Fake ChatGPT and Claude apps spread Yoba malware through GitHub, using signed executables to load a cryptominer on…
Source: HackRead · 2026-11-05
Tag: Claude
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Yoba Malware Hides a Crypto Miner Behind Legitimate OpenAI and Anthropic Binaries
Yoba malware uses fake ChatGPT and Claude apps to sideload malware through signed installers and deploy a cryptocurrency miner.
Check Point Research has spent two months tracking a campaign it calls Yoba, which uses the popularity of AI tools to spread a custom malware chain. The attackers build fake GitHub projects that pose as AI desktop applications such as GPT-5.6 Sol, GPT-6 Astra and Claude Opus. The projects look convincing and run on real GitHub infrastructure, which helps them seem legitimate to potential victims.
“These projects look convincing and use real GitHub infrastructure, which helps them appear legitimate to potential victims. The website exhibits search-targeted content and suspicious installer-delivery tactics consistent with an SEO-poisoning lure.
Inside the installers, there is a malicious component we refer to as the YobaDropper. This dropper includes another component, the YobaLoader, which is responsible for contacting attacker-controlled infrastructure and fetching the final payload.” reads the report. “In this report, we describe how victims are lured to these fake projects, how the YobaDropper and YobaLoader work together, and how the attackers ultimately use this chain to run unwanted software on infected systems.”
The lure is simple. Each project has a repository, a GitHub.io page describing the software, and a ZIP release with the malware. The repositories show no fake stars or forks, yet they still rank among the top Google results, most likely through SEO poisoning that optimizes titles, descriptions and hosting domains.
The first repository appeared around July 2026, but the first malicious releases landed only on August 30 and 31, and a third followed about five days later on September 4. Each repository uses a different Outlook address, and commit times point to UTC+4, UTC-4 and UTC+3, which Check Point reads as a deliberate attempt to hide the actor’s real time zone.
Here’s the trick that gives the report its title. Each ZIP holds a legitimate signed executable from OpenAI or Anthropic, named Setup.exe, next to a malicious winmm.dll. The signed program loads the DLL on its own, a technique known as DLL sideloading, which is often used to bypass security protections.
The ZIP files were not even password-protected. Attackers often use passwords to prevent security tools from automatically unpacking and scanning files. However, these binaries already had very low detection rates because they used anti-sandbox and other techniques to avoid analysis. The DLL’s compilation date is another clue: it was built on the same day it was added to the archive, unlike the legitimate files around it.
The dropper, called YobaDropper, checks the environment before running. It calculates a hash of the name of the program that launched it and continues only if the name is Setup.exe. This means that sandboxes that run the DLL on its own will fail this check. When that happens, the process does not exit. Instead, it sleeps 200,000 times for 5.5 seconds each, a total of about 12.73 days.
The malware also avoids victims in CIS countries by checking the keyboard layout. It excludes Russian, Belarusian, Tajik, Armenian, Azerbaijani, Georgian, Kazakh, Kyrgyz, Turkmen, Uzbek, and Tatar. Interestingly, Ukraine, which has often appeared in similar exclusion lists, is not included.
Next, the malware requests administrator privileges. If the user does not have them, it displays the message “Please run this program as Administrator.” and exits. The victim must then restart it with administrator rights.
Once it has the required privileges, the malware creates a folder called WMProcess under ProgramData. It copies the legitimate WMPDMC.exe file from System32 into this folder and places the second-stage payload there under the name wmpdui.dll.
The malicious code achieves persistence by using a scheduled task dressed up as Microsoft. The author is set to Microsoft Corporation, the description says it collects device metadata for Windows Media Player, and it starts three minutes after boot as Local System. The task launches the legitimate program, which sideloads the malicious DLL.
The second stage, YobaLoader, shares code with the dropper, which points to the same developer. It only runs under WMPDMC.exe and uses a different seed for its hashing than the dropper does, which separates the stages and weakens hash-based detection. Its job is to find its payload without a classic command server.
It goes to a Telegram channel, t[.]me/yoba_fanclub, and downloads the channel’s profile picture. The last 50 bytes of the image seed a domain generator that builds names from word lists in the form adjective-noun-verb-number on dedyn[.]io. Change the picture and the operator gets a new domain.
Then it asks Google’s DNS-over-HTTPS service for a TXT record on that domain. The reply is signed, and the loader checks the signature before decrypting it, which gives a link to a payload on Catbox. Check Point says this reliance on DoH and common hosting aims to bypass passive DNS monitoring and some gateway protections, and the campaign avoids dedicated command-and-control infrastructure, using Telegram, Tor, Catbox and DoH instead.
The operator can choose how the payload runs. It can inject it into svchost.exe, save it as an executable after removing the Zone.Identifier mark, or run it as a hidden PowerShell script while bypassing execution policies.
“Finally, the injected payload into
svchost.exeis an XMRig 6.26.0 miner with a hidden command line targeting a TLS-enabled onion mining endpoint through a local Tor SOCKS5 proxy. It can unpack and start Tor 0.4.9.12 plus lyrebird 0.8.1, monitor user-input activity to pause mining, and conditionally load the WinRing0 driver service for MSR-based mining optimization.” continnues the report.The miner uses Tor to connect to an encrypted mining server. It can start its own Tor components, use about 85% of CPU threads, and pause mining when the user is active. It resumes after 240 seconds of inactivity and can load the WinRing0 driver to optimize mining performance.
However, YobaLoader is not limited to cryptocurrency mining. Its DNS tasking system allows operators to deliver any PE executable or PowerShell payload, meaning future attacks could distribute other malware families.
Earlier versions appeared in June 2026. They used Cursor.exe for sideloading, a startup shortcut for persistence, and excluded only Russia. They also used .aber[.]ir instead of dedyn[.]io and included anti-debugging and anti-VM checks that later versions dropped.
Check Point believes the attackers may have Russian origins and mainly operate in the UTC-4 time zone, but this is an assessment, not a confirmed attribution.
Defenders should look for suspicious WMPDMC.exe copies under ProgramData, fake Microsoft scheduled tasks, Telegram-related traffic, DNS lookups for dedyn[.]io through dns.google, and unexplained high CPU usage.
“The Yoba campaign shows how attackers can use trusted platforms and the current interest in AI tools to distribute malware in a way that looks believable and harmless at first glance. By packaging their code together with well-known software names and hosting it on GitHub, they lower the chances that users or basic security checks will immediately flag the files as suspicious.” concludes the report. “Organizations should be cautious about installing “AI helpers” or similar tools from unverified repositories and should enforce policies that restrict running software from unknown sources. Combining user awareness with basic technical controls.”
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(SecurityAffairs – hacking, Yoba malware)
Source: Security Affairs · 2026-10-30
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Deploying AI agents securely: how Kaspersky products mitigate AI threats
Introduction
AI agents and associated tooling are becoming increasingly common in corporate infrastructure. Agents can perform a range of actions with the user’s privileges, such as accessing data, running commands, and interacting with external services. This creates new security risks, especially when these tools are used without the security team’s oversight or well-designed policies.
Detecting, tracking, and controlling AI agents is becoming one of the most pressing tasks for security teams today. We closely follow technological developments and continuously update our solutions to respond to new threats in a timely manner amid a rapidly changing technology landscape. This post describes new expertise built into Kaspersky products that helps counter the threats posed by the proliferation of AI agents. We approach this problem from several angles.
First, you need to thoroughly understand the threat you are dealing with. To this end, we added profiles to the Kaspersky Threat Intelligence Portal that describe various AI agents and how attackers exploit them. At their core, AI agents are programs (processes) that run in the operating system on endpoints and can also launch other processes. This means that Endpoint Detection and Response solutions are among the key endpoint controls for this threat. These solutions detect the presence and launch of AI agents, track their activity, and identify potentially dangerous behavior. When needed, they also take action, such as stopping a suspicious process, blocking the activity, or isolating the host, to reduce the likelihood of an incident escalating. For this purpose, we developed specially adapted detection rules.
You can use Kaspersky Endpoint Security, now also available as Kaspersky Next, to control and restrict the launch of AI agents. This solution uses two components to detect AI agents and control how they are used. Application Control categorizes the application processes launched on endpoints, and Web Control manages access to specific categories of web resources for users on endpoints.
Finally, even harmless AI agents can contain malicious instructions (skills), such as those downloaded from unsafe external sources. Kaspersky Scan Engine analyzes installed applications to detect these threats and also checks LLM files for malicious code. Let’s take a closer look at these solutions and technologies.
Popular AI applications and services in organizations
Tools from Anthropic and OpenAI take the top spots in the September 2026 ranking of AI agents by popularity among Kaspersky users. This list is based on telemetry from Kaspersky solutions. It shows the number of users who had the relevant AI vendors’ products installed at the time of the study.
# AI product 1 Claude 2 Claude Code 3 Codex 4 OpenCode 5 Zed 6 Wispr Flow 7 LM Studio 8 ZCode 9 Chatbox 10 Cherry Studio The data shows that agentic systems, especially AI agents for software development, are the most popular AI applications. The TOP 10 also includes an application for running large language models locally (LM Studio) and a voice note-taking app (Wispr Flow).
According to open sources, these are the most widely used AI services in 2026:
# AI service 1 ChatGPT 2 Google Gemini 3 Claude 4 Grammarly 5 DeepSeek While analyzing the most popular and widely used apps, our experts also noticed a sharp rise in vulnerable versions of AI-related software and services. From 2025 to date, more than 2500 vulnerabilities have been published:
Number of published vulnerabilities in LLMs, AI tools, and plugins with similar functionality, 2025–2026 (download)
* Data for previous quarters was updated and may differ slightly from previously published data.
Application Control and Web Control
To enforce the organization’s security policy for AI agents and other AI applications on the corporate network, Kaspersky products offer application control based on KL categories. These categories classify installed software and give you granular control over which approved applications can run in the organization.
Application Control analyzes the processes launched on endpoints, tracking which AI agents are running in corporate systems at any given moment, on which hosts, and under which accounts. Application Control also shows how long a given program has been in use.
This makes it easier for the Kaspersky Security Center (KSC) administrator to control applications. Administrators can use KL categories to allow or block AI agents inside the company for specific groups of employees.
Here is what this functionality looks like to KSC administrators:
KL categories available when configuring Application Control
For details on how to configure Application Control in KSC, follow this link.
In Kaspersky Endpoint Security (KES), Application Control can operate in two modes:
- Denylist. In this mode, Application Control lets users run any applications except those blocked by Application Control rules. This is the default mode.
- Allowlist. In this mode, Application Control blocks users from running any applications except those explicitly allowed by Application Control rules.
Below are the steps to configure Application Control to block AI tools.
Configuring Application Control for the AI Tools category in KES
With this setup, when a user tries to launch an AI agent, they get a notification and the agent doesn’t start.
KES notification about an attempt to launch an AI agent
For more on how to configure Application Control in KES, follow this link. Our public portal also lets you explore information on KL categories and the applications that fall under them, in particular details on how AI agents are categorized and how popular they are.
AI Tools KL category
The View applications drop-down list shows products for a specific vendor.
Similarly, Web Control lets you manage user access to web resources. If a user tries to open a denylisted website, KES blocks access and shows a warning.
KES notification about an attempt to access a prohibited web resource
When configuring access, the administrator can block websites that provide AI as a service.
Website categories
For more on configuring Web Control, follow this link. The help portal also provides information on website categories that can be assigned to resources to configure access control.
Kaspersky Endpoint Detection and Response
To an EDR solution, an AI agent is a regular process. It has its own executable, a tree of child processes, and network communications, all of which need to be monitored for suspicious activity. An attacker can use an AI agent as an automation tool. An AI agent installed as a legitimate user tool can also be leveraged in a ClickFix attack, where it executes a malicious instruction received from an untrusted source, such as a document, web page, or email. That said, unsafe actions by an agent do not always indicate an attack. There may be no malicious intent in the chain at all. An incident can result from what looks like an ordinary instruction from a user who doesn’t fully understand the consequences of having it executed. The mere fact that an agent was launched does not reveal whether it is being used in an attack. That is why Kaspersky EDR (KEDR) uses both conventional behavioral rules and rules written and adapted to detect AI tools. The latter identify risky agent actions and settings, and help determine which AI tool was involved in suspicious activity.
Conventional KEDR rules detect the vast majority of familiar malicious techniques, including when performed by an AI agent. A suspicious PowerShell launch, persistence via Task Scheduler, or an attempt to steal credentials leaves clear, identifiable traces. These traces look the same no matter what initiated the actions. The rules analyze launch parameters, process relationships, affected files, network activity, and other telemetry events.
For example, in a recent Armored Likho campaign, we found signs that AI had been used to create loader and dropper samples, which were later compiled with NSIS (Nullsoft Scriptable Install System). The key telltale artifacts were icons in the scripts and excessive comments on every function, which is uncharacteristic of human-written code. In addition, the group continues to generate new modules for its BusySnake Stealer and BusySnake RAT samples. For example, the attackers used AI to develop a module that runs multiple checks for virtual environment artifacts and calculates a score. Depending on the score, the sample either runs or exits with code 0. Even though the initial access samples, payloads, and their modules were AI-generated, the commands they execute via rundll32 and PowerShell are detected by the shell_creation_by_rundll32 and suspicious_powershell_cmd_or_script_spawning rules.
So, for detecting an attack like this, it is not important who wrote the loader, the payload, or its modules: they are given away by the commands they execute, process relationships, and other traces of activity in the system. AI can be involved both in preparing the code and in carrying out the attack directly on the device.
In the latter case, the attacker uses the agent to carry out their tasks and lets it choose and run the required commands within the privileges it has. For effective protection, it is critical to link suspicious actions back to the AI tool and the conditions under which it operates. In KEDR, several specialized rules handle this task.
We have created separate rules for popular AI agents, ai_agent_*_access_to_sensitive_file_**, where * is the tool name and ** is the operating system. They generate alerts when an agent accesses files that contain credentials, tokens, or keys.
The ai_agent_dangerous_execution_mode rule detects agents launched with auto-approval of all actions (so-called YOLO mode) or with the sandbox disabled. These settings do not indicate an attack on their own, but they let the agent perform dangerous actions without user confirmation.
The suspicious_mcp_server_artifact rule triggers when MCP server scripts are created or modified in hidden, temporary, or cache directories. Attackers can place an MCP server in these locations to quietly connect it to the agent: through the MCP server, they can pass instructions to the agent, call third-party tools, and access the user’s data.
The llm_api_access_by_unusual_apps rule generates an alert when an application on Windows, Linux, or macOS connects to the API of a known external LLM service, unless this activity falls under predefined exclusions. This helps spot model access by applications that do not normally behave this way.
Some Kaspersky EDR rules tag events for investigation but do not generate alerts. For example, the
ai_agent_**_executionrules flag launches of known agents on Windows, Linux, and macOS. The local_ai_model_artifact rule records the creation or modification of AI model files in typical application, runtime, and cache directories. These events reveal AI tools on the device, including tools that run on local models and never call an external API.Kaspersky Threat Intelligence Portal
AI agent profiles
The Kaspersky Threat Intelligence Portal offers a large number of software profiles, including profiles of AI agents. You can sort them by the
ai_prefix:
AI agent profiles on Kaspersky TIP
The profile of an AI agent such as Claude Desktop includes a detailed description of the software, the privilege level required to run it, supported platforms, and threat actor groups known to use it. It also lists tactics and techniques mapped to the MITRE ATT&CK framework, with descriptions of the procedures the tool can be used for.
Claude profile on Kaspersky TIP
The description also lists ways to detect the software through standard system auditing mechanisms:
- Windows: events from standard audit logs and Sysmon events
- Linux: syscall audit events and Sysmon events
- Network auditing: a set of protocols and the traffic generated by the software
The detection section lists the main artifacts the software leaves in the system: command lines, possible launch parameters, names of the services and scheduled tasks it creates, and the key network artifacts.
Detection section in the Claude profile
Hunt Hub
In December 2025, the Kaspersky Threat Intelligence Portal added Hunt Hub (HH), a repository of detection rules. This is a unique Kaspersky project, and we developed it in response to growing market demand.
This functionality is available in the Threat Landscape section of the portal. It gives analysts access to detection rules: their descriptions and their mapping to MITRE ATT&CK tactics and techniques, as well as to the associated cybercriminal groups.
In the Hunt Hub section, customers can explore the detection logic of EDR, NDR, and NGFW products, including the logic for detecting AI agents.
Rules in the Hunt Hub repository
The rule card provides:
- Detailed description
- Rule’s severity level, confidence level, and alert format
- Detection tips
- Description of possible false positives
- Platforms the rule can run on
- Tactics, techniques, and sub-techniques covered by the rule
- Groups that use the technique covered by the rule
- Detection logic that shows what exactly triggers the rule
Detection rule card in Hunt Hub
Kaspersky Scan Engine
Skills are a key component of the AI agent ecosystem. They’re prewritten sets of natural-language instructions that extend agent functionality. We previously covered the risks that malicious skills pose to agent users. A skill can require the usage of additional programs and services, come with helper scripts (for example, written in Python), or contain a malicious command embedded right in the instruction text. This means that adding skills to an originally harmless AI agent opens up several new ways for malicious code to enter the system. To prevent this, any skills obtained from external sources must be scanned. Kaspersky Scan Engine is well suited for this task. This is a cyberthreat detection and response solution that seamlessly integrates with various applications. It can detect malicious code in agent skills, associated scripts, and in files generated by running agents.
Detection of a malicious agent skill in Kaspersky Scan Engine
Trained LLMs come in many formats. Some of them (PTH, PT, PKL, KERS, and others) also store parameters and weights (training datasets). These files can also contain malicious code, so it’s critical to scan them before running and using them. Kaspersky Scan Engine scans potentially dangerous LLM formats for infected data. It can deserialize and analyze models to detect malicious code regardless of model size.
Detection of an LLM with malicious code in Kaspersky Scan Engine
Conclusion
AI agents are rapidly making their way into corporate environments: they help employees work faster and take on a wide range of tasks, but they also open up new opportunities for attackers. To take advantage of this technology without compromising security, organizations need to build multi-layered protection and minimize risks. A range of Kaspersky solutions can help with this:
- Kaspersky TIP keeps you up to date on current scenarios in which attackers use AI agents, and its Hunt Hub section provides rules for detecting suspicious AI agent activity.
- Kaspersky EDR detects AI agents in the infrastructure and monitors their activity on endpoints. If it detects potentially dangerous actions, the product can log the event, notify the SOC, and take response actions, such as stopping the process or isolating the host.
- Application Control in Kaspersky products can fully restrict the use of AI agents within the company by using application categorization. The Web Control component can restrict access to AIaaS web resources.
- Kaspersky Scan Engine can be integrated into various company processes to detect malicious code in any files, including AI agent skills and LLMs.
When companies combine the tools described above in a comprehensive approach, they can adopt AI agents in an informed way: with an understanding of the associated risks and effective means of minimizing them.
Source: Securelist (Kaspersky GReAT) · 2026-10-26
