The University of Copenhagen in Denmark confirmed that hackers accessed one of its test environments containing personal data belonging to 525,000 people. The educational organization detected the breach last Tuesday and disclosed it on Friday. In an update yesterday, the university added another 20,000 staff and students whose PIN codes were exposed. “The University of Copenhagen has confirmed a cyber attack in which hackers gained access to one of the university’s test environments,” the university says. Founded in 1479, the University of Copenhagen is Denmark’s oldest university and one of its leading research institutions. It has approximately 36,800 students and 10,700 employees, with six faculties and more than 200 research centers. The University of Copenhagen is still investigating to determine the extent of the incident, but warns that the following information may have been exposed:
Sansec tracks a Magento skimmer campaign that infected 953 stores, using five domains, bot filtering and malicious code hidden in CMS blocks. A criminal group has infected 953 online stores since July 2025, mostly Magento shops, according to Sansec researchers. The attackers add a small piece of code to store pages. When someone visits a shop, their browser contacts the attackers’ server to get further instructions. The server checks whether the visitor is a real shopper or a security scanner. If it detects a scanner, it hides the malicious activity. “deepveindrilling.com is the newest web address of a criminal group that breaks into online stores. The group adds a small piece of code to the store’s pages.” Sansec’s analysis states. “The group has done this to 953 stores since July 2025, mostly Magento shops. It moves to a new address every few months.” The group’s latest domain is deepveindrilling.com. Sansec found it in the CMS blocks of 83 stores, almost all running Magento. CMS blocks are content sections that Magento adds to store pages. The domain was registered on October 6, 2026, and the first stores began loading it the following evening. This is the fifth domain linked to a campaign that Sansec has tracked since July 2025 and reported in March as the PolyShell mass attack. Most stores using the latest domain had already been targeted. Of the 83 stores, 60 had previously loaded one of the group’s malicious scripts, and 34 had been doing so since October 2025. The domain sits behind Cloudflare, and its front page returns a 404. It only answers script requests from visitors of infected stores. The loader code lives in the cms_block table, and Sansec’s scanner, eComscan, found it there on 148 server installs over the campaign, never in core_config_data or theme files. eComscan first found the new loader at 21:54 UTC on October 7. By the end of October 8, Sansec’s crawler had found it on 74 stores, as two scripts. The first reads a value called fpCode from the visitor’s browser storage, and when the value is set, the script puts the stored code in an invisible div and clicks it. The browser then runs the code as an event handler, without using eval(). Nothing in the block writes fpCode, so the script from deepveindrilling.com has to set it. A visitor who received the payload once runs it again on every later page view, even when the domain is offline or blocked. The March version deleted its stored payload after one run. On October 23 the operator rewrote the blocks again, and the loader appeared on 9 more stores. All 71 stores Sansec re-crawled by October 24 run the new version. It replaces the plain script tag with an inline loader that sends a browser fingerprint to https://deepveindrilling.com/?s=49b7ca5f&fp=. The fingerprint flags automated browsers through navigator.webdriver, Selenium globals and ChromeDriver’s cdc_ properties. It also adds the GPU name from WebGL, CPU cores, memory, screen size, time zone, languages and client hints. The server picks its response from all of that. The filter works. When urlscan.io loaded an infected store with an automated Chrome browser, it got HTTP 200, and Sansec found no added content in the page. Sansec hasn’t captured the final payload yet. Older payloads give a hint of what real visitors get. Sansec Watch, the firm’s monitoring service for content security policies, shows what the earlier stage served. On December 12, 2025, a customer logged in to an infected store got a script from manulfie.click that loaded https://crypto-insider.top/ssl/ and, five minutes later, https://leqdger.click/ssl/. On December 15 and 16, neswnsssn.click inserted the same leqdger.click script on two more stores. leqdger.click is a misspelling of the crypto wallet brand Ledger. It was registered on September 12, 2025 and has since expired. Before October, the second script was the “Script (async)” integration of Keitaro, a commercial ad tracker that doubles as a traffic distribution system, a tool that sorts visitors. It sends the referrer, page title and query string to a base64 encoded URL such as https://additionalver.com/Mb2z8J?&se_referrer=, where the path is the Keitaro campaign alias. The system then decides per visitor whether to return a payload, a redirect or nothing. “When Sansec requested the additionalver.com script as a Windows desktop visitor on October 24, it returned QWE=123;, a statement that does nothing, and set a cookie that marks the visitor for 24 hours. deepveindrilling.com drops Keitaro and moves the bot filtering into the loader.” continues the report. Here are the five loader domains over 15 months: DomainRegisteredFirst seen on storesStoresmanulfie.clickbefore July 2025, re-registered 2026-05-232025-07-09863neswnsssn.clickbefore December 2025, re-registered 2026-05-222025-12-12494lanhd6549tdhse.top2026-03-252026-03-30355additionalver.com2026-06-072026-06-08246deepveindrilling.com2026-10-062026-10-0783 Two other domains were found on a small number of stores: hdfdsi84j3h.top, registered three minutes after lanhd6549tdhse.top, and c85grt98sd.com. Two older domains, neswnsssn.click and manulfie.click, expired and were registered again in May 2026. Both now use the nameservers of abovedomains.com, a domain parking service. Between August and October 2026, Sansec still found the manulfie.click loader on nine stores and the neswnsssn.click loader on six. When visitors open these stores, their browsers send information about the previous page and the current page title to the new domain owner. The browsers also run any script returned by that domain. Sansec Watch received reports of visitors’ browsers connecting to all four earlier domains. One domain, additionalver.com, was linked to around 52,000 reports. The October activity did not rely on a new vulnerability. Of the 83 affected stores, 60 had already been infected. Earlier attacks appear to have exploited file upload flaws in Magento. In March, the group injected its malicious code into 471 stores in just one hour by uploading webshells through PolyShell (CVE-2026-48356). Some infected Magento stores had files uploaded through PolyShell or SessionReaper (CVE-2025-54236). In a separate group of 3,024 stores affected by another skimmer, the infection rate was 32%. One Magento 2.4.7-p1 store contained PolyShell webshells disguised as PNG images, alongside malicious code in a CMS block. The code switched from additionalver.com to deepveindrilling.com on October 7. The store had not installed Adobe’s PolyShell fix, APSB26-73. However, installing all patches does not guarantee a store is safe. Another store running Magento 2.4.7-p10 had no missing core patches but was infected on October 23. Earlier scans had already found a rogue admin account and a GSocket backdoor. Sansec recommends checking CMS content for malicious code, including base64-encoded domains. Administrators should search both the cms_block and cms_page tables for suspicious patterns such as fpCode and se_referrer. They should also inspect uploaded files for webshells, check for unknown admin accounts, and look for suspicious processes. Removing the malicious scripts is not enough: store owners must also identify and fix the vulnerability or backdoor that allowed the attackers in. After cleanup, change the database, admin and API integration passwords. Install APSB26-73 for PolyShell and APSB25-88 for SessionReaper. Sansec Shield blocks both attacks while you schedule the upgrade. Sansec lists the indicators of compromise at the end of its report. “If your store loads any domain from the indicators of compromise, the attacker can write to your database. Deleting the script does not remove that access. See How to check your store.” concludes the report. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Magento)
DreamHost has forced password resets for email and FTP/SFTP users following an incident that disrupted connections to the File Manager interface. The latest update from the web hosting provider notes that all affected account owners have received recovery instructions. Users will be unable to log in until their account owner sets new credentials. DreamHost is a U.S. web hosting provider and domain registrar that offers WordPress hosting, business email, virtual private servers, dedicated servers, and cloud services. According to the company page, it serves over 400,000 customers in more than 100 countries and supports at least 1.5 million websites and applications, including 750,000 WordPress installations. DreamHost initially reported connectivity problems affecting its File Manager interface on October 23, but the impact quickly expanded to other services.
A critical flaw dubbed 'HTTP2Shell' in Brotli, the C decoder library built into Node.js, could allow attackers to corrupt memory and access sensitive data. The security issue is tracked as CVE-2026-111497 and is also known as “Broccoli2Shell” and “Node2Shell.” It affects Brotli versions 1.0.3 through 1.0.9, 1.1.0, and 1.2.0. The vulnerability has been reported under several names because it was initially identified in Node.js before researchers found that the same flaw in the Brotli decoder could affect software across multiple ecosystems. The name HTTP2Shell reflects its impact on HTTP clients and servers, although the vulnerability is not specific to HTTP/2 and can affect other applications that decompress attacker-controlled Brotli data. Apart from Node.js, potentially affected software includes Python HTTP clients such as urllib3[brotli], requests, httpx, and aiohttp[speedups], when configured with a vulnerable Brotli decoder, as well as other applications and tools that use vulnerable Brotli implementations.
Microsoft released emergency .NET security updates to patch a critical vulnerability known as Broccoli2Shell, also tracked as Node2Shell and HTTP2Shell. Tracked as CVE-2026-111497, this critical-severity flaw was found by Imperva Red Team and stems from an out-of-bounds write in the Brotli decoder, which expands data compressed using Google's widely used Brotli lossless compression algorithm. Successful exploitation lets unprivileged attackers use a maliciously crafted Brotli-compressed stream to trigger crashes or memory leaks and, in some cases, gain remote code execution on devices with vulnerable Brotli binaries or libraries. According to a Microsoft advisory published Sunday, attackers can also exploit this flaw to remotely escalate privileges on systems with unpatched .NET installations that use non-default configurations.
A Chinese national was arraigned in California after being extradited from the Netherlands for his alleged involvement in a cryptocurrency investment scam that stole nearly $37 million from 174 victims in the United States. In these fraud schemes (also known as pig butchering or romance baiting scams), cybercriminals contact targets through social media, dating sites, and messaging apps, build trust, and lure them into fake investment schemes, stealing their money instead of investing the funds as promised. Prosecutors said that, together with accomplices in the United States and Cambodia, 44-year-old Zhen Zhao tricked victims into transferring large sums of money to attacker-controlled accounts, claiming that the funds would be invested in digital assets. The criminal ring Zhao was allegedly part of reached out to targets across the United States via unsolicited text messages, social media, phone calls, and even online dating services to gain their trust, promoting the fraudulent digital asset investments and saying that the victims' funds' value increased, when, in fact, their money was stolen.
uCreater is Android spyware disguised as trusted apps, stealing credentials, messages and wallet data through a rentable platform. uCreater is a rentable Android spyware platform that hides inside repackaged legitimate apps, targeting messaging services and mobile wallets. Its YingGai framework can intercept one-time passwords, steal account and chat data, collect contacts, manipulate sessions and spoof camera feeds to bypass identity checks. Victims may install modified versions of Telegram, WeChat or GCash through sideloading or social engineering. Researcher Ovi found stolen data on a control server, but the number of victims and links between operators and customers remain unconfirmed. “The repackaged APK can be relatively thin: target-specific instrumentation is retrieved after installation from the operator’s Alibaba OSS and MinIO infrastructure, including buckets and paths named Huuk_<target> and fengzhuang-app (literally, “packaging”). Distribution hosts include telegram-apk.uzyzx.com.” researcher Ovi states. “I could reconstruct much of the post-installation execution chain from the recovered code, but I could not directly establish the initial delivery mechanism for every victim.” Ovi rebuilt most of the chain from recovered code but couldn’t establish how every victim got the first install. The product is older than the samples. The package com.ucreater.autotool dates to about 2014, Windows tools tied to ucreater.com were signed between 2016 and 2019, and the YingGai mobile line shows up in records from September 2021. The samples Ovi studied run from 2024 to 2026: uip.exe builds from February to December 2024, Telegram repacks from July and November 2024, the WeChat host and Telegram framework from December 2024, and the Onion Box client from March 2026. The control server’s ICP filing, 湘ICP备2024061212号, dates to 2024. The infrastructure hints at scale, though not at victim numbers. Ovi counts about 20 target apps with confirmed code support, six operator domains with dozens of subdomains, three Redis buses, two MQTT buses, and a cloud-phone cluster in Changsha spread across several ISPs. A Censys search for the operator’s Tengine gateway fingerprint returned roughly 48 hosts. It looks like a multi-year product sold to many downstream operators, but he can’t say how many customers used it or how many devices fell. Attributing the platform to a specific legal entity is more difficult. Hunan Youchuang Technology is linked to the ucreater.com brand and a self-signed certificate on videoflow.cn. Changsha Sukai Network holds the ICP filing for the control server’s main domain, qianxuncc.cn, while Changsha Zhuoqing E-commerce appears as the signing identity on uip.exe, using a DigiCert certificate. A common owner is plausible but unproven. Another lead involves hunanzhijing.com, a domain belonging to the Hunan Intelligent Police Public Safety Technology Research Institute. It shares the same gateway fingerprint, and an official Hunan document connects the institute to an intelligent-policing research project. However, shared cloud infrastructure establishes only co-tenancy, not ownership or operational involvement. There is no evidence that the police developed or operated uCreater. Other infrastructure links also require caution. A Thailand-based relay used by the Telegram component, hosted on Huawei Cloud, also served gambling websites, an advertising-account service and an SMS service. The cloud-phone web client displayed a certificate for another vendor, while the gateway served a Vue application titled 舆情交易, meaning “public-opinion trading”. These observations are leads for further investigation, not proof of common ownership. uCreater relies on app virtualization, a technique already identified by Zimperium as a growing mobile threat. VirtualApp runs another application inside a container, LSPatch merges code hooks into repackaged apps without requiring root access, and Pine hooks Android’s runtime. Together, these components allow malicious code to operate inside a legitimate app while it is signed in and has access to decrypted data. “uCreater is notable because it turns these techniques into a reusable, remotely managed product.” the researchers continues. Operators distribute modified versions of Telegram-X, Telegram-Web and other messaging apps, integrating modules through LSPatch or loading them from an asset called TgJarPine.dex. WeChat can instead run inside a VirtualApp container, including the va_vx or Onion Box host. LSPatch also stores the original APK under cache/lspatch/origin/*.apk. The presence of this path or the specific asset can help identify an infected device. The approach differs from GodFather, a banking trojan documented by Zimperium that runs apps in a virtual container and displays fake overlays to capture credentials. uCreater operates inside the real application without relying on an overlay. It uses XposedBridge hooks, reflection and access to the app’s own database interfaces. This allows it to reach beyond login screens and potentially support any application for which the vendor develops a module. Ovi found no evidence linking the two platforms. One distinguishing feature is a local command server embedded in each hooked application. It exposes around 40 numbered handlers, with descriptions and parameters that allow operators to discover available functions. The code supports device monitoring, contact and SMS collection, installed-app enumeration, message sending, chat-history retrieval, session management and account-related actions. The Telegram module can execute SQL queries directly against the application’s live database through its own interface. Its Db_MessagesStorageRawQuery handler accepts operator-supplied SQL and returns results through compatibility classes that recreate Telegram objects. This can expose information that would otherwise be protected in the app’s encrypted or locally stored data while the application is running. The module also hooks Telegram’s SRPHelper.getX routine, capturing the two-step verification password in cleartext before hashing and storing it for exfiltration. The code provides functions to list or reset sessions, change or confirm a linked phone number, delete an account and lock users out. It can also remove its own session from the victim’s device list, potentially concealing the operator’s access. Ovi stresses that the source code demonstrates these capabilities, not that every action has been successfully performed against victims. The implant retrieves its control-server address from Redis using the Domain key, optionally selecting a value for a particular channel. It caches the address and can fall back to a hardcoded value, explaining why callback domains differ between builds. Signed JSON beacons report to endpoints under /api/callback/Tg*, while command requests include a device fingerprint and signature and use retries with increasing delays. Updates and additional code arrive through Alibaba Object Storage Service (OSS) and MinIO. The Telegram module retrieves an update manifest and hooks from designated storage paths, while a contacts.vcf file can supply contact information. The WeChat component subscribes to a Redis channel identified by a device-specific machine ID, with its host and credentials embedded in a child code file. The WeChat module focuses on account access rather than chat-history collection. Its _2_0_UploadAppLoginInfo command retrieves login identity information, after which the app data can be packaged, uploaded to OSS and used in account cloning. Ovi found no evidence that the examined sample accessed WeChat’s encrypted database. Ovi tested uCreater on a Pixel 10 Pro running Android 17 and found that its bundled Pine engine fails because of changes to Android’s internal APIs. The latest sample, dated July 29, 2026, still uses the outdated engine. Although this creates obstacles for operators, updating Pine could restore functionality, and other devices remain untested. Ovi also identified five samples that successfully contacted the control server, confirming active deployments targeting Telegram, Telegram-X and WeChat. Ovi and researcher Jurre discovered the platform’s source code after finding an exposed GitLab server. Other infrastructure was protected by IP allowlists, but this server contained the control platform’s source code, configuration and operational details. The repositories revealed Spring Cloud services for command dispatch, authorization capture, data collection, backups and message bridging, alongside a Vue-based administration console. Thirteen repositories contained commit histories identifying roughly three dozen developers, with activity timestamps consistent with China time. At least one commit used a corporate email domain associated with Tanzhou, a Changsha software firm. uCreater’s backend is a rented, multi-tenant platform that lets operators manage infected devices, steal account data and automate contact collection. Kafka, RocketMQ, Redis and Nacos coordinate commands and schedule activity to avoid triggering security controls. The infrastructure runs on Aliyun and Huawei Cloud, with control services and storage hosted in China. Although GitLab records identify developers behind key components, Ovi stresses they are not confirmed operators. The recovered backend did not appear to support Telegram, and some callback domains are no longer active. Ovi attributes the platform to a vendor in Changsha and Hunan operating through three companies, each linked to a different technical or infrastructure artifact. The reseller model, focus on mobile wallets and theft of authentication codes suggest financially motivated crime, but the researcher presents this as an assessment rather than a proven fact. The technical findings show that uCreater goes beyond a conventional banking trojan or a tool that merely steals credentials. By embedding code inside legitimate applications, it can manipulate sessions, access account data, collect contacts and potentially take control of messaging accounts. Its rented, multi-tenant architecture also allows different operators to use the same underlying platform. The scale of the infrastructure indicates a substantial operation, but neither the number of customers nor the number of compromised devices has been established. Likewise, infrastructure overlaps and developer records provide investigative leads, not conclusive proof of who operated the spyware or who was responsible for individual attacks. “Mapping points to a commercial resale structure, and targeting driven by wallets and OTPs. My assumption is financially-motivated crimeware.” concludes the report. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock (LockA locked padlock) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites. Share: Opens in a new window Opens in a new window Opens in a new window Press Release CISA Unveils New Strategy to Strengthen National Cyber Defense and Critical Infrastructure Security CISA 2030: Defend, Harden, Prevail Released October 26, 2026 Related topics: Cybersecurity Best Practices , Critical Infrastructure Security and Resilience WASHINGTON – The Cybersecurity and Infrastructure Security Agency (CISA) today released its new strategy, CISA 2030: Defend, Harden, Prevail, which advances President Trump’s Cyber Strategy for America and National Resilience Strategy while strengthening the security and resilience of the nation’s critical infrastructure.
Kali Linux has released version 2026.3, its third release this year, featuring six new hacking tools, desktop environment improvements, refreshed build scripts, and more. This Linux distribution is available as an installable OS or as a live environment and supports a wide range of hardware, including Raspberry Pi devices and Android phones via Kali NetHunter. Kali is designed for ethical hackers and cybersecurity professionals who want to perform red-teaming, penetration testing, security assessments, and network research. In this release, the Kali Team has updated 126 packages, added new libraries, and upgraded the kernel from 6.19 to 7.1.5. It has also added 6 new tools, including: Kali Linux 2026.3 also brings desktop environment updates, including KDE Plasma 6.7 and new icons. KDE Plasma 6.7 includes performance and integrated Intel GPU power-usage improvements, plus workflow optimizations.
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. ai_agent_opencode_access_to_sensitive_file_win Tap to previewTap to hide 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. ai_agent_dangerous_execution_mode Tap to previewTap to hide 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. suspicious_mcp_server_artifact Tap to previewTap to hide 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. llm_api_access_by_unusual_apps Tap to previewTap to hide Some Kaspersky EDR rules tag events for investigation but do not generate alerts. For example, the ai_agent_**_execution rules 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. local_ai_model_artifact Tap to previewTap to hide 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.
Confirm this action.
Leaving now will discard your changes.