel collection path, ChocoShell launches Chrome, Edge, and Brave with the –remote-debugging-port flag and issues Network.getAllCookies through the Chrome DevTools Protocol (CDP). This completely bypasses ABE, enabling the browser to perform its own internal decryption and returns plaintext cookie values. To handle privilege issues (SYSTEM-launched browsers inherit the wrong token), the malware creates transient scheduled tasks with TASK_LOGON_INTERACTIVE_TOKEN to launch the browser under the signed-in user’s session. After extraction, the browser is stopped and relaunched with –restore-last-session to avoid alerting the user.

For Firefox family browsers (Firefox, Waterfox, LibreWolf, Floorp, Zen), the malware copies unencrypted cookies.sqlite databases from each profile. Additionally, ChocoShell collects Microsoft 365 and Azure Active Directory (AD) access tokens, refresh tokens, and Web Account Manager (WAM) tokens from .tbres files in the Token Broker cache. Collection of these tokens represents a significant threat to enterprise environments, as threat actors could replay SSO sessions without browser cookies. Additionally, Wi-Fi credentials are harvested via netsh wlan show profile with key=clear.

Exfiltration and cleanup. All collected data is aggregated into a JSON structure, GZip-compressed, Base64-encoded, and sent by POST to the C2’s /t/event endpoint. After exfiltration, all collected data variables are nulled, garbage collection is forced, VSS shadow copies are deleted via Windows Management Instrumentation (WMI), temporary elevation scripts are removed, and all UAC bypass registry keys (already cleaned during escalation) are verified removed.

FruitStone: Operator C2 panel

FruitStone is the web-based C2 panel that Storm-2945 operators use to manage the entire CaptiveCrunch campaign infrastructure. Implemented as a single-page application (HTML and JavaScript) serving as the front-end of the C2 server with all functionality exposed without authentication, FruitStone provides a centralized dashboard for managing compromised endpoints, building and deploying new campaign payloads, and reviewing all collected data (such as screenshots, keystrokes, browser credentials).

Operational cover. The panel is branded as “CloudSync Console” with a footer reading “Acuity Systems, Inc. — Cloud Infrastructure Portal v3.2.1,” designed to appear as legitimate enterprise cloud management software if the panel URL is discovered by defenders or hosting providers. This masquerading extends to the CornFlake agent’s service name (Cloud Sync Service) and description (“Synchronizes files with the cloud storage provider”), creating a consistent cover story across the toolchain.

Session management and multi-operator support. FruitStone uses JSON Web Token (JWT)-based authentication, session revocation, and rate limiting with IP blocking to prevent brute force attacks against the panel sign in. Multiple operators could be provisioned with individual accounts, and all active sessions are visible with IP address, user-agent, and creation time to enable operational security awareness across the operators.

Agent management. The panel displays all registered CornFlake agents in a dashboard with real-time status updates via Server-Sent Events (SSE). Each agent card shows comprehensive system information including hostname, username, OS version, CPU, RAM, disk usage, screen resolution, timezone, domain membership, and camera/microphone presence, all collected during the CornFlake posture sweep. Agents are grouped by country and subnet, with geographic distribution visualized on a map.

Operators could interact with individual agents through:

• Remote shell — Interactive cmd.exe or PowerShell command execution with command history
• File system browser — Live directory traversal and arbitrary file download from compromised hosts
• Collection tasking — On-demand screenshot, process list, keylog buffer flush, clipboard dump, security posture survey, ChromeKatz cookie/password extraction, camera capture, and audio recording
• Configuration push — Live runtime reconfiguration of C2 servers, watch paths, and C2 beacon timing
• Agent update — In-place implant update by pushing a new CornFlake build to a running agent
• Agent kill — Remote termination of the CornFlake implant

Campaign builder. A step-by-step wizard enables operators to configure and build new CornFlake payloads directly from the panel:

1. Identity — Campaign ID, C2 host and port, HTTP base URL, executable file name (svchost32.exe by default), and dropper type (C dropper at ~19 KB, Go stub at ~8 MB, or standalone self-installer)

2. Capabilities — Toggle individual collection modules: screenshots, process enumeration, keylogging, clipboard monitoring, posture survey, file exfiltration, and ChromeKatz browser credential theft

3. File Paths — Configure targeted directories and file extensions by category (documents, archives, images, code, data, emails, encryption keys)

4. Evasion — Enable garble symbol randomization (for GoLang payloads), XOR string encoding, GZip upload compression, and debug mode

Infrastructure management. FruitStone provides management interfaces for three layers of supporting infrastructure:

• Proxy relays — Multi-proxy C2 relay architecture with TLS certificate tracking (fingerprint, expiry), health checks, connection counts, bytes forwarded, and rotation capabilities that push updated server lists to all online agents
• Beacon profiles — Configurable timing profiles controlling agent sleep intervals, reconnection delays, TLS Server Name Indication (SNI) spoofing (like teams.microsoft.com), and DNS fallback domains
• Staging servers — External payload hosting infrastructure with push-to-deploy, file listing, and health monitoring

Device code abuse for cloud access

Since July 16, Microsoft has observed a portion of CaptiveCrunch landing pages redirecting users to device code authentication flow experiences. In these cases, users served these landings might be instructed to enter a device code into a legitimate Microsoft sign-in page, a technique commonly referred to as device code phishing.

Device code authentication is a legitimate OAuth workflow designed for devices that cannot support a traditional sign-in experience. However, threat actors could abuse this flow by initiating an authentication request on behalf of a user then convincing the user to enter an actor-controlled device code into a legitimate Microsoft authentication page. When successful, the victim authenticates the threat actor’s session rather than their own.

This activity is consistent with previously reported device code phishing operations conducted by Midnight Blizzard since August 2024. The observed technique does not appear fundamentally novel; however, integrating device code phishing into captive portal and traffic manipulation operations might increase the likelihood that users perceive the authentication request as legitimate. For additional details on Midnight Blizzard-related device code phishing techniques, see: Storm-2372 conducts device code phishing campaign. To understand other threat actors’ use of device code phishing and associated mitigations, see Inside an AI‑enabled device code phishing campaign.

How to protect against CaptiveCrunch activity

Minimize trust in hospitality and guest networks

When traveling, users should treat hotel, conference, airport, and other guest wireless networks as untrustworthy.

• Prefer private connectivity (including mobile hotspots, satellite, and eSIM-based cellular data connections) over public Wi‑Fi whenever practical.
  • For enterprise-managed devices, organizations can choose to prevent Wi-Fi connections to networks that have not been provisioned via Mobile Device Management (MDM). See: https://learn.microsoft.com/windows/client-management/mdm/policy-csp-wifi#allowmanualwificonfiguration
• Consider using enterprise-managed travel routers or hotspot devices that establish encrypted tunnels back to trusted corporate infrastructure before accessing sensitive resources.
• Avoid downloading software updates, certificates, browser updates, network troubleshooting tools, or security utilities presented through captive portals or other unexpected web prompts.
• Verify update requests through trusted operating system mechanisms rather than pop-up messages or website prompts.

Strengthen identity and access controls

Organizations should assume that public and hospitality network infrastructure might not be trustworthy and should adopt controls that limit exposure to traffic manipulation, credential theft, and device code phishing.

• Educate users to recognize ClickFix-style prompts, fake verification checks, and paste-and-run instructions as malicious, especially when they invoke command interpreters or script hosts such as cmd.exe, PowerShell, rundll32.exe, or mshta.exe.
• Use passwordless solutions like passkeys and implement multifactor authentication (MFA).
  • Use the Microsoft Authenticator app for passkeys and MFA, and complement MFA with conditional access policies, where sign-in requests are evaluated using additional identity-driven signals.
  • Conditional access policies can also be scoped to strengthen privileged accounts with phishing resistant MFA.
  • Your passkey and MFA policy can be further secured by only allowing MFA and passkey registrations from trusted locations and devices.
• Only allow device code flow where necessary. Microsoft recommends blocking device code flow wherever possible. Where necessary, configure Microsoft Entra ID’s device code flow in your Conditional Access policies.
• Implement a sign-in risk policy to automate response to risky sign-ins. A sign-in risk represents the probability that a given authentication request is not authorized by the identity owner. A sign-in risk-based policy can be implemented by adding a sign-in risk condition to Conditional Access policies that evaluates the risk level of a specific user or group. Based on the risk level (high/medium/low), a policy can be configured to block access or force MFA.
  • When a user is a high risk and Conditional access evaluation is enabled, the user’s access is revoked, and they are forced to re-authenticate.
  • For regular activity monitoring, use Risky sign-in reports, which surface attempted and successful user access activities where the legitimate owner might not have performed the sign-in. 
• Use a Security Service Edge (SSE) solution like Global Secure Access to secure access to any app or resource using network, identity, and endpoint access controls.

Reduce exposure during captive portal registration

Organizations should review what information employees provide to hospitality providers when connecting to guest networks.

• Do not reuse corporate credentials on hotel, conference, or guest-network registration pages.
• Where possible, organizations should evaluate whether venue-provided wireless is required for corporate events and conferences.
• Organizations should minimize unnecessary disclosure of employee identities, organizational affiliations, and travel details when booking accommodations or registering for guest network access, consistent with corporate policy and applicable local requirements.

Microsoft Defender detections and hunting guidance

Microsoft Defender customers can refer to the list of applicable detections below. Microsoft Defender coordinates detection, prevention, investigation, and response across endpoints, identities, email, apps to provide integrated protection against attacks like the threat discussed in this blog.

Microsoft Defender for Endpoint detects Storm-2945 activity under the detection Suspicious activity linked to a Russian state-sponsored threat actor has been detected. However, these alerts might be triggered by unrelated threat actor activity. The following chart lists Microsoft Defender detections specific to the TTPs utilized by Storm-2945 in this attack.

Microsoft Security Copilot

Microsoft Security Copilot is embedded in Microsoft Defender and provides security teams with AI-powered capabilities to summarize incidents, analyze files and scripts, summarize identities, use guided responses, and generate device summaries, hunting queries, and incident reports.

Customers can also deploy AI agents, including the following Microsoft Security Copilot agents, to perform security tasks efficiently:

• Threat Intelligence Briefing agent
• Phishing Triage agent
• Threat Hunting agent
• Dynamic Threat Detection agent

Security Copilot is also available as a standalone experience where customers can perform specific security-related tasks, such as incident investigation, user analysis, and vulnerability impact assessment. In addition, Security Copilot offers developer scenarios that allow customers to build, test, publish, and integrate AI agents and plugins to meet unique security needs.

Threat intelligence reports

Microsoft Defender XDR customers can use the following threat analytics reports in the Defender portal (requires license for at least one Defender XDR product) to get the most up-to-date information about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide the intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer environments.

• Actor profile: Midnight Blizzard

Microsoft Security Copilot customers can also use the Microsoft Security Copilot integration in Microsoft Defender Threat Intelligence, either in the Security Copilot standalone portal or in the embedded experience in the Microsoft Defender portal to get more information about this threat actor.

Hunting queries

Microsoft Defender XDR

Microsoft Defender XDR customers can run the following advanced hunting queries to find related activity in their networks:

Detect file creation after Wi-Fi connectivity test on devices

The following query checks for a file creation on a device within two minutes of the device performing built‑in Network Connectivity Status Indicator (NCSI) test, which occurs when network connectivity is established to a Wi-Fi network with a captive portal. This activity might indicate an attacker’s initial access file presence on a device.

Please note that not all files discovered through this query might be malicious or related to this threat activity.

let ncsi_endpoints = dynamic(["msftconnecttest.com","edge-http.microsoft.com","msftncsi.com","captive.apple.com","clients1.google.com",
    "clients3.google.com","clients4.google.com","clients6.google.com","connectivitycheck.gstatic.com","connectivitycheck.android.com",
    "android.clients.google.com","www.gstatic.com","detectportal.firefox.com","detectportal.brave-http-only.com","cloudflareportal.com",
    "cloudflarecp.com","cloudflareok.com","connectivity-check.warp-svc","connectivity.cloudflareclient.com","spectrum.s3.amazonaws.com",
    "nmcheck.gnome.org"]);
let NCSIEvents = DeviceNetworkEvents
    | where Timestamp > ago(7d)
    | where RemoteUrl has_any (ncsi_endpoints)
    | project NCSI_Timestamp = Timestamp, DeviceId, DeviceName, RemoteUrl, NCSI_ReportId = ReportId, NCSI_InitiatingProcessFileName = InitiatingProcessFileName, NCSI_InitiatingProcessCommandLine = InitiatingProcessCommandLine, NCSI_AccountName = InitiatingProcessAccountName;
let FileDownloadEvents = DeviceFileEvents
    | where Timestamp > ago(7d)
    | where ActionType == "FileCreated"
    | where FileName has_any (".exe",".msi",".zip",".rar",".7z")
    | project Download_Timestamp = Timestamp, DeviceId, FileName, FolderPath, Download_ReportId = ReportId, Download_InitiatingProcessFileName = InitiatingProcessFileName, Download_InitiatingProcessCommandLine = InitiatingProcessCommandLine, Download_AccountName = InitiatingProcessAccountName;
NCSIEvents
| join kind=inner (
    FileDownloadEvents
) on DeviceId
| where Download_Timestamp >= NCSI_Timestamp and Download_Timestamp <= NCSI_Timestamp + 2m
| project
    NCSI_Timestamp,
    Download_Timestamp,
    DeviceName,
    DeviceId,
    RemoteUrl,
    FileName,
    FolderPath,
    InitiatingProcessFileName = Download_InitiatingProcessFileName,
    InitiatingProcessCommandLine = Download_InitiatingProcessCommandLine,
    AccountName = Download_AccountName,
    NCSI_ReportId,
    Download_ReportId

Detect connectivity to Storm-2945 infrastructure

The following query checks for connectivity to Storm-2945 infrastructure observed in this attack activity.

let target_domains = dynamic(["ms365-device.com", "ms365-live.com", "m365-owa.com", "owa-ms365.com"]);
let target_ips = dynamic(["31.57.243.154", "38.146.28.75", "38.146.28.132", "104.194.159.150", "107.189.26.194", "213.145.86.112"]);
DeviceNetworkEvents
| where RemoteUrl has_any(target_domains) or RemoteIP in (target_ips)
| project
    Timestamp,
    DeviceName,
    DeviceId,
    RemoteUrl,
    RemoteIP,
    LocalIP,
    InitiatingProcessFileName,
    InitiatingProcessCommandLine,
    AccountName = InitiatingProcessAccountName,
    ReportId

Detect CornFlake RAT presence on affected systems

The following query checks for the presence of the CornFlake RAT binary.

DeviceProcessEvents
| where FolderPath == "%APPDATA%\\svchost32\\svchost32.exe"
   or FolderPath endswith @"\svchost32\svchost32.exe"
| project Timestamp, DeviceName, DeviceId, FileName, FolderPath, InitiatingProcessFileName, InitiatingProcessCommandLine, AccountName, ReportId

Detect CornFlake RAT Windows service registration

The following query checks for the CornFlake RAT Windows service registration.

DeviceRegistryEvents
| where RegistryKey has @"\SYSTEM\CurrentControlSet\Services\svchost32"
| where ActionType == "RegistryValueSet"
| where (RegistryValueName == "DisplayName" and RegistryValueData == "Cloud Sync Service")
    or (RegistryValueName == "Description" and RegistryValueData == "Synchronizes files with the cloud storage provider")
| project
    Timestamp,
    DeviceName,
    DeviceId,
    RegistryKey,
    RegistryValueName,
    RegistryValueData,
    ActionType,
    InitiatingProcessFileName,
    InitiatingProcessCommandLine,
    InitiatingProcessAccountName,
    ReportId

Microsoft Sentinel

Microsoft Sentinel customers can use the TI Mapping analytics (a series of analytics all prefixed with ‘TI map’) to automatically match the malicious domain indicators mentioned in this blog post with data in their workspace. If the TI Map analytics are not currently deployed, customers can install the Threat Intelligence solution from the Microsoft Sentinel Content Hub to have the analytics rule deployed in their Sentinel workspace.

Detect network IP and domain indicators of compromise using ASIM

The following query checks IP addresses and domain IOCs across data sources supported by ASIM network session parser:

//IP list and domain list- _Im_NetworkSession
let lookback = 30d;
let ioc_ip_addr = dynamic(["213.145.86.112"]);
let ioc_domains = dynamic(["213.145.86.112/t/pixel.gif", "213.145.86.112/cdn/chunks/polyfill-7e2b.min.js", "213.145.86.112/t/event"]);
_Im_NetworkSession(starttime=todatetime(ago(lookback)), endtime=now())
| where DstIpAddr in (ioc_ip_addr) or DstDomain has_any (ioc_domains)
| summarize imNWS_mintime=min(TimeGenerated), imNWS_maxtime=max(TimeGenerated),
  EventCount=count() by SrcIpAddr, DstIpAddr, DstDomain, Dvc, EventProduct, EventVendor

Detect web sessions IP and file hash indicators of compromise using ASIM

The following query checks IP addresses, domains, and file hash IOCs across data sources supported by ASIM web session parser:

//IP list - _Im_WebSession
let lookback = 30d;
let ioc_ip_addr = dynamic(["213.145.86.112"]);
let ioc_sha_hashes =dynamic([“918fa52ae45ed60ba7cc8bdc99c3cbe9ab92e0375ec31fc05d0d4513be11c593”, “be99857449d2856dd5a84e21c8a3d5e0e01456adb44062ddec5a6b4970d8d42c”]);
_Im_WebSession(starttime=todatetime(ago(lookback)), endtime=now())
| where DstIpAddr in (ioc_ip_addr) or FileSHA256 in (ioc_sha_hashes)
| summarize imWS_mintime=min(TimeGenerated), imWS_maxtime=max(TimeGenerated),
  EventCount=count() by SrcIpAddr, DstIpAddr, Url, Dvc, EventProduct, EventVendor

Detect domain and URL indicators of compromise using ASIM

The following query checks domain and URL IOCs across data sources supported by ASIM web session parser:

// file hash list - imFileEvent
// Domain list - _Im_WebSession
let ioc_domains = dynamic(["https://213.145.86.112/t/pixel.gif", "https://213.145.86.112/cdn/chunks/polyfill-7e2b.min.js", "https://213.145.86.112/t/event"]);
_Im_WebSession (url_has_any = ioc_domains)

ChocoShell C2 communications

The following query detects ChocoShell communications with its C2 server using HTTPS with URI paths designed to blend in with legitimate web traffic. Beacons use /t/pixel.gif?m=<status>, mimicking an image tracking pixel.

let lookback = 30d;
let ioc_url_artifacts = dynamic(["/t/pixel.gif?m="]);
_Im_WebSession(starttime=todatetime(ago(lookback)), endtime=now())
| where DstDomain  in (ioc_url_artifacts)
| summarize imWS_mintime=min(TimeGenerated), imWS_maxtime=max(TimeGenerated),
  EventCount=count() by SrcIpAddr, DstIpAddr, Url, Dvc, EventProduct, EventVendor

Indicators of compromise

References

• https://reliaquest.com/blog/threat-spotlight-dns-poisoning-tactics-expand-to-hospitality/
• https://www.volexity.com/blog/2025/02/13/multiple-russian-threat-actors-targeting-microsoft-device-code-authentication

Learn more

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

The post CaptiveCrunch: Midnight Blizzard targets travelers worldwide for malware delivery and credential theft appeared first on Microsoft Security Blog.

This week on the Lock and Code podcast…

Twenty years ago, a British mathematician named Clive Humby popularized a phrase that came to describe data’s relationship with the entire global economy: “Data is the new oil.”

Pithy as the phrase sounds, it is undeniably true.

Data steers decisions at businesses of every size. Data created entirely new industries built around its capture. And, for a select number of companies, data has produced billions—if not trillions—of dollars in value.

So how is it that, on the dark web, your stolen identity can be purchased for just 95 cents?

That’s what a Malwarebytes researcher found last month after spending 48 hours inside the dark web to investigate cybercrime. Across a variety of forums and directories, he found subscription plans for malware that steals information once implanted on a device. He found guides for deploying social engineering scams. He found people selling their services to build fake websites that trick people into handing over their usernames and passwords. And he found one of the dark web’s most traded commodities—personal data, packaged together about individual people, to help a cybercriminal commit identity fraud.

These packages are called “fullz.” For victims in the United States, a fullz contains a full name, Social Security Number, date of birth, address, and other personal details. That is enough, on its own, for a cybercriminal to potentially open a bogus line of credit, file a fake tax return, access financial accounts, or obtain medical services under someone else’s name.

As we wrote on Malwarebytes Labs:

“For less than the cost of a cup of coffee, a cybercriminal can buy enough information to devastate someone’s financial life.”

It’s the kind of risk that could scare anyone, especially considering the scale behind it. In just the first six months of 2026, Malwarebytes found more than 7,500 compromised data sets on the dark web containing more than 8.4 billion records.

And yet, even today, cybersecurity professionals still get asked why anyone should bother protecting their data.

The public, understandably, are exhausted. With data breaches happening every week—if not every day—cybersecurity can start to feel pointless. With young people unable to build financial security, they start believing that they have nothing worth stealing. And with Big Tech already collecting our every movement, behavior, click, and concern, people understandably feel powerless to fight any kind of data abuse, be it corporate or criminal.

So today’s episode approaches the question from a different direction. This isn’t about why you should protect yourself—plenty of company websites will tell you that, and most of them rely on fear. This is about why hackers want your data in the first place.

Today, on the Lock and Code podcast, host David Ruiz explains how cybercriminals turn a single repeated password into account takeover, how a screenshot of your house from Google Maps became a tool in extortion emails, and why the most benign information about you—an address, an age, one public photo—is often the most useful data a stranger can buy.

Tune in today to listen to the full episode.

Show notes and credits:

Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/
Outro Music: “Good God” by Wowa (unminus.com)

Listen up—Malwarebytes doesn’t just talk cybersecurity, we provide it.

Protect yourself from online attacks that threaten your identity, your files, your system, and your financial well-being with our exclusive offer for Malwarebytes Premium for Lock and Code listeners.

The second quarter of 2026 (April–June) was largely defined by the continuing downstream effects following Microsoft’s Digital Crimes Unit-led disruption efforts against the Tycoon2FA phishing-as-a-service (PhaaS) platform in March. Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs. Despite ongoing efforts to rebuild operations, Tycoon2FA did not recover its previous scale or influence during Q2, and no single service emerged to replace the platform at comparable scale.

These trends reflect both the measurable impact that disruption operations can have on phishing ecosystems and the adaptability of threat actors as they diversify delivery channels. At the same time, Microsoft Threat Intelligence observed continued growth in Teams-based social engineering, particularly voice phishing (vishing), with weekly malicious call attempts reaching nearly ten times the mid-2025 baseline by the end of the quarter. This activity illustrates how threat actors continue to expand beyond email into trusted workplace communication platforms where communications may appear more trustworthy to users.

Microsoft detected approximately 7.6 billion email-based phishing threats throughout the quarter, with monthly volumes declining modestly from 2.7 billion in April to 2.4 billion in June. Credential phishing remained the dominant objective behind malicious payloads, while business email compromise (BEC) activity largely returned to historical norms after a brief, anomalous surge in April. Notable campaigns observed during the quarter also demonstrated how threat actors combine automation, trusted services, and multi-stage delivery chains to scale operations. These campaigns ranged from an automated BEC campaign that reached more than 67,000 users across 42,000 organizations in under three hours, to a multi-stage phishing campaign that used nested EML files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

This blog provides a view of email threat activity across the second quarter of 2026, highlighting key trends in phishing techniques, payload delivery, and threat actor behavior observed by Microsoft Threat Intelligence. We examine shifts in QR code and CAPTCHA-gated phishing activity, malicious payload trends, BEC activity, the growth of Teams-based threats, and notable campaigns observed during the quarter. We also provide recommendations and Microsoft Defender detections to help organizations identify and mitigate evolving threats while prioritizing defensive measures.

Tycoon2FA Q2 disruption impact

The disruption operation that Microsoft’s Digital Crimes Unit launched against Tycoon2FA infrastructure in early March continued to produce measurable results throughout Q2 2026. After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year. For reference, the average monthly volume of phishing messages linked to Tycoon2FA during the second half of 2025 was 15.1 million. By the end of Q2, volumes were running at roughly 8% of that baseline, representing a 92% total decline since the disruption operation began.

Tycoon2FA’s influence across two primary phishing tactics, QR code lures and CAPTCHA-gated landing pages, also continued to decline throughout the quarter:

• CAPTCHA-gated phishing: Tycoon2FA’s share of CAPTCHA-gated phishing sites fell from 41% in March to 16% in April and 12% by June, down from a peak of 76% in December 2025.
• QR code phishing: The share of QR code campaigns redirecting to Tycoon2FA domains decreased from 20% in March to 17% in April and 14% by June, down from a peak of 33% in November 2025.

These declines indicate that the platform’s customer base has not migrated to replacement infrastructure at anything close to the scale they previously operated.

After being forced off Cloudflare, which had provided anti-analysis protection that made Tycoon2FA pages harder to scan and take down, the service continued to rely on infrastructure hosted on the .RU top-level domain (TLD), a shift that began in late March. More than 40% of newly observed Tycoon2FA domains used .RU registrations throughout Q2. While this reflects an ongoing effort to find replacement hosting, Tycoon2FA’s role in the phishing ecosystem has nonetheless been significantly diminished and the pace of recovery has been slow.

QR code phishing attacks

After peaking at 18.7 million attacks in March, the highest monthly volume in at least a year, QR code phishing declined for three consecutive months in Q2. Volume fell 7% in April to 17.4 million, then dropped more sharply in May (-38%) and June (-22%), closing the quarter at 8.3 million attacks. By June, QR code phishing had returned to levels last seen in mid-2025.

The delivery methods used in QR code attacks shifted notably during Q2. PDF attachments remained the dominant vehicle throughout, but their dominance weakened after April:

• PDF attachments peaked at 79% of QR code attacks in April before falling to 59% in May and 58% in June. By raw volume, malicious PDFs containing QR codes dropped more than 60% between April and June.
• DOC/DOCX attachments moved in the opposite direction, increasing 30% in May to account for 38% of QR code payloads, the highest share since December 2025. By June, DOC/DOCX payloads reached 40% of QR code attacks. This swap between PDF and DOC/DOCX dominance is a pattern that has recurred throughout the past year, as operators appear to rotate between delivery formats.
• Email-embedded QR codes, which had surged 336% in March and accounted for 5% of QR code attacks, effectively disappeared in Q2. This delivery method dropped to near-zero across all three months, leaving QR code phishing almost entirely an attachment-based tactic.

CAPTCHA-gated phishing tactics

After accumulating to nearly 12 million attacks in March, the highest monthly volume observed over the past year, CAPTCHA-gated phishing declined sharply throughout Q2. Volume fell 32% in April to 8.2 million, then dropped another 65% in May and 24% in June, closing the quarter at just 2.2 million attacks. Since the March peak, CAPTCHA-gated phishing has fallen more than 81%, reaching its lowest monthly volume in more than a year.

The rapid rotation of delivery methods that characterized Q1 continued into Q2, with no single payload type maintaining the top position for more than one or two months:

• PDF attachments surged to 63% of CAPTCHA-gated attacks in April, the highest single-payload share observed in the past year, after more than quadrupling in March. This dominance was short-lived, however. PDF volumes dropped 69% in May and another 70% in June, falling to just 22% of attacks by the end of the quarter.
• HTML attachments, which had been a major delivery vector through January (37% of attacks), declined sharply during Q2. After declining to 8% in April, HTML payloads fell to just 3% in May before recovering slightly to 5% in June, their lowest sustained share in at least a year.
• SVG files reached their lowest observed volume in April (5% of attacks) before rebounding to 12% in May and 26% in June. While still well below the levels seen when Tycoon2FA actively used SVG files, this gradual recovery bears monitoring.
• Email-embedded URLs reclaimed the top position in June for the first time since December 2025, accounting for 30% of CAPTCHA-gated attacks. This was more a function of every other delivery method declining in raw volume than a resurgence in URL-based delivery. The actual volume of URL-delivered CAPTCHA-gated phish in June was still far lower than most months over the past year.
• DOC/DOCX files declined from their March spike, falling steadily from 15% to 10% of attacks over the quarter.

Tycoon2FA’s continued decline was a significant factor in the overall volume reduction. The platform’s share of CAPTCHA-gated phishing fell from 41% in March to 16% in April, 18% in May, and 12% by June, down from a peak of 76% in December 2025. No single service has emerged to fill the gap at comparable scale, contributing to the sustained decline in CAPTCHA-gated phishing activity overall.

Malicious payloads

Credential phishing continued to dominate the malicious payload landscape throughout Q2, accounting for 94–96% of all payload-based attacks each month. These credential phishing payloads either linked users to phishing pages or locally loaded spoofed sign-in screens on a user’s device. Traditional malware delivery represented just 4–6% of payloads, consistent with its long-term decline.

HTML and PDF attachments remained the two most common malicious payload types across the quarter, together accounting for roughly 60–70% of all payload-based attacks each month:

• HTML attachments held the top position across all three months at 35–41% of attacks. After peaking in April, HTML payload volume declined 33% in May and another 17% in June.
• PDF attachments consistently ranked second at 24–31% of attacks. PDF volume was relatively stable in April before declining 41% in May and 4% in June.
• SVG files continued the decline that has tracked closely with Tycoon2FA’s diminishing activity. After peaking at 23% of malicious payloads in July 2025, SVG’s share fell to around 7% by Q2, consistent with SVG’s historical role as a preferred Tycoon2FA payload format.
• DOC/DOCX and ZIP/GZIP files oscillated without a clear directional trend. DOC/DOCX increased 26% in May before falling 17% in June, while ZIP/GZIP attachments declined 48% in April, rebounded 27% in May, then dropped 40% in June.
• ICS files (calendar invitations), while still a small share of overall payload volume (roughly 4%), nearly quadrupled in June (+277%). These attacks take advantage of the fact that calendar invitations are processed differently than standard email attachments and can inject malicious links into a user’s calendar without requiring an explicit open-and-click interaction.
• EXE files continued to decline, falling to their lowest monthly volume in June, reflecting the broader shift away from traditional malware delivery via email attachments.

Business email compromise

April 2026 produced the most anomalous BEC data point in more than a year: nearly 9 million attacks, a 121% increase from March and more than double any previous month. The spike was short-lived as volume fell 62% in May to 3.4 million and settled at 3.9 million in June, both figures consistent with the monthly baseline that had held throughout the prior year. The April surge appeared to be driven by a small number of high-volume campaigns rather than a fundamental escalation in BEC activity.

The composition of BEC attacks remained consistent throughout Q2. Generic outreach messages (like “Are you at your desk?”) accounted for 87–92% of initial contact emails each month, while explicit requests for specific financial transactions or documents represented just 3–8%. This pattern underscores that BEC operators overwhelmingly favor establishing conversational rapport with targets before making fraudulent requests, rather than leading with direct financial asks.

Within the smaller subset of explicit financial requests, the most notable trend was the near-disappearance of fake invoice payment requests:

• Invoice payment requests fell 67% in May and another 77% in June, reaching their lowest volume in more than a year. By June, invoice-themed BEC accounted for less than 0.4% of all attacks, down from around 3.6% in March.
• Payroll update requests declined moderately across the quarter, from roughly 4% of attacks in March to 2.3% by June.
• Gift card requests remained at roughly 1–4% of attacks, with no clear directional trend.

Microsoft Teams threats

While email remains the dominant initial access vector, threat actors increasingly abused Microsoft Teams during Q2 to deliver social engineering, phishing, and malware payloads. Unlike email, Teams traffic typically bypasses secure email gateways and benefits from the perceived legitimacy of a colleague-initiated chat, which can make lures particularly effective in this environment.

Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June. Financial and executive impersonation has remained largely absent from Teams-based attacks over the past several months.

The dominant lure theme remained technical support impersonation, with attackers posing as an employee’s information technology (IT) help desk, typically warning of an impending account lockout. However, the way attackers presented themselves continued to evolve:

• Display names shifted away from IT- or help desk-branded identities. For the second consecutive month, more than half (52%) of Teams-based phishing attacks in June used generic display names rather than obvious IT support impersonation.
• Attacker email addresses associated with these chats moved away from support-themed domains toward software-as-a-service (SaaS) terminology, scan/update language, and infrastructure keywords. This shift may align with the broader rise of ClickFix-style attacks adopting update-fix and similar themes.

Vishing through Teams showed the steepest growth of any threat category tracked in this report during Q2. Average weekly malicious call attempts rose 31% from April to May and another 27% into June, with the final two weeks of June recording the two highest weekly volumes on record. Since the beginning of 2026, weekly vishing attempts have increased roughly 80% and now run at nearly ten times the mid-2025 baseline. Attackers time these calls deliberately when targets are most likely to be online and active, with the heaviest activity falling between 14:00 and 20:00 UTC, Monday through Friday, with near-zero weekend activity. Notably, a growing share of these calls go unanswered, end quickly, or are rejected outright, partly reflecting Microsoft’s ongoing efforts to harden the Teams attack surface and improve protections against social engineering abuse.

Notable phishing campaigns

The following campaigns were observed during the quarter and highlight notable credential phishing, BEC, and malware delivery activity. For analysis of a separate code of conduct-themed credential phishing campaign observed in April of Q2, see Breaking the code: Multi-stage ‘code of conduct’ phishing campaign leads to AiTM token compromise.

Automated BEC campaign scales aging report and payroll diversion lures

On June 1, 2026, Microsoft Defender Research observed a high-volume BEC campaign that used automation to operate at scale. Over a send window of under three hours (14:08–16:52 UTC), the actor reached more than 67,000 users across more than 42,000 organizations, almost exclusively in the United States. Targeting spanned a broad range of industries rather than a single vertical, most notably retail and consumer goods (17%), technology and software (15%), and financial services (14%). The campaign ran two lures in succession from shared infrastructure: arequest impersonating sales executives to obtain aging report data and customer contact details, and a payroll diversion pretext impersonating the CEO or President to redirect salary payments to attacker-controlled bank accounts.

Delivery was fully scripted. The messages were generated programmatically using Python’s email.mime library, identifiable from its default MIME boundary format (===============[integer]==), and dispatched through the Amazon Simple Email Service (SES) API rather than a manual webmail interface, as indicated by the SES Feedback-ID and Message-ID formats. This allowed the actor to iterate through a recipient list and inject per-message variables (like spoofed executive display names, recipient addresses, and unique tracking identifiers) at volume. Messages were sent from a DomainKeys Identified Mail (DKIM)-configured Slovak domain (ecajovna[.]sk) through SES, so they passed Sender Policy Framework (SPF) and achieved DKIM alignment. Neither lure contained a malicious link or attachment; both relied on eliciting a reply to attacker-controlled mailboxes that mimicked legitimate providers (ilyff[.]com, j-gmails[.]com, x2mails[.]com).

Automation also extended to targeting and follow-up. The actor addressed generic role-based mailboxes (like “ar”, “accountsreceivable”, “hr”, “payroll”) rather than named individuals, reducing per-target effort. Each message embedded a 1×1 open-tracking pixel served from an Amazon SES engagement subdomain, with per-message identifiers that let the actor confirm which recipients opened the email and prioritize follow-up against those targets. The combination of scripted message generation, API-based bulk delivery, role-based targeting, and automated engagement tracking allowed a single actor to run a personalized, financially motivated BEC operation at a scale not practical to execute manually.

Staff update campaign with nested EML file and calendar invitation leads to BAT file dropper

Between June 14–15, 2026, Microsoft Defender Research observed a phishing campaign targeting more than 107,000 users across nearly 19,000 organizations, almost exclusively in the United States. The campaign targeted a broad range of industries rather than a single vertical, most notably financial services (17%), technology and software (14%), and retail and consumer goods (14%). Emails impersonated an internal “Internal Affairs – Financials & Staff Updates” function at the recipient’s own organization, with the display name and subject line both opening with the recipient’s organization name and closing with constant trailing text. The messages were sent from a Postfix host on 9i6pokerdepot[.]com routed through Barracuda’s outbound mail service, and DKIM passed cleanly for the sending domain.

The visible email body contained minimal content. One line told the reader to download the attached file for the meeting summary, followed by a confidentiality notice. Each message carried two attachments: a nested EML posing as a Teams archive recording, and an ICS calendar invite addressed to placeholder administrative accounts at the recipient’s domain. The nested EML’s file name retained an unfilled template token ( {{DATE2}} ), indicating a per-recipient templating tool.

When opened, the EML displayed a voicemail notification with a single action button. That button pointed to Microsoft’s OAuth sign-in endpoint at login.microsoftonline[.]com, with parameters that asked for a silent sign-in attempt against an Entra application that the attacker had registered as multi-tenant.

Because no active sign-in session could satisfy the silent request, Microsoft’s authentication service redirected the recipient to the destination the attacker had pre-registered on the application. That destination was a path on clickup-attachments[.]com, ClickUp’s public attachment host, and served a Windows batch file named Financial_report.bat. Because the link routed through Microsoft authentication infrastructure, both recipients and URL scanners saw a login.microsoftonline[.]com link.

The batch file ran a hidden PowerShell command that pulled installer.exe from pixeldrain[.]com, saved it under the user’s Temp directory, ran it with a silent flag, and deleted the dropper on exit. Rather than stealing credentials, the campaign ultimately resulted in silent malware execution on the user’s Windows device.

Mitigation and protection guidance

Microsoft recommends the following mitigations to reduce the impact of this threat. Check the recommendations card for the deployment status of monitored mitigations.

• Review the recommended settings for Exchange Online Protection and Microsoft Defender for Office 365 to ensure your organization has established essential defenses and knows how to monitor and respond to threat activity.
• Invest in user awareness training and phishing simulations. Attack simulation training in Microsoft Defender for Office 365, which also includes simulating phishing messages in Microsoft Teams, is one approach to running realistic attack scenarios in your organization.
• Enable Zero-hour auto purge (ZAP) in Defender for Office 365 to quarantine sent mail in response to newly acquired threat intelligence and retroactively neutralize malicious phishing, spam, or malware messages that have already been delivered to mailboxes.
• Responders could also manually check for and purge unwanted emails containing URLs and/or Subject fields that are similar, but not identical, to those of known bad messages. Investigate malicious email that was delivered in Microsoft 365 and use Threat Explorer to find and delete phishing emails.
• Turn on Safe Links and Safe Attachments in Microsoft Defender for Office 365.
• Enable network protection in Microsoft Defender for Endpoint.
• Encourage users to use Microsoft Edge and other web browsers that support Microsoft Defender SmartScreen, which identifies and blocks malicious websites, including phishing sites, scam sites, and sites that host malware.
• Enable password-less authentication methods (for example, Windows Hello, FIDO keys, or Microsoft Authenticator) for accounts that support password-less. For accounts that still require passwords, use authenticator apps like Microsoft Authenticator for MFA. Refer to this article for the different authentication methods and features.
  • Conditional access policies can also be scoped to strengthen privileged accounts with phishing resistant MFA.
• Configure automatic attack disruption in Microsoft Defender XDR. Automatic attack disruption is designed to contain attacks in progress, limit the impact on an organization’s assets, and provide more time for security teams to remediate the attack fully.

Microsoft Defender detections

Microsoft Defender customers can refer to the list of applicable detections below. Microsoft Defender coordinates detection, prevention, investigation, and response across endpoints, identities, email, apps to provide integrated protection against attacks like the threat discussed in this blog.

Microsoft Defender for Endpoint

The following alert might indicate threat activity associated with this threat. The alert, however, can be triggered by unrelated threat activity.

• Suspicious activity likely indicative of a connection to an adversary-in-the-middle (AiTM) phishing site

Microsoft Defender for Office 365

The following alerts might indicate threat activity associated with this threat. These alerts, however, can be triggered by unrelated threat activity.

• A potentially malicious URL click was detected
• A user clicked through to a potentially malicious URL
• Suspicious email sending patterns detected
• Email messages containing malicious URL removed after delivery
• Email messages removed after delivery
• Email reported by user as malware or phish

Microsoft Security Copilot

Microsoft Security Copilot is embedded in Microsoft Defender and provides security teams with AI-powered capabilities to summarize incidents, analyze files and scripts, summarize identities, use guided responses, and generate device summaries, hunting queries, and incident reports.

Customers can also deploy AI agents, including the following Microsoft Security Copilot agents, to perform security tasks efficiently:

• Threat Intelligence Briefing agent
• Phishing Triage agent
• Threat Hunting agent
• Dynamic Threat Detection agent

Security Copilot is also available as a standalone experience where customers can perform specific security-related tasks, such as incident investigation, user analysis, and vulnerability impact assessment. In addition, Security Copilot offers developer scenarios that allow customers to build, test, publish, and integrate AI agents and plugins to meet unique security needs.

Threat intelligence reports

Microsoft Defender XDR customers can use the following Threat Analytics reports in the Defender portal (requires license for at least one Defender XDR product) to get the most up-to-date information about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer environments.

Microsoft Defender XDR threat analytics

• Activity Profile: Email threat landscape, June 2026
• Activity Profile: Email threat landscape, May 2026
• Activity Profile: Email threat landscape, April 2026
• Tool Profile: Tycoon2FA
• Technique Profile: QR code phishing
• Technique Profile: ClickFix technique leverages clipboard to run malicious commands
• Threat Overview Profile: Business Email Compromise
• Threat Overview Profile: Adversary-in-the-middle credential phishing

Microsoft Security Copilot customers can also use the Microsoft Security Copilot integration in Microsoft Defender Threat Intelligence, either in the Security Copilot standalone portal or in the embedded experience in the Microsoft Defender portal to get more information about this threat actor.

Indicators of compromise (IOCs)

Learn more

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

The post Email threat landscape: Q2 2026 trends and insights appeared first on Microsoft Security Blog.

Executive Summary

• SentinelLABS developed a multi-stage reverse-engineering benchmark for the latest generation of frontier models by recreating our recent investigation of fast16, a unique 2005 sabotage implant.
• Most AI benchmarks test bounded tasks. This benchmark tests whether a model can keep a malware investigation trustworthy as new evidence repeatedly invalidates its earlier conclusions.
• OpenAI’s GPT-5.6 Sol was the only publicly available model to complete the full eight-stage investigation, giving concrete shape to what ‘Frontier-class’ capabilities offer analysts. GPT-5.5, GLM-5.2, and the Opus 4.x family produced capable local analysis but could not carry it through the gradient.
• What distinguished the completed runs was project-scale recovery: withdrawing contradicted conclusions, repairing technical artifacts, and updating dependent reporting without losing the investigation.
• Senior reverse engineers remain essential. Even the strongest runs made semantic errors, accepted weak quality controls, and claimed readiness prematurely. We assess the best current use as supervised investigative agency, with human analysts defining objectives, exposing blind spots, and retaining final publication authority.

Beyond Vulnerability Discovery

Since ChatGPT arrived in late 2022, we have been bullish on what large language models could do for reverse engineering and malware analysis. The early models were useful for teaching but too rudimentary for production work; that changed with the advent of reasoning models. OpenAI’s o1-preview, in September 2024, was the first to show the kind of sustained problem-solving the work demands, and within months Sean Heelan had used o3 to find a net-new vulnerability in the Linux kernel. In cybersecurity, though, our understanding of what these models can do remains stovepiped to vulnerability discovery.

The frontier labs took on vulnerability discovery deliberately, because that competency keeps agentic code generation from quietly shipping vulnerable code at scale. OpenAI built Aardvark, since folded into Codex; Google DeepMind announced Big Sleep, available internally to its Project Zero researchers; and Anthropic followed with selective access to Mythos Preview. Concerns that these capabilities could be misused have led the labs to stricter guardrails and ‘know your customer’ style controls that limit access to specific capabilities, or to entire model variants.

OpenAI’s Daybreak initiative and its Trusted Access Program opened a dedicated variant, GPT-5.*-cyber-preview, with guardrails relaxed for cybersecurity use cases, while Anthropic’s Glasswing initiative and its Cyber Verification Program provided early access to Mythos Preview and the promise of lesser guardrails respectively. For a short period in mid-June 2026 access to the highest-end flagship models from both providers required some form of U.S. government clearance. At the time of writing, GPT 5.6 Sol is widely available, while Mythos 5 still requires clearance and access as a Glasswing partner.

The existence of this new class of models left us with an unusual task: benchmarking what these models can actually do on the work defenders care about, and assessing whether they live up to the surrounding hype. If they do, we have to reckon with what that means for malware analysis and reverse engineering, disciplines that until now have been limited mostly by how little expertise exists relative to the collective need.

A Benchmark Built From a Real Investigation

We recently published our research on fast16, a 2005 Windows toolkit built to sabotage high-precision solvers used to model nuclear-weapons behavior. The sample provided an ideal test case because its layered design punishes shallow analysis.

On the surface, svcmgmt.exe appears to be a Windows service implant with two embedded components. Their combined logic becomes visible only through a higher layer of abstraction, an encrypted Lua-driven operations framework. A kernel driver easily mistaken for a filesystem rootkit actually contains a patching engine with 101 rules, and those rules must be repurposed to identify candidate target software before an analyst can extrapolate the approximate effects of the sabotage operation. This investigation is a daunting task for human experts, and reproducing it autonomously should be considered a significant milestone for artificial intelligence.

When Vitaly Kamluk shared our discovery of fast16 at Black Hat Asia, he pointed to the failed attempts of Opus 4.6, GPT-5.4, Grok 4.2 Reasoning, Gemini 3.1 Pro, and DeepSeek Reasoner to triage the sample autonomously. At best, the models concluded fast16 was a rootkit and could not move past that assumption, converging on the same plausible but inaccurate conclusion most human reverse engineers reached on first contact.

Used instead as assistants, the models earned their keep. Opus 4.6 and GPT-5.4 compressed what was originally a three-week dedicated expert effort by Kamluk into an 8-10 hour task manageable by a less experienced reverse engineer. Autonomous investigation was out of reach when we began this work; co-intelligence was the ceiling.

Standardizing the Benchmark

We codified our reverse-engineering methodology, inspired by Rolf Rolles’ approach to statically analyzing binaries, into a skill the harness loads on /reverse, which removes prompting variability across models. Each run gives the model a sample, IDA Pro 9.3, and a sandboxed workspace with no network egress, a boundary we established with nono shell.

The model must produce fully annotated IDA databases by renaming variables, recasting data types, categorizing functions, recovering hidden components, and folding those findings back into the host-sample database.

Only after producing a gold-master IDB for approval does a run advance to walking the execution flow and detailing findings, with every claim anchored to specific addresses and artifacts so the work can be independently verified. Judging the model on a refined IDB rather than a prose report closes off the deductive shortcuts and approximations typical of LLM malware triage.

We took an ecological approach, a bounded case study that benchmarks a capability profile in a complex real-world scenario rather than feeding another general leaderboard or isolating a single narrow competency. We also deliberately tested the combination of model and harness, since agentic workflows are how practitioners actually use these systems, and most benchmarks still test models in abstraction.

Runs level up through eight stages, each introducing new artifacts and minimal prompting for the new task:

Measuring ‘Intelligence’

When Nick Fountain interviewed us for NPR’s Planet Money, he observed that fast16’s brand of sabotage amounts to epistemic warfare, an attack on the target’s confidence in its own ability to know something to be true. There is a fitting symmetry in using the same sample to examine what the frontier labs sell as intelligence, because the difficulty there is also epistemic. ‘Intelligence’ as a concept bundles many interwoven capacities, and model tiers simplistically priced as if intelligence gets cranked up with a simple knob tells us nothing about what any one offering is worth on real work.

Many of those capacities already have familiar names, and the names do finer work than a score. Take thoroughness, the completeness of a particular piece of work. An analyst who consistently applies that effort where it matters is diligent. One thorough artifact does not make a diligent analyst; diligence is the longitudinal quality, and thoroughness one of its observable expressions.

Exhaustive coverage is not diligence either. An analyst who inspects everything indiscriminately while neglecting the consequential work is being pedantic, and their effort is wasted on the broader investigation. Diligence, in short, is sustained, priority-sensitive thoroughness.

Many similar qualities sit inside what we call intelligence, and a meaningful benchmark must account for them as we would with a human analyst, by characterizing behavior across an entire investigation rather than compressing it into a rank.

We scored two different things. Local analytical quality asks whether the model made technically sensible decisions with the evidence available. Investigative horizon asks how long it preserved that quality as the scope, evidence, and reporting obligations expanded. The two are related but not the same, and the gap between them is where most models failed.

What the Runs Showed

GPT-5.5 produced long autonomous runs, sometimes grinding uninterrupted for a day or two, with results relatively consistent across reasoning-effort settings. It did meaningful, semantically conservative work refining IDBs and repeatedly rejected edits that improved a count while making the decompilation less truthful. Its weakness was aggregation. Residual cast debt, function-tree defects, or incomplete cross-component verification kept it from clearing broad evaluation gates, and it never converged its extensive work into a complete package for review. Notably, nominal effort settings and multi-agent use showed no monotonic relationship to clearance. GPT-5.5 never escaped the grind phase of level 1.

We ran a single GLM-5.2 run through a serverless deployment on Fireworks AI with a $100 budget. The run showed measured local experimentation and capable backup-based recovery, which it needed after the most severe promotion failure we observed, losing 113 embedded functions and having to restore them before working again. One run is not a characterization of the model, but it is enough to temper claims of GLM-5.2 performing at the level of the frontier completers.

Opus 4.7 and 4.8 advanced further, showing capable technical correction and evidence-responsive revision, including a strong example of converting a speculative relationship into a structural test and rejecting it. Their recurring limitation was milestone overpromotion, a familiar eagerness to convert favorable local results into readiness claims before visible defects were resolved. They required operator challenge at important quality boundaries and did not sustain closure through the full benchmark. We are not altogether certain these newer iterations are in fact performing better than the earlier Opus 4.6 for our use cases.

The common pattern across this tranche was local competence without dependable project-wide closure. These models performed many of the individual behaviors seen in the strongest runs. What they did not demonstrate was the same behavior recurring through the full eight-stage investigation.

GPT-5.6 Sol | A New Class of Contender

When GPT-5.6 Sol was introduced, we seized the opportunity to evaluate it against our real-world benchmark. As the name implies, we expected incremental improvement from GPT-5.5 but instead found the model operating in a league of its own, with all three GPT-5.6 Sol runs completing the benchmark at different effort levels.

Sol was far from infallible. Its runs declared checkpoints clean or complete before later review found meaningful problems. The distinctive pattern was what happened next. Sol repeatedly responded to a local failure with a structural fix, reassessing the quality-control process that had let the mistake through and building scaffolding, such as codifying file hashes for even routine operations, to keep entire classes of failure from recurring.

The xhigh run provides the clearest example because its error began with a valid observation. fast16 contains a program of 101 byte-pattern rules that recognize locations inside an executable, capture addresses, replace code, and repair the executable metadata those changes require. Replaying that rule program across a larger software collection, Sol found that some rules fired not only in LS-DYNA, the engineering package fast16 targets, but also in MOHID and PKPM, two applications built from a similar Fortran lineage. It proposed that fast16 might be a general compatibility component rather than a tool engineered for sabotage.

The collisions were real, and we made similar observations early in our own investigation, but the explanation gave too much weight to the mere presence of matches. The model corrected course by demanding greater rigor of itself, asking whether the matches produced a meaningful modification sequence. In LS-DYNA they did. In MOHID and PKPM they produced only haphazard fragments, often triggered by generic compiler-generated code.

What first looked like broad compatibility was better explained as collateral risk from loose rules. Sol not only self-corrected but found better footing for that correction than we did. It then withdrew the compatibility hypothesis, preserved the valid measurements, restored the LS-DYNA modification mechanism as the authoritative account, and marked the rejected branch as historical provenance so later analysis would not inherit it.

5.6 Sol’s performance has pushed the boundary of the possible for automation in cyber threat investigations. Given its ready availability, we are interested in seeing how these capabilities enable the practitioner ecosystem to advance the state of the art in the same manner as alternate research disciplines like math and physics.

Project-Scale Recovery

What separated the completed runs from the rest was not technical insight, error detection, or willingness to revise. Every cohort supplied examples of each. The recurring difference was the scale at which recovery occurred. Across a long-horizon investigation, the completed runs did more than correct the sentence or script in front of them. They rebuilt the part of the project that later work would trust, in a repeatable pattern:

1. Withdraw the contradicted claim rather than softening its wording while continuing to rely on it.
2. Map the blast radius, identifying every conclusion, artifact, and test that depends on the disputed result.
3. Repair the cause, correcting the analysis or the quality-control gap that let the error through.
4. Carry the correction through the files another analyst or later stage will actually use.
5. Reopen the artifacts and run a check capable of disproving the corrected claim.
6. Separate work that blocks the current conclusion from uncertainty that can be disclosed and deferred.

That last step is easy to underestimate. An investigation can fail by stopping too early, and it can also fail by expanding forever. Deciding which unresolved questions block a conclusion is what convergence means in this benchmark.

This is not a single new reasoning faculty. It is the coordination of familiar capabilities over time, spanning correction, scope control, verification, state management, synthesis, and stopping judgment. The evidence supports project-scale recovery as a common feature of the completed runs. It does not prove a permanent intelligence threshold, and looking only at finishers cannot explain why every other trajectory fell short. The number of runs per family is small, and operator involvement was consequential throughout. What survives those caveats is the modest formulation that the completed runs repeatedly re-established a trustworthy investigative state after later evidence destabilized it.

Notes on Cost

Across all experiments, our runs consumed more than 23 billion tokens across model families and effort levels. The aggregate cache read rate was 93.58%, translating to different discounting rates across providers. Our self-hosted GLM-5.2 run offers a floor for comparison, producing 267.41M tokens on a $100 budget and a decent level-1 attempt charitably comparable to some GPT-5.5 and Opus 4.7 performance.

What These Results Mean for Analysts

The practical lesson stops well short of replacement for senior reverse engineers. The strongest runs still made serious semantic errors, accepted weak quality controls, promoted work too early, and depended on operator review at consequential moments. The new capability is that some runs remained useful after those failures. They owned substantial investigative workstreams, recovered from invalidated theories, preserved the valid parts of earlier work, and continued into publication-oriented analysis without starting over.

That changes the role these systems can plausibly play. A weaker model may be valuable as a local copilot, explaining a function, testing a hypothesis, or drafting a section. The strongest observed runs could act more like supervised investigative agents, maintaining and repairing a larger body of work across days, artifacts, and changing evidence. The supervision qualifier remains essential. Humans defined the investigative objective, controlled evidence release, exposed important blind spots, decided what quality meant, and retained final publication authority.

The scale of the shift deserves emphasis. Reverse engineering and malware analysis have been kept from scaling for three decades by the scarcity of expert practitioners. That constraint does not disappear, since expert guidance still determines whether the work can be trusted, but a single expert supervising these systems can now expand their output by any metric we track.

Conclusion

The line the benchmark exposed ran between local competence and sustained ownership of an investigation, rather than between smarter and lesser models. GPT-5.5, GLM-5.2, and the Opus 4.x family frequently produced valuable analysis. GPT-5.6 Sol also made meaningful errors. What distinguished the completed runs was their repeated ability to recover and diffuse that correction throughout the whole project, rather than just confronting their immediate context.

Having completed this stage of our evaluations, we took the best candidate produced by 5.6 Sol High and put it through an extensive adversarial refinement process using an ensemble of models to produce the highest automatable quality of IDBs and investigation artifacts. We are sharing these so other researchers can verify the work output against the sample and build on them rather than start from the raw binary. We make no claim that it is perfect, only that it is the greatest level of automated refinement the most capable models achieved through proper methodology, repeated standard enforcement, and a lot of tokens burned.

That is a less dramatic claim than a new form of autonomous intelligence. It is also more useful. For defenders, the question that matters most is whether the models can reliably preserve and advance a trustworthy investigation. Once we can count on that, it’s up to us to redefine a greater scope of impact for a new frontier of cyber threat investigations.

Resources

fast16-idbs – Gold-Master IDA Databases

From late April 2026 to mid-June 2026, Microsoft Defender Experts observed increased ACR Stealer activity across customer environments. These campaigns are successfully using ClickFix lures to steal browser credentials, authentication tokens, and sensitive documents from enterprise environments. Successful compromise can expose browser credentials, session tokens, authentication artifacts, and sensitive enterprise data, potentially enabling account compromise, unauthorized access to cloud resources, and follow-on intrusion activity. Security teams should prioritize monitoring for ClickFix lures, suspicious WebDAV activity, obfuscated PowerShell execution, and attempts to access browser credential stores.

ACR Stealer is an information-stealing malware family reportedly offered through a malware-as-a-service (MaaS) model and associated with the rebranding of Amatera Stealer. During this period, two campaigns stand out, together appearing frequently in reviewed recent intrusions. Both begin the same way, with a ClickFix social engineering technique that tricks targets into running the threat actor’s command, but the intrusion chains that follow diverge in how they deliver payloads, establish execution, and evade detection.

The first campaign relies on WebDAV-delivered payloads, staged PowerShell, Python-based loaders and persistence, and, in some intrusions, blockchain-backed dead-drop command-and-control (C2) resolution. The second campaign takes a more fileless route, using MSHTA, obfuscated PowerShell, and steganography-assisted in-memory execution. Despite these differences, both campaigns ultimately pursue the same goal: stealing browser-stored credentials and other sensitive data for exfiltration.

These two campaigns represent some of the most prevalent ACR Stealer delivery campaigns observed by Defender Experts; however, they do not represent the full range of delivery methods used by this malware family. Attribution to ACR Stealer is based on the observed behavior and post-exploitation tradecraft, corroborated by open-source intelligence on the infrastructure associated with this malware family. Additional campaigns, infrastructure patterns, and execution chains are likely active, and organizations should treat the indicators and techniques described here as representative.

Microsoft Defender for Endpoint can help surface both campaigns through behavioral coverage for living-off-the-land execution, suspicious WebDAV and MSHTA activity, obfuscated PowerShell, scheduled-task persistence, in-memory payload execution, and browser credential theft. In this blog, we analyze both campaigns in detail, including their delivery mechanisms, post-exploitation tradecraft, indicators of compromise, hunting opportunities, and guidance to help defenders detect and disrupt related activity in their environments.

Campaign 1: WebDAV-based ClickFix with Python loaders and blockchain C2

Initial access

In this campaign, a ClickFix prompt, likely delivered through malvertising or SEO-manipulated search results, instructs the target user to run a command that launches cmd.exe. The command subsequently invokes rundll32.exe to load a DLL from a remote WebDAV share accessed over HTTPS. The WebDAV path commonly uses a GUID-based directory structure and filenames designed to resemble legitimate resources (for example, google.ct), enabling the activity to blend with expected network traffic and evade casual inspection.

We observed three variants of the initial execution command:

Variant 1: Direct rundll32 invocation

Variant 2: pushd-Mounted WebDAV Share

Variant 3: Headless and obfuscated pushd execution

Variants 2 and 3 are notable for their use of pushd, which transparently maps the remote WebDAV share to a temporary local drive prior to execution. This technique allows threat actors to execute remotely hosted content through what appears to be a local path, simplifying payload execution while reducing user awareness. In the more advanced variant, threat actors further enhance stealth by launching commands through conhost.exe –headless, suppressing visible console windows, and employing environment variable obfuscation with delayed variable expansion to conceal critical execution components such as pushd, rundll32, and the remote host name. Combined with minimized or headless execution, these techniques reduce user visibility, complicate static analysis and detection, and enable the infection chain to execute with minimal indication to the victim.

Execution, persistence, and evasion through process masquerading

Once rundll32.exe loads the DLL retrieved from the remote server, the malware establishes communication with threat actor-controlled infrastructure and executes a heavily obfuscated PowerShell script. The script employs excessive arithmetic no-ops, dead loops, fake control flow, and randomized variable names to hinder static analysis and evade signature-based detection.

The PowerShell script subsequently deploys another stage that functions as both a malware installer and a persistence mechanism. It:

• Downloads a ZIP-packaged payload from a remote server and extracts it into a deceptive directory under %LocalAppData%\Temp (for example, LogiOptionsPlus).
• Launches a Python script using a bundled pythonw.exe instance to avoid displaying a console window.
• Removes previous deployments and terminates running instances before installation, effectively operating as an updater.
• Establishes persistence through a hidden scheduled task disguised as a legitimate software update, ensuring execution at user sign-in.
• Copies timestamps from a trusted Windows binary (notepad.exe) to the deployed files and clears PowerShell command history to reduce forensic visibility.

Python loader launching the stealer

The Python component serves as a heavily obfuscated loader designed to conceal its true functionality until runtime. It employs multiple layers of defense against static analysis, including dynamic API resolution, encoded string reconstruction, junk-data removal, character shifting, string reversal, Base64 decoding, and zlib decompression. These techniques ensure that the embedded payload remains unreadable in its static form and is reconstructed only during execution, significantly hindering signature-based detection and automated analysis.

Once decoded, the final-stage payload functions as an in-memory shellcode loader. It extracts an archive file masquerading as a legitimate application installer, reads a file from the archive, and injects the payload into a system process. The loader allocates executable memory using VirtualAlloc, copies the payload into the allocated memory region, and transfers execution through the Windows Fiber API (ConvertThreadToFiber, CreateFiber, and SwitchToFiber). This technique facilitates stealthy in-memory execution while minimizing artifacts written to disk.

Credential theft and data staging for exfiltration

The malware (injected code) aggressively harvests information from browser credential stores. It invokes Windows Data Protection API (DPAPI) routines to decrypt locally stored browser passwords, cookies, and authentication tokens. It also enumerates files across the system, targeting PDFs, Microsoft 365 documents, and data stored in enterprise-synchronized directories such as OneDrive and SharePoint. The collected data is subsequently archived, indicating preparation for exfiltration.

Blockchain dead-drop C2 resolution

A notable variation in this campaign is the use of blockchain services for C2 resolution, utilizing a technique known as EtherHiding. While most intrusions rely on more conventional C2 mechanisms, a subset deploys an additional secondary Python loader that leverages blockchain services as dead-drop resolvers. When this loader executes, it has been observed communicating with public blockchain RPC endpoints and third-party Web3 node infrastructure, likely querying data stored on a decentralized public ledger to retrieve follow-up payloads or a C2 address.

By externalizing C2 information to the blockchain, operators could dynamically update infrastructure without modifying or redeploying the malware, significantly complicating detection and takedown efforts. This behavior was observed across both variants of the campaign.

Campaign 2: MSHTA-initiated PowerShell chain with steganographic payload delivery

The second campaign takes a distinctly different approach to both delivery and execution. Where Campaign 1 relies on disk-based artifacts (Python runtime, scheduled tasks, and masquerading binaries), this campaign achieves its objectives almost entirely through fileless, in-memory execution, making it harder to detect through file-based scanning and forensic analysis.

Initial access through MSHTA and ClickFix

The execution chain begins when the victim, directed through malvertising or SEO-manipulated search results, encounters a ClickFix prompt that triggers a command spawning MSHTA to fetch and execute remote HTA content from an threat actor-controlled domain. The embedded VBScript loader abuses COM objects to decode and execute encoded PowerShell content.

PowerShell downloader and obfuscation

The decoded PowerShell stage employs obfuscation techniques similar to those seen in Campaign 1: randomized variable names, arithmetic no-op operations, dead loops, misleading control flow, and custom encryption routines. Prior to contacting its next-stage infrastructure, the malware generates a victim-specific identifier and disables certificate validation. The retrieved content is executed directly in memory.

Steganography-based payload delivery

A notable technique in this campaign is the use of steganography to conceal malicious content inside a publicly hosted image. Instead of downloading a secondary script (as in Campaign 1), the malware retrieves a JPEG image from an image-hosting service.

Analysis of the script revealed custom routines that extract an embedded payload from image pixels, decrypt and decompress it, and execute it entirely in memory. The payload dynamically resolves APIs such as LoadLibrary, GetProcAddress, VirtualAlloc, CreateThread, and WaitForSingleObject at runtime to perform reflective shellcode execution. By combining steganography with in-memory execution, the malware minimizes on-disk artifacts and complicates both detection and analysis.

Credential theft, data collection, and exfiltration

Following execution, the malware accesses credential stores belonging to Chromium-based browsers, including Google Chrome and Microsoft Edge, specifically the Login Data and Web Data databases, alongside Windows DPAPI decryption activity. This behavior indicates attempts to recover stored browser credentials, session cookies, authentication tokens, and other sensitive user information.

The malware also enumerates and accesses multiple high-value PDF documents across Desktop and Downloads locations, suggesting targeted collection of potentially sensitive files. The combination of browser credential harvesting and systematic document access points to an information-stealing objective focused on staging credentials and valuable user data for exfiltration.

Mitigation and protection guidance

Microsoft recommends the following mitigations to reduce the impact of ClickFix lures, script-based payload delivery, credential theft, and post-compromise activity.

• Educate users to recognize ClickFix-style prompts, fake verification checks, and paste-and-run instructions as malicious, especially when they invoke command interpreters or script hosts such as cmd.exe, PowerShell, rundll32.exe, or mshta.exe.
• Reduce exposure to malvertising, SEO poisoning, and other web-based delivery chains by enforcing web filtering, blocking low-reputation or newly observed domains, and limiting access to remote content sources that are not required for business operations.
• Use application control and attack surface reduction rules to restrict PowerShell, Python, mshta.exe, rundll32.exe, and similar tools from launching untrusted or internet-delivered content, particularly from user-writable directories such as Downloads, Temp, and %LocalAppData%.
• Monitor for suspicious persistence and defense-evasion behavior, including scheduled tasks masquerading as software updates, timestomping, PowerShell history clearing, and execution chains that progress from remote content retrieval into PowerShell, Python, or shellcode-loading behavior.
• Investigate abnormal access to Chromium-based browser databases, DPAPI-related decryption activity, staged collection of Microsoft 365 documents or PDFs, and compression activity that may indicate credential theft or data staging for exfiltration.
• If compromise is suspected, isolate affected devices, rotate exposed credentials, revoke potentially compromised tokens, review persistence mechanisms, and investigate outbound connections to remote shares, image-hosting services, or other infrastructure used to resolve or retrieve follow-on payloads.
• Harden endpoints against credential theft by reducing reliance on browser-stored credentials, enforcing multifactor authentication and conditional access, and reviewing how privileged accounts access sensitive applications and synchronized enterprise data.
• Turn on cloud-delivered protection and behavior-based detections to help identify rapidly evolving threats, suspicious script execution, in-memory payload delivery, abuse of browser credential stores, and unusual child-process activity.
• Run endpoint detection and response (EDR) in block mode and enable automated investigation and remediation so post-breach detections are contained, and malicious artifacts can be removed with minimal delay.
• Harden PowerShell by enforcing appropriate execution policies, turning on script block logging, module logging, and transcription, and monitoring this telemetry for signs of malicious script activity.
• Turn on tamper protection and prevent local administrators from weakening antivirus protection through local policy or exclusion changes.

Microsoft Defender XDR detections

Microsoft Defender XDR customers can refer to the list of applicable detections below. Microsoft Defender XDR coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog. 

Microsoft Security Copilot

Microsoft Security Copilot is embedded in Microsoft Defender and provides security teams with AI-powered capabilities to summarize incidents, analyze files and scripts, summarize identities, use guided responses, and generate device summaries, hunting queries, and incident reports.

Customers can also deploy AI agents, including the following Microsoft Security Copilot agents, to perform security tasks efficiently:

• Threat Intelligence Briefing agent
• Phishing Triage agent
• Threat Hunting agent
• Dynamic Threat Detection agent

Security Copilot is also available as a standalone experience where customers can perform specific security-related tasks, such as incident investigation, user analysis, and vulnerability impact assessment. In addition, Security Copilot offers developer scenarios that allow customers to build, test, publish, and integrate AI agents and plugins to meet unique security needs.

Threat intelligence reports

Microsoft Defender XDR customers can use the following threat analytics reports in the Defender portal (requires license for at least one Defender XDR product) to get current information available in the Defender portal about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide the intelligence, protection information, and recommended actions to help prevent, mitigate, or respond to associated threats found in customer environments:

• Amatera Stealer: Rebranded ACR Stealer With Improved Evasion, Sophistication
• Double Trouble: Latrodectus and ACR Stealer observed spreading via Google Authenticator Phishing Site

Microsoft Security Copilot customers can also use the Microsoft Security Copilot integration in Microsoft Defender Threat Intelligence, either in the Security Copilot standalone portal or in the embedded experience in the Microsoft Defender portal to get more information about this threat actor.

Advanced hunting queries

Microsoft Defender XDR customers can run the following advance hunting queries to find related activity in their networks:

Run the query below to identify suspicious commands executed through ClickFix-based activity observed while delivering this stealer

DeviceRegistryEvents
| where RegistryKey has "RunMRU"
| where (RegistryValueData has_all ("rundll32", "@ssl", " /c ", " start ") and (RegistryValueData matches regex @"\\\\[^\\]+@ssl\\[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}\\\w+\.\w+,#1" or
RegistryValueData matches regex @"(?i)pushd \\\\[^\\]+@ssl\\[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12} ")) 
or RegistryValueData has_all ("@ssl", " /c ", "conhost --headless ") and RegistryValueData contains "rundll32"

Run the query below to identify scheduled task creation used for persistence by a malicious PowerShell script

DeviceProcessEvents
| where InitiatingProcessFileName =~ "powershell.exe"
| where InitiatingProcessCommandLine has_all ("-Command", "powershell")
| where ProcessCommandLine has_all ("schtasks", " /run /tn ", " Autoupdate ") and ProcessCommandLine matches regex "[0-9]{8}"

Run the query below to identify suspicious MSHTA launch through PowerShell

DeviceProcessEvents
| where InitiatingProcessParentFileName has "explorer.exe"
| where InitiatingProcessFileName =~ "powershell.exe" and InitiatingProcessCommandLine in~ ('"PowerShell.exe" ', '"PowerShell.exe"')
| where ProcessCommandLine has_all ('"mshta.exe" https://') and ProcessCommandLine matches regex "/[0-9]{7}"

MITRE ATT&CK techniques observed

The following mapping summarizes the primary tactics and techniques observed across the two ACR Stealer intrusion chains. The mapping is intended to help defenders align observed behaviors with existing detection coverage, response playbooks, and hunting priorities.

Indicators of compromise (IOC)

References

• Intelligence Insights: May 2026 | Red Canary
• Possible ACR Stealer From Page Impersonating Claude

Learn more

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.   

• Evaluate your AI readiness with our latest Zero Trust for AI workshop.
• Microsoft 365 Copilot AI security documentation 
• How Microsoft discovers and mitigates evolving attacks against AI guardrails 
• Learn more about securing Copilot Studio agents with Microsoft Defender  

The post ACR Stealer: Two observed intrusion chains amid increased threat activity appeared first on Microsoft Security Blog.

Apple is urging users to treat any suspicious FaceTime call or message as untrusted, especially if it involves payments, refunds, password resets, or requests for personal information.

This warning appears in a broader Apple support article about scams that target iPhone and iPad users through social engineering. Apple says attackers may contact people by phone calls, FaceTime, text messages, or emails while pretending to represent a trusted organization.

The advice comes as many users still delay installing security updates, leaving them exposed even after Apple has released security patches.

Over recent months, we’ve seen a familiar pattern: attackers combine convincing Apple‑branded social engineering with known iOS vulnerabilities, then profit from the gap between “patch available” and “patch installed.”

Criminals have been reported making unsolicited FaceTime calls that look like they come from “Apple Support” or a bank, alongside messages disguised as urgent account alerts or refund offers. Once the victim answers or replies, the script is pretty much standard:

• The caller claims there is fraudulent activity or a technical problem.
• They pressure the user to “verify” card details, online banking credentials, or Apple ID information.
• In some cases, they persuade the victim to install remote‑access software or share one‑time passcodes.

Nothing in this process requires malware on the device. The “exploit” is human trust, backed by familiar names, logos, and a real‑time call that feels inherently more legitimate than a text message. That makes FaceTime a useful delivery channel for social engineering, especially since users are used to seeing Apple notifications and support prompts elsewhere in their digital life.

From an attacker’s point of view, combining social engineering with vulnerabilities is attractive. Social engineering can steal your usernames and passwords, while a browser‑side exploit can silently execute malicious code when users visit a booby‑trapped site. Chain those attacks together and the attacker can move from app‑level compromise to full system control. That’s how campaigns like DarkSword operate.

How to stay safe

Apple’s advice is straightforward:

• Don’t trust unexpected calls or texts.
• Never share sensitive information over unsolicited contact.
• Keep your iPhone updated to the latest iOS version.

We’d add:

• Protect your devices with an up-to-date, real-time security solution.
• Contact companies directly through trusted channels. Don’t use contact details provided in an email, text message, or call.
• Use Malwarebytes Scam Guard to determine whether a message is likely to be a scam.
• Be especially suspicious of unexpected FaceTime calls claiming to be from your bank or Apple. These organizations are unlikely to use FaceTime to contact you about serious account issues.

Apple also asks users to report suspicious FaceTime calls:

“If you receive a suspicious FaceTime call (for example, from what looks like a bank or financial institution), email a screenshot of the call information to reportfacetimefraud@apple.com.”

How to update your iPhone or iPad

To check whether you’re running the latest version of iOS or iPadOS:

• Go to Settings > General > Software Update. If an update is available, you’ll be able to download and install it from there.
• Turn on Automatic Updates if you haven’t already. You’ll find it on the same screen. That way, your device can install important security updates as soon as they’re available.

Scammers know more about you than you think. 

Malwarebytes Mobile Security protects you from phishing, scam texts, malicious sites, and more. With real-time AI-powered Scam Guard built right in. 

Download for iOS → Download for Android → 

In a series of campaigns observed between mid-2025 and mid-2026, Microsoft identified threat actor activity with overlapping tradecraft commonly associated with ShinyHunters, including voice phishing (vishing) and supply chain compromise, to target customer SaaS-based applications such as Salesforce instances. The threat actors abused trusted OAuth relationships for unauthorized access, data exfiltration, and persistence.

Two primary intrusion paths were observed including vishing techniques targeting OAuth consent and supply chain compromise through trusted workflows and integrations such as Salesloft and Gainsight. Abuse of these access paths led to inherited user and application privileges, allowing successful enumeration and querying of customer relationship management (CRM) records while evading conventional authentication detections. These intrusion paths often led to persistent access and exfiltration of data at scale. This tradecraft highlights how a single entry point can rapidly expand to greater enterprise impacts.

Microsoft observed activity associated with these techniques in many tenants from various industries such as retail, education and manufacturing. These findings reinforce the importance of monitoring OAuth-connected applications, validating third-party integrations, reviewing configurations, and enabling Salesforce event monitoring. Leveraging this data, Microsoft consulted with Salesforce to improve granularity in telemetry for Defender for Cloud Apps with near-real-time detection, offering connected application attribution and expanded application permission insights. This activity was not the result of a vulnerability inherent to Salesforce. Rather, the threat actors abused trusted OAuth relationships for unauthorized access, data exfiltration, and persistence.

Attack chain overview

Threat actor campaigns targeting Salesforce customers and using tradecraft associated with ShinyHunters pose a high-impact risk to sensitive data and downstream SaaS ecosystems. These campaigns abuse OAuth trust relationships to operate within pre-existing, legitimate workflows.

Observed activity can be grouped into two primary intrusion paths:

Voice‑phishing-driven OAuth consent abuse

In campaigns beginning in mid-2025, the threat actors conducted vishing attacks impersonating IT support personnel. Threat actors socially engineered employees into authorizing attacker-controlled connected apps within their Salesforce tenant. In several confirmed cases, threat actors guided users through the OAuth consent workflow to grant access to a malicious application disguised as a legitimate Salesforce Data Loader tool. After users granted consent, these highly privileged OAuth applications enabled threat actors to perform API calls on behalf of the victim user, facilitating:

• Enumeration of Salesforce instances belonging to targeted organizations
• Persistent access to Salesforce CRM data
• Possible lateral movement into other SaaS platforms through discovered credentials

This intrusion path exploits the OAuth authorization flow of trusted SaaS services rather than relying on malware or credential replay. Threat actors exfiltrate data through sanctioned application access inherited from user privileges.

SaaS supply‑chain compromise targeting trusted integrations

Following initial access campaigns, threat actors  escalated into supply‑chain-driven attacks targeting third‑party SaaS vendors offering popular solutions that integrate with Salesforce, often using OAuth tokens. In August 2025, compromised Salesloft Drift credentials enabled attackers to obtain connection secrets used by downstream SaaS applications, enabling the use of OAuth tokens in multiple customer Salesforce instances.

A subsequent campaign in November 2025 targeted Gainsight-published applications integrated with Salesforce, allowing attackers to leverage trusted external connections to maintain persistent API access in multiple Salesforce customer instances. These activities often appeared indistinguishable from legitimate integration behavior. Threat actors performed discovery, bulk data queries, and mass exfiltration of sensitive CRM records, including accounts, contacts, and service case data, without generating traditional sign-in anomalies.
More recently, in June 2026, the market intelligence platform Klue experienced an incident where a threat actor, Storm-3138, gained access to its system.  Credentials used to access Salesforce customer instances were used in the same fashion, to discover, query, and exfiltrate data.

Improving visibility into Salesforce OAuth abuse

For customers using Salesforce Shield: Event Monitoring, the upgraded Microsoft Defender for Cloud Apps Salesforce connector onboards the Real-Time Event Monitoring (RTEM) framework, enabling faster detection and investigation of Salesforce-based attacks.

Investigations into these campaigns exposed a recurring challenge for security teams: malicious activity often appeared indistinguishable from legitimate Salesforce usage because threat actors operated through trusted identities, approved OAuth applications, and authorized integrations. Traditional authentication-focused detections frequently provided limited visibility into the resulting application activity.

To improve investigation and detection of these scenarios, Microsoft expanded Salesforce visibility in Defender for Cloud Apps through additional event telemetry, connected application attribution, and enhanced application permissions insights. These capabilities help security teams identify suspicious OAuth activity, investigate potentially compromised integrations, and better understand how access was obtained and used within customer Salesforce instances.

Key capabilities include:

• Near-real-time visibility into Salesforce security and activity events.
• Connected application attribution, including application identity and granted OAuth scopes.
• Expanded identity, session, and API activity context to support investigations.
• Improved correlation within Microsoft Defender to help identify suspicious activity spanning identities, applications, and SaaS environments.

Together with Salesforce Shield: Event Monitoring, these capabilities help security teams investigate suspicious OAuth activity, validate the legitimacy of connected applications, and better understand the potential impact of a compromise.

New posture and governance capabilities for connected OAuth apps

While improved detection is critical, recent incidents have also highlighted the need for stronger preventive controls and ongoing governance of OAuth-connected applications. To address this, Microsoft Defender introduces new posture capabilities for connected and external client apps in Salesforce. Security teams can gain visibility into each OAuth app and its non-human identity, prioritize risk, and reduce the attack surface.

Deep visibility into app permissions and access

Microsoft Defender provides comprehensive visibility into all Salesforce-integrated connected and external client apps, including granted OAuth scopes and privileges.

Highly privileged apps

Security teams often struggle to identify applications with powerful administrative or sensitive permissions. The highly privileged apps insight highlights applications that have been granted elevated scopes, enabling quick identification of apps that may pose significant risk.

Additionally, security teams can use permission-based filters to identify apps with specific high-risk scopes and validate whether such access is justified.

Unused apps

Organizations often create applications for temporary or one-time use, but those applications are rarely removed afterward. These unused apps continue to retain permissions, creating unnecessary exposure. With the recent changes, Defender now allows security teams to identify applications that have been inactive for extended periods (for example, 90 days or more), making it easy to review and revoke access where appropriate to reduce the attack surface.

Risk-based prioritization of connected apps

To further streamline investigation and response, Defender introduces a comprehensive risk scoring model for connected applications. Each application is assigned a numerical risk score [0-100] based on multiple risk indicators, such as usage patterns, permission sensitivity, and behavioral signals. This allows security teams to prioritize efforts effectively and focus on applications that require immediate attention. Security teams can create custom policies based on risk thresholds to trigger alerts, actions, and notifications.

Risk score investigation

To further investigate the specific Non-Human identity risk details, the factors contributing to the risk score are available in Non-Human Identities Risk score tab.

Mitigation and protection guidance

Microsoft recommends the following mitigations to reduce the impact of this threat. Check the recommendations card for the deployment status of monitored mitigations.  

• Microsoft Defender for Cloud Apps customers can connect their Salesforce instance to get improved visibility and threat detection capabilities.
• Implement best practices as recommended by Salesforce.
• Secure your Experience Cloud Guest User access
• Familiarize yourself with and proactively monitor Salesforce event logs.

Microsoft Defender detections

Microsoft Defender customers can refer to the list of applicable detections including new detections powered by the upgraded Microsoft Defender for Cloud Apps Salesforce connector. Microsoft Defender coordinates detection, prevention, investigation, and response for endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.

Customers with provisioned access can also use Microsoft Security Copilot in Microsoft Defender to investigate and respond to incidents, hunt for threats, and protect their organization with relevant threat intelligence.

Threat intelligence reports 

Microsoft customers can use the following reports in Microsoft products to get the most up-to-date information about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer Salesforce instances.

• OSINT Profile: ShinyHunters Calling: Financially Motivated Data Extortion Group Targeting Enterprise Cloud Applications

Advanced hunting

NOTE: The sample queries let you search one week of events. To inspect events and hunt for threat actor-related indicators over a longer period, go to the Advanced Hunting page > Query tab, and use the calendar dropdown to set the time range to Last 30 days (the maximum for raw data).

Hunt for Salesforce connected-app activity from suspicious infrastructure

CloudAppEvents
| where Application == "Salesforce"
| where ActionType in ("ApiTotalUsage", "API Event")
| extend ConnectedAppId = tostring(
    coalesce(
        RawEventData.CONNECTED_APP_ID, // from ApiTotalUsage 
        RawEventData.ConnectedAppId // from API Event
    )
)
| where isnotempty(ConnectedAppId)
| where array_length(UncommonForUser) > 0 // at least 1 attribute is flagged as uncommon

Hunt for API activity associated with connected apps and relevant user ids

CloudAppEvents
| where Application == "Salesforce"
| where ActionType in ("ApiTotalUsage", "API Event")
| extend SalesforceUserId=coalesce(tostring(RawEventData.USER_ID), tostring(RawEventData.UserId))
| extend ConnectedAppName=tostring(RawEventData.CONNECTED_APP_NAME)  // Connected App Name is not available on the ApiEvent event
| summarize count() by AccountObjectId, AccountId, AccountDisplayName, SalesforceUserId, IPAddress, UserAgent, ConnectedAppName

Hunt for anomalous report export / large data access

CloudAppEvents
| where Application == "Salesforce"
| where ActionType  == "ReportExport"
| extend SalesforceUserId = tostring(RawEventData.USER_ID)
| summarize Events=count() by AccountObjectId, AccountId, AccountName, SalesforceUserId, IPAddress, UserAgent

Pivot from a suspicious connected app (name/id) to impacted users and actions

CloudAppEvents
| where Application == "Salesforce"
| where RawEventData has ""
| project Timestamp, AccountId, AccountDisplayName, ActionType, IPAddress, UserAgent, RawEventData
| order by Timestamp desc

Audit queries to verify what objects users are accessing

CloudAppEvents
| where Application == "Salesforce"
| where ActionType == "UniqueQuery"
| extend 
    QueryText = tostring(RawEventData.QUERY_IDENTIFIER), // Full query text
    QueryObject = extract(@"(?i)\bfrom\s+([^\s]+)", 1, tostring(RawEventData.QUERY_IDENTIFIER)), // Extract just the target object
    SalesforceUserId = tostring(RawEventData.USER_ID)
| where QueryText != "SOQL"
| project Timestamp, AccountDisplayName, SalesforceUserId, QueryObject, QueryText

Hunt for users with very high Defender risk score signing into Salesforce