During Q1 2026, the exploit kits leveraged by threat actors to target user systems expanded once again, incorporating new exploits for the Microsoft Office platform, as well as Windows and Linux operating systems.

In this report, we dive into the statistics on published vulnerabilities and exploits, as well as the known vulnerabilities leveraged by popular C2 frameworks throughout Q1 2026.

Statistics on registered vulnerabilities

This section provides statistical data on registered vulnerabilities. The data is sourced from cve.org.

We examine the number of registered CVEs for each month starting from January 2022. The total volume of vulnerabilities continues rising and, according to current reports, the use of AI agents for discovering security issues is expected to further reinforce this upward trend.

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

Next, we analyze the number of new critical vulnerabilities (CVSS > 8.9) over the same period.

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

The graph indicates that while the volume of critical vulnerabilities slightly decreased compared to previous years, an upward trend remained clearly visible. At present, we attribute this to the fact that the end of last year was marked by the disclosure of several severe vulnerabilities in web frameworks. The current growth is driven by high-profile issues like React2Shell, the release of exploit frameworks for mobile platforms, and the uncovering of secondary vulnerabilities during the remediation of previously discovered ones. We will be able to test this hypothesis in the next quarter; if correct, the second quarter will show a significant decline, similar to the pattern observed in the previous year.

Exploitation statistics

This section presents statistics on vulnerability exploitation for Q1 2026. The data draws on open sources and our telemetry.

Windows and Linux vulnerability exploitation

In Q1 2026, threat actor toolsets were updated with exploits for new, recently registered vulnerabilities. However, we first examine the list of veteran vulnerabilities that consistently account for the largest share of detections:

• CVE-2018-0802: a remote code execution (RCE) vulnerability in the Equation Editor component
• CVE-2017-11882: another RCE vulnerability also affecting Equation Editor
• CVE-2017-0199: a vulnerability in Microsoft Office and WordPad that allows an attacker to gain control over the system
• CVE-2023-38831: a vulnerability resulting from the improper handling of objects contained within an archive
• CVE-2025-6218: a vulnerability allowing the specification of relative paths to extract files into arbitrary directories, potentially leading to malicious command execution
• CVE-2025-8088: a directory traversal bypass vulnerability during file extraction utilizing NTFS Streams

Among the newcomers, we have observed exploits targeting the Microsoft Office platform and Windows OS components. Notably, these new vulnerabilities exploit logic flaws arising from the interaction between multiple systems, making them technically difficult to isolate within a specific file or library. A list of these vulnerabilities is provided below:

• CVE-2026-21509 and CVE-2026-21514: security feature bypass vulnerabilities: despite Protected View being enabled, a specially crafted file can still execute malicious code without the user’s knowledge. Malicious commands are executed on the victim’s system with the privileges of the user who opened the file.
• CVE-2026-21513: a vulnerability in the Internet Explorer MSHTML engine, which is used to open websites and render HTML markup. The vulnerability involves bypassing rules that restrict the execution of files from untrusted network sources. Interestingly, the data provider for this vulnerability was an LNK file.

These three vulnerabilities were utilized together in a single chain during attacks on Windows-based user systems. While this combination is noteworthy, we believe the widespread use of the entire chain as a unified exploit will likely decline due to its instability. We anticipate that these vulnerabilities will eventually be applied individually as initial entry vectors in phishing campaigns.

Below is the trend of exploit detections on user Windows systems starting from Q1 2025.

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

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

On Linux devices, exploits for the following vulnerabilities were detected most frequently:

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

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

In the first quarter of 2026, we observed a decrease in the number of detected exploits; however, the detection rates are on the rise relative to the same period last year. For the Linux operating system, the installation of security patches remains critical.

Most common published exploits

The distribution of published exploits by software type in Q1 2026 features an updated set of categories; once again, we see exploits targeting operating systems and Microsoft Office suites.

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

Vulnerability exploitation in APT attacks

We analyzed which vulnerabilities were utilized in APT attacks during Q1 2026. The ranking provided below includes data based on our telemetry, research, and open sources.

TOP 10 vulnerabilities exploited in APT attacks, Q1 2026 (download)

In Q1 2026, threat actors continued to utilize high-profile vulnerabilities registered in the previous year for APT attacks. The hypothesis we previously proposed has been confirmed: security flaws affecting web applications remain heavily exploited in real-world attacks. However, we are also observing a partial refresh of attacker toolsets. Specifically, during the first quarter of the year, APT campaigns leveraged recently discovered vulnerabilities in Microsoft Office products, edge networking device software, and remote access management systems. Although the most recent vulnerabilities are being exploited most heavily, their general characteristics continue to reinforce established trends regarding the categories of vulnerable software. Consequently, we strongly recommend applying the security patches provided by vendors.

C2 frameworks

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

The chart below shows the frequency of known C2 framework usage in attacks against users during Q1 2026, according to open sources.

TOP 10 C2 frameworks used by APTs to compromise user systems, Q1 2026 (download)

Metasploit has returned to the top of the list of the most common C2 frameworks, displacing Sliver, which now shares the second position with Havoc. These are followed by Covenant and Mythic, the latter of which previously saw greater popularity. After studying open sources and analyzing samples of malicious C2 agents that contained exploits, we determined that the following vulnerabilities were utilized in APT attacks involving the C2 frameworks mentioned above:

• CVE-2023-46604: an insecure deserialization vulnerability allowing for arbitrary code execution within the server process context if the Apache ActiveMQ service is running
• CVE-2024-12356 and CVE-2026-1731: command injection vulnerabilities in BeyondTrust software that allow an attacker to send malicious commands even without system authentication
• CVE-2023-36884: a vulnerability in the Windows Search component that enables command execution on the system, bypassing security mechanisms built into Microsoft Office applications
• CVE-2025-53770: an insecure deserialization vulnerability in Microsoft SharePoint that allows for unauthenticated command execution on the server
• CVE-2025-8088 and CVE-2025-6218: similar directory traversal vulnerabilities that allow files to be extracted from an archive to a predefined path, potentially without the archiving utility displaying any alerts to the user

The nature of the described vulnerabilities indicates that they were exploited to gain initial access to the system. Notably, the majority of these security issues are targeted to bypass authentication mechanisms. This is likely due to the fact that C2 agents are being detected effectively, prompting threat actors to reduce the probability of discovery by utilizing bypass exploits.

Notable vulnerabilities

This section highlights the most significant vulnerabilities published in Q1 2026 that have publicly available descriptions.

CVE-2026-21519: Desktop Window Manager vulnerability

At the core of this vulnerability is a Type Confusion flaw. By attempting to access a resource within the Desktop Window Manager subsystem, an attacker can achieve privilege escalation. A necessary condition for exploiting this issue is existing authorization on the system.

It is worth noting that the DWM subsystem has been under close scrutiny by threat actors for quite some time. Historically, the primary attack vector involves interacting with the NtDComposition* function set.

RegPwn (CVE-2026-21533): a system settings access control vulnerability

CVE-2026-21533 is essentially a logic vulnerability that enables privilege escalation. It stems from the improper handling of privileges within Remote Desktop Services (RDS) components. By modifying service parameters in the registry and replacing the configuration with a custom key, an attacker can elevate privileges to the SYSTEM level. This vulnerability is likely to remain a fixture in threat actor toolsets as a method for establishing persistence and gaining high-level privileges.

CVE-2026-21514: a Microsoft Office vulnerability

This vulnerability was discovered in the wild during attacks on user systems. Notably, an LNK file is used to initiate the exploitation process. CVE-2026-21514 is also a logic issue that allows for bypassing OLE technology restrictions on malicious code execution and the transmission of NetNTLM authentication requests when processing untrusted input.

Clawdbot (CVE-2026-25253): an OpenClaw vulnerability

This vulnerability in the AI agent leaks credentials (authentication tokens) when queried via the WebSocket protocol. It can lead to the compromise of the infrastructure where the agent is installed: researchers have confirmed the ability to access local system data and execute commands with elevated privileges. The danger of CVE-2026-25253 is further compounded by the fact that its exploitation has generated numerous attack scenarios, including the use of prompt injections and ClickFix techniques to install stealers on vulnerable systems.

CVE-2026-34070: LangChain framework vulnerability

LangChain is an open-source framework designed for building applications powered by large language models (LLMs). A directory traversal vulnerability allowed attackers to access arbitrary files within the infrastructure where the framework was deployed. The core of CVE-2026-34070 lies in the fact that certain functions within langchain_core/prompts/loading.py handled configuration files insecurely. This could potentially lead to the processing of files containing malicious data, which could be leveraged to execute commands and expose critical system information or other sensitive files.

CVE-2026-22812: an OpenCode vulnerability

CVE-2026-22812 is another vulnerability identified in AI-assisted coding software. By default, the OpenCode agent provided local access for launching authorized applications via an HTTP server that did not require authentication. Consequently, attackers could execute malicious commands on a vulnerable device with the privileges of the current user.

Conclusion and advice

We observe that the registration of vulnerabilities is steadily gaining momentum in Q1 2026, a trend driven by the widespread development of AI tools designed to identify security flaws across various software types. This trajectory is likely to result not only in a higher volume of registered vulnerabilities but also in an increase in exploit-driven attacks, further reinforcing the critical necessity of timely security patch deployment. Additionally, organizations must prioritize vulnerability management and implement effective defensive technologies to mitigate the risks associated with potential exploitation.

To ensure the rapid detection of threats involving exploit utilization and to prevent their escalation, it is essential to deploy a reliable security solution. Key features of such a tool include continuous infrastructure monitoring, proactive protection, and vulnerability prioritization based on real-world relevance. These mechanisms are integrated into Kaspersky Next, which also provides endpoint security and protection against cyberattacks of any complexity.

The post Alert: Social Engineering Campaign Targets Open Source Developers via Slack appeared first on Daily CyberSecurity.

A significant proportion of cyberincidents are linked to supply chain attacks, and this proportion is constantly growing. Over the past year, we have seen a wide variety of methods used in such attacks, ranging from creation of malicious but seemingly legitimate open-source libraries or delayed attacks in such seemingly legitimate libraries, to the simplest yet most effective method: compromising the accounts of popular library owners to subsequently release malicious versions of their libraries. Such libraries are used by developers everywhere and are included in many solutions and services. The consequences of an attack can vary widely, ranging from delivering malware to a developer’s device to compromising an entire infrastructure if the malicious library has made its way into the code of a service or product.

This is exactly what happened in March 2026, when attackers injected malicious code into the popular Python library LiteLLM, which serves as a multifunctional gateway for a large set of AI agents. The attackers released two trojanized versions of LiteLLM that delivered malicious scripts to the victim’s system. Both versions made their way into the PyPI repository for Python. A technical analysis revealed that the attackers’ primary targets were servers storing confidential data related to AWS, Kubernetes, NPM, etc., as well as various databases (MySQL, PostgreSQL, MongoDB, etc.). In the latter case, the attackers were primarily interested in database configurations. In addition, the malware’s logic included functionality for stealing confidential data from crypto wallets and techniques for establishing a foothold in the Kubernetes cluster.

Repository compromise

The compromise affected the package distribution channel via PyPI: on March 24, 2026, malicious LiteLLM versions litellm==1.82.7 and litellm==1.82.8 were uploaded to the registry. It was specifically the package’s distribution contents that were compromised: in version 1.82.7, the malicious code was embedded in proxy_server.py, and in 1.82.8, the file litellm_init.pth was added.

Technical analysis

Both versions of the library contained the same malicious code, but its execution was implemented differently. In version 1.82.7, it was executed only when the proxy functionality was imported, while in 1.82.8, a .pth file was added that was able to execute the code every time the interpreter started.

Example of the malicious code in proxy_server.py

The malicious code in the proxy_server.py and litellm_init.pth files contained Python code encoded in Base64. After execution, the infected script saved this code alongside itself as a p.py file and immediately executed it.

The p.py script launched the main payload – another script, also encoded in Base64 – without saving it to disk. At the same time, it wrote the output of this payload to a file in the directory from which it was launched. Before being written, the output was encrypted using the AES-256-CBC algorithm with a random key generated in the code, which was saved to the session.key file. The key file was also encrypted – using a pre-initialized public RSA key.

The encrypted key and the output were combined into a tpcp.tar.gz archive and sent to the attackers’ remote server.

What exactly happened within the malicious payload whose output was sent to the C2 server? After it was launched, a recursive scan of the working directories on the victim’s system (/root, /app/, /var/www, etc.) began. In each directory, the script scanned the contents of files, which it output to the stdout buffer, from where it was then saved to the aforementioned file as the result. Next, the script collected system information and also saved it to the file. After that, it proceeded to search for sensitive data. It was interested in the following data located on servers and within the infrastructures of various services:

• SSH keys
• GIT accounts
• .env files
• AWS, Kubernetes, email service, database, and WireGuard configurations
• files related to Helm, Terraform, and CI
• TLS keys and certificates

A notable feature of this malware is that it does not limit itself to stealing files and configurations from the disk but also attempts to extract runtime secrets from the cloud infrastructure.

The code above uses the addresses 169.254.169.254 and 169.254.170.2. The first corresponds to the AWS Instance Metadata Service (IMDS), through which an EC2 instance (a virtual server in AWS, a machine running in the cloud) can retrieve metadata and temporary IAM role credentials (an AWS account with a set of permissions that a service or application can use to obtain temporary credentials for calls to the AWS API). The second is used in Amazon ECS to issue temporary credentials to a container during execution. Thus, the malicious script targets not only static secrets but also those issued by the cloud that can grant direct access to AWS resources at the time of infection.

Additionally, the script searches for crypto wallet configurations, as well as webhooks associated with Slack and Discord messengers. The latter indicates that the attackers are interested not only in infrastructure secrets and accounts, but also in communication channels within the development team.

In the next stage, the malware moves from data collection to establishing a foothold in the Kubernetes cluster infrastructure: if it has sufficient access, it configures a privileged pod (the smallest execution unit in Kubernetes, containing one or more containers) by enabling the securityContext.privileged=true option and mounts the node’s root filesystem via hostPath. This allows it to escape the container and perform actions at the node level.

Next, the malware executes another stage of infection: it saves a Base64-encoded script disguised as a legitimate system component to the Kubernetes node’s disk at the path /root/.config/sysmon/sysmon.py, and registers it via systemd. After launching, the script waits for an initial delay of 300 seconds, then begins periodically contacting the C2 node checkmarx[.]zone/raw, retrieving a link to the next payload from there. If the received value differs from the state previously saved in /tmp/.pg_state, the script downloads a new file to /tmp/pglog, makes it executable, and runs it in the background. At this stage, the attackers gain a foothold in the system and are capable of regularly delivering updated payloads without the need for re-injection. Since the malicious payload is written not to the container’s temporary file directory but directly to the Kubernetes cluster node, the attackers will retain access to the infrastructure even after the container has terminated.

A similar scenario is used for local persistence: in the absence of Kubernetes, the sysmon.py script is deployed in the user’s directory at ~/.config/sysmon/sysmon.py and is also registered as a service via systemd.

OpenVSX version of the malware

While analyzing files communicating with the C2 server, we discovered malicious versions of two common Checkmarx software extensions: ast-results 2.53.0 and cx-dev-assist 1.7.0. Checkmarx is used for application security assessment. These trojanized extensions contained malicious code that delivered the NodeJS version of the malware described above.

This version is downloaded from checkmarx[.]zone/static/checkmarx-util-1.0.4.tgz using NodeJS package installation utilities and is named checkmarx-util. Its key difference from the Python version is that it does not attempt to elevate privileges to the Kubernetes node level and does not create a privileged pod for persistence. Instead, it implements local persistence within the current environment. This means that the NodeJS variant persists only where it is already running.

Additionally, the list of folders to search for and steal secrets from is significantly smaller in this version than in the Python variant.

Checkmarx extensions are used to scan code and infrastructure configurations, so their compromise is quite dangerous: an attacker gains access not only to project files but also to a significant portion of the development environment, tokens, and local configurations.

Victimology

While assessing the attack’s impact, we saw victims all over the world. Most infection attempts occurred in Russia, China, Brazil, the Netherlands, and UAE.

Conclusion

As the technical analysis shows, the malicious scripts found in the LiteLLM versions are dangerous not only because they steal files containing sensitive data, but also because they target multiple critical infrastructure components simultaneously: the local system, cloud runtime secrets, the Kubernetes cluster, and even cryptographic keys. Such a broad scope of data collection allows an attacker to quickly move from compromising a single system and Python environment to seizing service accounts, secrets, and entire infrastructures.

Prevention and protection

To protect against infections of this kind, we recommend using a specialized solution for monitoring open-source components. Kaspersky provides real-time data feeds on compromised packages and libraries, which can be used to secure the supply chain and protect development projects from such threats.

Home security solutions, such as Kaspersky Premium, help ensure the security of personal devices by providing multi-layered protection that prevents and neutralizes infection threats. Additionally, our solution can restore the device’s functionality in the event of a malware infection.

To protect corporate devices, we recommend using a complex solution such as Kaspersky NEXT, which allows you to build a flexible and effective security system. The products in this line provide threat visibility and real-time protection, as well as EDR and XDR capabilities for threat investigation and response.

At the time of writing, the compromised versions of LiteLLM had already been removed from PyPI and OpenVSX. If you have used them, and as a proactive response to the threat, we recommend taking the following measures on your systems and infrastructure:

• Perform a full system scan using a reliable security solution.
• Rotate all potentially compromised credentials: API keys, environment variables, SSH keys, Kubernetes service account tokens, and other secrets.
• Check hosts and clusters for signs of compromise: the presence of ~/.config/sysmon/sysmon.py files and suspicious pods in Kubernetes.
• Clear the cache and conduct an inventory of PyPI modules: check for malicious ones and roll back to clean versions.
• Check for indicators of compromise (files on the system or network signs).

Indicators of Compromise:

URLs
models[.]litellm[.]cloud
checkmarx[.]zone

Infected packages
85ED77A21B88CAE721F369FA6B7BBBA3
2E3A4412A7A487B32C5715167C755D08
0FCCC8E3A03896F45726203074AE225D

Scripts
F5560871F6002982A6A2CC0B3EE739F7
CDE4951BEE7E28AC8A29D33D34A41AE5
05BACBE163EF0393C2416CBD05E45E74

Attackers are using fake Claude Code install pages and malicious search ads to spread infostealer malware targeting Windows and macOS systems.

The post Fake Claude Code Spreads Malware to Windows, macOS Users appeared first on TechRepublic.

Introduction

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

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

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.

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

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

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.

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.

Indicators of Compromise

Additional IoCs are available to customers of our Threat Intelligence Reporting service. For more details, contact us at crimewareintel@kaspersky.com.

File hashes
752e3eb5a9c295ee285205fb39b67fc4
c1e4be64f80bc019651f84ef852dfa6c
a8eeda4ae7db3357ed2ee0d94b963eff
c0c04df98b7d1ca9e8c08dd1ffbdd16b
88487ab7a666081721e1dd1999fb9fb2
d42ba771541893eb047a0e835bd4f84e
5f71b83ca752cb128b67dbb1832205a4
208fa7e01f72a50334f3d7607f6b82bf
e27edcdeb44522a9036f5e4cd23f1f0c
ea50282fa1269836a7e87eddb10f95f7
643696a052ea1963e24cfb0531169477
f5765930205719c2ac9d2e26c3b03d8d
576de7a075637122f47d02d4288e3dd6
7888eb4f51413d9382e2b992b667d9f5
3283f8c54a3ddf0bc0d4111cc1f950c0

Domains and IPs
arkanix[.]pw
arkanix[.]ru

All statistics in this report come from Kaspersky Security Network (KSN), a global cloud service that receives information from components in our security solutions voluntarily provided by Kaspersky users. Millions of Kaspersky users around the globe assist us in collecting information about malicious activity. The statistics in this report cover the period from November 2024 through October 2025. The report doesn’t cover mobile statistics, which we will share in our annual mobile malware report.

During the reporting period:

• 48% of Windows users and 29% of macOS users encountered cyberthreats
• 27% of all Kaspersky users encountered web threats, and 33% users were affected by on-device threats
• The highest share of users affected by web threats was in CIS (34%), and local threats were most often detected in Africa (41%)
• Kaspersky solutions prevented nearly 1,6 times more password stealer attacks than in the previous year
• In APAC password stealer detections saw a 132% surge compared to the previous year
• Kaspersky solutions detected 1,5 times more spyware attacks than in the previous year

To find more yearly statistics on cyberthreats view the full report.

IT threat evolution in Q3 2025. Mobile statistics
IT threat evolution in Q3 2025. Non-mobile statistics

Quarterly figures

In Q3 2025:

• Kaspersky solutions blocked more than 389 million attacks that originated with various online resources.
• Web Anti-Virus responded to 52 million unique links.
• File Anti-Virus blocked more than 21 million malicious and potentially unwanted objects.
• 2,200 new ransomware variants were detected.
• Nearly 85,000 users experienced ransomware attacks.
• 15% of all ransomware victims whose data was published on threat actors’ data leak sites (DLSs) were victims of Qilin.
• More than 254,000 users were targeted by miners.

Ransomware

Quarterly trends and highlights

Law enforcement success

The UK’s National Crime Agency (NCA) arrested the first suspect in connection with a ransomware attack that caused disruptions at numerous European airports in September 2025. Details of the arrest have not been published as the investigation remains ongoing. According to security researcher Kevin Beaumont, the attack employed the HardBit ransomware, which he described as primitive and lacking its own data leak site.

The U.S. Department of Justice filed charges against the administrator of the LockerGoga, MegaCortex and Nefilim ransomware gangs. His attacks caused millions of dollars in damage, putting him on wanted lists for both the FBI and the European Union.

U.S. authorities seized over $2.8 million in cryptocurrency, $70,000 in cash, and a luxury vehicle from a suspect allegedly involved in distributing the Zeppelin ransomware. The criminal scheme involved data theft, file encryption, and extortion, with numerous organizations worldwide falling victim.

A coordinated international operation conducted by the FBI, Homeland Security Investigations (HSI), the U.S. Internal Revenue Service (IRS), and law enforcement agencies from several other countries successfully dismantled the infrastructure of the BlackSuit ransomware. The operation resulted in the seizure of four servers, nine domains, and $1.09 million in cryptocurrency. The objective of the operation was to destabilize the malware ecosystem and protect critical U.S. infrastructure.

Vulnerabilities and attacks

SSL VPN attacks on SonicWall

Since late July, researchers have recorded a rise in attacks by the Akira threat actor targeting SonicWall firewalls supporting SSL VPN. SonicWall has linked these incidents to the already-patched vulnerability CVE-2024-40766, which allows unauthorized users to gain access to system resources. Attackers exploited the vulnerability to steal credentials, subsequently using them to access devices, even those that had been patched. Furthermore, the attackers were able to bypass multi-factor authentication enabled on the devices. SonicWall urges customers to reset all passwords and update their SonicOS firmware.

Scattered Spider uses social engineering to breach VMware ESXi

The Scattered Spider (UNC3944) group is attacking VMware virtual environments. The attackers contact IT support posing as company employees and request to reset their Active Directory password. Once access to vCenter is obtained, the threat actors enable SSH on the ESXi servers, extract the NTDS.dit database, and, in the final phase of the attack, deploy ransomware to encrypt all virtual machines.

Exploitation of a Microsoft SharePoint vulnerability

In late July, researchers uncovered attacks on SharePoint servers that exploited the ToolShell vulnerability chain. In the course of investigating this campaign, which affected over 140 organizations globally, researchers discovered the 4L4MD4R ransomware based on Mauri870 code. The malware is written in Go and packed using the UPX compressor. It demands a ransom of 0.005 BTC.

The application of AI in ransomware development

A UK-based threat actor used Claude to create and launch a ransomware-as-a-service (RaaS) platform. The AI was responsible for writing the code, which included advanced features such as anti-EDR techniques, encryption using ChaCha20 and RSA algorithms, shadow copy deletion, and network file encryption.

Anthropic noted that the attacker was almost entirely dependent on Claude, as they lacked the necessary technical knowledge to provide technical support to their own clients. The threat actor sold the completed malware kits on the dark web for $400–$1,200.

Researchers also discovered a new ransomware strain, dubbed PromptLock, that utilizes an LLM directly during attacks. The malware is written in Go. It uses hardcoded prompts to dynamically generate Lua scripts for data theft and encryption across Windows, macOS and Linux systems. For encryption, it employs the SPECK-128 algorithm, which is rarely used by ransomware groups.

Subsequently, scientists from the NYU Tandon School of Engineering traced back the likely origins of PromptLock to their own educational project, Ransomware 3.0, which they detailed in a prior publication.

The most prolific groups

This section highlights the most prolific ransomware gangs by number of victims added to each group’s DLS. As in the previous quarter, Qilin leads by this metric. Its share grew by 1.89 percentage points (p.p.) to reach 14.96%. The Clop ransomware showed reduced activity, while the share of Akira (10.02%) slightly increased. The INC Ransom group, active since 2023, rose to third place with 8.15%.

Number of each group’s victims according to its DLS as a percentage of all groups’ victims published on all the DLSs under review during the reporting period (download)

Number of new variants

In the third quarter, Kaspersky solutions detected four new families and 2,259 new ransomware modifications, nearly one-third more than in Q2 2025 and slightly more than in Q3 2024.

Number of new ransomware modifications, Q3 2024 — Q3 2025 (download)

Number of users attacked by ransomware Trojans

During the reporting period, our solutions protected 84,903 unique users from ransomware. Ransomware activity was highest in July, while August proved to be the quietest month.

Number of unique users attacked by ransomware Trojans, Q3 2025 (download)

Attack geography

TOP 10 countries attacked by ransomware Trojans

In the third quarter, Israel had the highest share (1.42%) of attacked users. Most of the ransomware in that country was detected in August via behavioral analysis.

* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by ransomware Trojans as a percentage of all unique users of Kaspersky products in the country/territory.

TOP 10 most common families of ransomware Trojans

* Unique Kaspersky users attacked by the specific ransomware Trojan family as a percentage of all unique users attacked by this type of threat.

Miners

Number of new variants

In Q3 2025, Kaspersky solutions detected 2,863 new modifications of miners.

Number of new miner modifications, Q3 2025 (download)

Number of users attacked by miners

During the third quarter, we detected attacks using miner programs on the computers of 254,414 unique Kaspersky users worldwide.

Number of unique users attacked by miners, Q3 2025 (download)

Attack geography

TOP 10 countries and territories attacked by miners

* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by miners as a percentage of all unique users of Kaspersky products in the country/territory.

Attacks on macOS

In April, researchers at Iru (formerly Kandji) reported the discovery of a new spyware family, PasivRobber. We observed the development of this family throughout the third quarter. Its new modifications introduced additional executable modules that were absent in previous versions. Furthermore, the attackers began employing obfuscation techniques in an attempt to hinder sample detection.

In July, we reported on a cryptostealer distributed through fake extensions for the Cursor AI development environment, which is based on Visual Studio Code. At that time, the malicious JavaScript (JS) script downloaded a payload in the form of the ScreenConnect remote access utility. This utility was then used to download cryptocurrency-stealing VBS scripts onto the victim’s device. Later, researcher Michael Bocanegra reported on new fake VS Code extensions that also executed malicious JS code. This time, the code downloaded a malicious macOS payload: a Rust-based loader. This loader then delivered a backdoor to the victim’s device, presumably also aimed at cryptocurrency theft. The backdoor supported the loading of additional modules to collect data about the victim’s machine. The Rust downloader was analyzed in detail by researchers at Iru.

In September, researchers at Jamf reported the discovery of a previously unknown version of the modular backdoor ChillyHell, first described in 2023. Notably, the Trojan’s executable files were signed with a valid developer certificate at the time of discovery.

The new sample had been available on Dropbox since 2021. In addition to its backdoor functionality, it also contains a module responsible for bruteforcing passwords of existing system users.

By the end of the third quarter, researchers at Microsoft reported new versions of the XCSSET spyware, which targets developers and spreads through infected Xcode projects. These new versions incorporated additional modules for data theft and system persistence.

TOP 20 threats to macOS

Unique users* who encountered this malware as a percentage of all attacked users of Kaspersky security solutions for macOS (download)

* Data for the previous quarter may differ slightly from previously published data due to some verdicts being retrospectively revised.

The PasivRobber spyware continues to increase its activity, with its modifications occupying the top spots in the list of the most widespread macOS malware varieties. Other highly active threats include Amos Trojans, which steal passwords and cryptocurrency wallet data, and various adware. The Backdoor.OSX.Agent.l family, which took thirteenth place, represents a variation on the well-known open-source malware, Mettle.

Geography of threats to macOS

TOP 10 countries and territories by share of attacked users

IoT threat statistics

This section presents statistics on attacks targeting Kaspersky IoT honeypots. The geographic data on attack sources is based on the IP addresses of attacking devices.

In Q3 2025, there was a slight increase in the share of devices attacking Kaspersky honeypots via the SSH protocol.

Distribution of attacked services by number of unique IP addresses of attacking devices (download)

Conversely, the share of attacks using the SSH protocol slightly decreased.

Distribution of attackers’ sessions in Kaspersky honeypots (download)

TOP 10 threats delivered to IoT devices

Share of each threat delivered to an infected device as a result of a successful attack, out of the total number of threats delivered (download)

In the third quarter, the shares of the NyaDrop and Mirai.b botnets significantly decreased in the overall volume of IoT threats. Conversely, the activity of several other members of the Mirai family, as well as the Gafgyt botnet, increased. As is typical, various Mirai variants occupy the majority of the list of the most widespread malware strains.

Attacks on IoT honeypots

Germany and the United States continue to lead in the distribution of attacks via the SSH protocol. The share of attacks originating from Panama and Iran also saw a slight increase.

The largest number of attacks via the Telnet protocol were carried out from China, as is typically the case. Devices located in India reduced their activity, whereas the share of attacks from Indonesia increased.

Attacks via web resources

The statistics in this section are based on detection verdicts by Web Anti-Virus, which protects users when suspicious objects are downloaded from malicious or infected web pages. These malicious pages are purposefully created by cybercriminals. Websites that host user-generated content, such as message boards, as well as compromised legitimate sites, can become infected.

TOP 10 countries that served as sources of web-based attacks

This section gives the geographical distribution of sources of online attacks (such as web pages redirecting to exploits, sites hosting exploits and other malware, and botnet C2 centers) blocked by Kaspersky products. One or more web-based attacks could originate from each unique host.

To determine the geographic source of web attacks, we matched the domain name with the real IP address where the domain is hosted, then identified the geographic location of that IP address (GeoIP).

In the third quarter of 2025, Kaspersky solutions blocked 389,755,481 attacks from internet resources worldwide. Web Anti-Virus was triggered by 51,886,619 unique URLs.

Web-based attacks by country, Q3 2025 (download)

Countries and territories where users faced the greatest risk of online infection

To assess the risk of malware infection via the internet for users’ computers in different countries and territories, we calculated the share of Kaspersky users in each location on whose computers Web Anti-Virus was triggered during the reporting period. The resulting data provides an indication of the aggressiveness of the environment in which computers operate in different countries and territories.

This ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out Web Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.

* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users targeted by web-based Malware attacks as a percentage of all unique users of Kaspersky products in the country/territory.
On average, over the course of the quarter, 4.88% of devices globally were subjected to at least one web-based Malware attack.

Local threats

Statistics on local infections of user computers are an important indicator. They include objects that penetrated the target computer by infecting files or removable media, or initially made their way onto the computer in non-open form. Examples of the latter are programs in complex installers and encrypted files.

Data in this section is based on analyzing statistics produced by anti-virus scans of files on the hard drive at the moment they were created or accessed, and the results of scanning removable storage media: flash drives, camera memory cards, phones, and external drives. The statistics are based on detection verdicts from the on-access scan (OAS) and on-demand scan (ODS) modules of File Anti-Virus.

In the third quarter of 2025, our File Anti-Virus recorded 21,356,075 malicious and potentially unwanted objects.

Countries and territories where users faced the highest risk of local infection

For each country and territory, we calculated the percentage of Kaspersky users on whose computers File Anti-Virus was triggered during the reporting period. This statistic reflects the level of personal computer infection in different countries and territories around the world.

Note that this ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out File Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.

* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users on whose computers local Malware threats were blocked, as a percentage of all unique users of Kaspersky products in the country/territory.
On average worldwide, local Malware threats were detected at least once on 12.36% of computers during the third quarter.