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.
Two critical RCE vulnerabilities (CVE-2026-109618 and CVE-2026-109619), collectively tracked as PHP2Shell, have been successfully exploited in the wild. The malicious activity was caught by Adel Karimi, detection and response lead at Perplexity, whose honeypots recorded evidence of confirmed remote code execution against an Apache mod_php installation running PHP 8.4.26. "This is now confirmed RCE with usable memory disclosure, not just a crash followed by a suspicious process. The response delivered an 800 KiB decoded cookie dump, and a function address derived from that dump using the public exploit's offsets appears in the subsequent execution payload," Karimi shared. "The client acknowledged the full response; the shell command then ran about 1.6 ms after the execution request's final captured byte."
Security Affairs AI-CYBERSECURITY newsletter includes a collection of the best articles and research on AI in the international landscape Artificial intelligence is rapidly changing cybersecurity, reshaping both the techniques used by attackers and the tools available to defenders. AI agents can automate tasks, analyze large amounts of data, discover vulnerabilities and accelerate offensive operations. At the same time, AI is creating new capabilities for threat detection, incident analysis and response. Security Affairs’ new newsletter follows this evolution, covering every week the latest threats, attacks, vulnerabilities and developments at the intersection of AI and cybersecurity. AI-CYBERSECURITY Newsletter 25-year-old Israeli founder sells stealth AI startup for up to $35 million without raising capital OpenAI claims 100-plus secret files followed ex-employee back to Apple Open weight takes 71.5% of tokens and 23% of spend OpenAI Models Hacked Data Portals, Raided a Crypto Wallet to Finish Tasks Meta removes language that raised scraping and AI training concerns Can LLMs Supercharge Geolocations? Our Tests Show Big Improvements But Humans Remain Key Agents are your newest insiders. Security must move at their speed Anthropic Acquires Confidential AI Startup Opaque Systems ATT&CK v20: Critical Infrastructure Campaigns, Adversary Use and Abuse of AI, & Iranian Cyber Espionage UK Election Watchdog Develops AI System to Flag Deepfakes Within 3 Hours Meta’s Muse hits 10M U.S. downloads in 40 days, a milestone reached by only 11 other apps Suppressing misalignment monitoring alerts and disabling misalignment monitoring Anthropic pitches AI safety bill that may block a model if 50+ people could be killed Making attacking agents pay LiteLLM Compromised in Supply Chain Attack Delivering Credential Stealer and Backdoor AI Just Found a Bug That Puts up to 70% of the World’s Websites at Risk AI-driven, large-scale nation-state campaign using ARTEX enables supply chain compromise Torvalds: AI is fine for competent devs, but spare us the ‘drive-by’ patches Our latest defenses against evolving web scams UK government sets out $800 million of AI security funding Securing our frontier model training environment Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
Researchers found a malicious AGENTS markdown instruction file in a customer’s Downloads folder that tricked an AI coding agent into installing a rogue npm package. They reproduced the attack against OpenAI Codex and demonstrated a related technique against Claude Code to leak OAuth tokens and hijack the account using only the malicious instructions. Markdown files such as AGENTS.md are plain-text files that contain project-specific instructions for AI coding agents, such as the tools to use, how to run tests, and the coding conventions to follow. Researchers at Forever Security, a company that monitors for risks introduced by AI agents and autonomous software, discovered the tactic on a customer’s computer, where a coding agent attempted to install the malicious npm package ‘function-color.’
Security Affairs Malware newsletter includes a collection of the best articles and research on malware in the international landscape Malware Newsletter Auditing the auditor: Unmasking Apex Accelerator GhostAction Attack Escalates By Targeting GitHub Users Plex vulnerabilities being exploited in the wild to drop a cryptominer WordPress Rank Math SEO PRO Plugin 3.0.123 is vulnerable to a high priority Backdoor ROMULUS: Analysis of a New Multi-Payload ClickFix Delivery Platform Hackers Hijack Five OVHcloud CLI Releases to Distribute Secret-Stealing Malware Pegasus and Mercenary Spyware Attacks on Serbian Pro-Democracy Figures When wipers leave backups: an analysis of ArgonWiper’s encryption workflow LiteLLM Compromised in Supply Chain Attack Delivering Credential Stealer and Backdoor Operation FlashHole: Lazarus breaks security, defies control Cross-Platform Malware Fingerprint Identification with a Convolutional KAN–Transformer Hybrid Architecture Geometry-Aware Risk-Guided Active Learning for Malware Concept Drift Adaptation Mal-AAEGAN: Adversarial Autoencoder with Double-Critic WGAN-GP for Malware Generation Q-MALDetect: Quantum Image-Based Malware Detection and Adversarial Robustness Analysis Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
A new round of the weekly Security Affairs newsletter has arrived! Every week, the best security articles from Security Affairs are free in your email box. Enjoy a new round of the weekly SecurityAffairs newsletter, including international press. Capcom Data Breach Exposes 11.7 Million Email AddressesU.S. CISA adds Arista VeloCloud Orchestrator flaw to its Known Exploited Vulnerabilities catalogPHP Warns of New Code Execution Flaw Days After PHP2Shell PatchesMeta Faces $25 Billion Penalty Linked to Cambridge Analytica ScandalCapcom Data Breach Exposes 11.7 Million Email AddressesU.S. CISA adds Arista VeloCloud Orchestrator flaw to its Known Exploited Vulnerabilities catalogPHP Warns of New Code Execution Flaw Days After PHP2Shell PatchesMeta Faces $25 Billion Penalty Linked to Cambridge Analytica ScandalPHP2Shell: Millions of Attacks, No Confirmed Successful Exploits YetINTERPOL Operation Serengeti III Nets 1,462 Arrests and Recovers $105 Million in Cybercrime CrackdownMicrosoft Links ConnectWise Breach to China-Linked Group Using AI Pentesting Tool ARTEXSignal Vulnerability Could Keep Calls Alive After Users Hang UpPHP2Shell Attacks Started Four Days Before Public Disclosure, AWS ReportsFBI and CISA Warn of SafePay Ransomware Attacks on 600 OrganizationsRussian Satellite Makes a Close Approach To A Commercial Radar Satellite Used by UkraineOpenAI Reports Two Incidents of AI Agents Disabling Their Own OversightUS Government Orders DeepSeek Out of Federal Civilian SystemsPHP Prepares Emergency Fixes for Two Critical Remote Code Execution FlawsNSO Spyware Pegasus Hit a Serbian University Rector Six Days Before Snap ElectionsNikkei data breach affected over 1.8 million recordsGoogle Cloud Outage in London Disrupts Revolut Banking ServicesAfD Email Breach Exposes 421,000 Messages in Anonymous Online ArchiveMicrosoft Treats AI Agents as Insider Threats in New Security FrameworkPlex2Shell Exploit Used to Turn Vulnerable Plex Servers Into Crypto MinersMeta wants to scrape your website to train its AI, and you can’t say noDeepfakes and Election Manipulation: UK Watchdog Seeks Faster DetectionBig Fish: OpenBSD Root Exploit Chains a 37-Year-Old BSD Bug With a Kernel FlawAttackers Abuse Stolen ConnectWise Application Credentials to Target Microsoft 365Japan’s SiteGuard WAF flaw CVE-2026-106931 is already being exploited16-Year-Old Broadcom Wi-Fi Bug Lets Attackers Take Over Routers Without a PasswordRust-Based CikaBika Bot Turns Discord into a Command Center International Press – Newsletter Cybercrime U.K. Sanctions Cryptomus for RU Money Laundering Anonymous hackers claim to leak 421,179 internal emails from Germany’s Russia-aligned AfD Feds Suspect Ransom Negotiator Ran ShinyHunters Extortion Site #StopRansomware: SafePay Ransomware Cybercrime crackdown in Africa nets 1,462 arrests, recovers over USD 105 million U.K. Sanctions Cryptomus for RU Money Laundering Nigerian Man Sentenced To Eight Years For Hacking, Fraud, And Identity Theft Schemes 1 Million Impacted by Capcom Data Breach Malware Auditing the auditor: Unmasking Apex Accelerator GhostAction Attack Escalates By Targeting GitHub Users Plex vulnerabilities being exploited in the wild to drop a cryptominer ROMULUS: Analysis of a New Multi-Payload ClickFix Delivery Platform Hackers Hijack Five OVHcloud CLI Releases to Distribute Secret-Stealing Malware Hacking ConnectWise (SkyKick) Vendor Credentials Abused in M365 Attacks One Parser, Two Bugs, Many Routers: Exploiting Broadcom WiFi Across eCos and Linux Plex2Shell Unauthenticated RCE in Plex Media Server Big Fish: Exploiting a 37-Year-Old Kernel Bug in OpenBSD Rapid Response: php2shell, Leaked Critical PHP Vulnerabilities (CVE-2026-109618 & CVE-2026-109619) Making attacking agents pay LiteLLM Compromised in Supply Chain Attack Delivering Credential Stealer and Backdoor Amazon Threat Intelligence identifies PHP2Shell exploitation prior to public disclosure in MadPot Proof-of-concept exploit released for PHP vulnerabilities (CVE-2026-109618, CVE-2026-109619) PHP2Shell Exploit Tooling Found on Exposed Attacker Server Intelligence and Information Warfare TAG-193’s Regional Espionage Operations ATT&CK v20: Critical Infrastructure Campaigns, Adversary Use and Abuse of AI, & Iranian Cyber Espionage No to Palantir – Five Times UK Election Watchdog Develops AI System to Flag Deepfakes Within 3 Hours Pegasus and Mercenary Spyware Attacks on Serbian Pro-Democracy Figures Russian satellite makes possibly unprecedented approach to another spacecraft without permission AI-driven, large-scale nation-state campaign using ARTEX enables supply chain compromise Operation FlashHole: Lazarus breaks security, defies control Pegasus spyware founder urges governments to trust his tech and ditch US AI Cybersecurity Character.ai files for bankruptcy following lawsuits alleging ties to teen suicides Cybersecurity Jobs Report 235,786 Agents are your newest insiders. Security must move at their speed Google Cloud services face outage in London, Revolut services down too Government websites go dark after AI-discovered flaw sparks security scramble Widespread PHP Flaw May Cause Many Site Outages Today Suppressing misalignment monitoring alerts and disabling misalignment monitoring AI Just Found a Bug That Puts up to 70% of the World’s Websites at Risk CVE-2026-113380 — RingRTC (Signal): Flooded Call Event Queue Blocks Local Hang-Up Cellebrite’s Advanced Unlocks and the case of the missing iPhone 17 Meta ordered to pay $25 billion penalty to New Mexico in data privacy case PHP Warns of Another Unpatched RCE Two Days After Emergency PHP2Shell Patches Our latest defenses against evolving web scams Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
Pierluigi Paganini answers questions about the PHP2Shell vulnerability, early exploitation, AI-assisted attacks, and the risks to businesses, servers, and personal data. 1. Your latest report reveals that hackers began exploiting the PHP2Shell vulnerability four days before it was publicly disclosed. First, can you explain what PHP is, how widely it is used across the internet, and why this particular security flaw is so serious? PHP is a programming language widely used to build websites and web applications. It powers a significant share of the web. PHP2Shell is serious because it can allow attackers to execute commands on a vulnerable server remotely. Depending on the configuration and the server’s privileges, this could give threat actors access to sensitive data, applications and other systems. 2. According to AWS researchers, attacks were already underway before the vulnerability became public. How did researchers uncover this early exploitation, and what does it tell us about the ability of hackers to identify security weaknesses before developers can fix them? AWS researchers detected exploitation attempts against their honeypot network four days before public disclosure and the release of patches. This shows that attackers can discover or weaponize vulnerabilities before defenders have time to respond. AI-assisted analysis may accelerate this process, helping attackers turn technical information into working exploits in hours rather than days. However, detected attempts do not automatically mean successful compromises. 3. The vulnerability could potentially allow attackers to execute malicious commands on affected servers. What does that mean in practical terms, and what risks does it pose to websites, businesses and the personal information of their users? Remote command execution means an attacker may be able to make a server run commands without authorization. Depending on the attacker’s access, they could steal data, install malware, change website content, or use the compromised server to attack other systems. Businesses could face service disruption, financial losses and reputational damage. Users may also be exposed if their personal information is stored on an affected system. The actual impact depends on the server’s configuration and security controls. 4. The latest findings raise concerns about the gap between discovering a vulnerability and protecting systems against it. How quickly can attackers exploit these weaknesses, and are businesses adequately prepared to respond when critical security flaws emerge? The window can be extremely short. In some cases, attackers can develop or adapt an exploit within hours, leaving organizations little time to react. Many businesses still rely on patching schedules that are too slow for internet-facing systems. Organizations need continuous monitoring, rapid patching, tested incident response plans and clear visibility into exposed assets. They should also investigate suspicious activity dating back to before a vulnerability was publicly disclosed, because attacks may already be underway. 5. With PHP powering a significant portion of the internet, could we see further attacks exploiting this vulnerability? What should website operators do immediately to protect their systems, and should ordinary internet users be concerned about their data? Further attacks are possible, particularly against systems that remain unpatched or exposed. Website operators should apply the relevant security updates, check whether their PHP applications are vulnerable, and review logs for suspicious activity dating back to October 17. They should also restrict access and investigate signs of compromise. Ordinary users do not need to panic, but they should keep their accounts protected with unique passwords and multi-factor authentication where available. Whether personal data is at risk depends on which services they use and whether those systems were compromised. AI-assisted tools are increasingly being used to analyze code and identify vulnerabilities. How much of a role could artificial intelligence play in helping attackers discover and exploit flaws like PHP2Shell, and can defenders use the same technology to their advantage? AI can help analyze code, identify suspicious patterns and speed up the process of turning a vulnerability into a working exploit. This lowers some of the technical barriers for attackers and may shorten the time between discovering a flaw and exploiting it. However, AI is not the only factor, and we should not assume that every attack is AI-driven. Defenders can use the same capabilities to review code, prioritize vulnerabilities, analyze logs and detect suspicious behavior. The real advantage comes from combining AI with reliable data, skilled security teams and effective response processes. This case shows that attackers may exploit a vulnerability before a patch is available. Does this mean traditional vulnerability management is no longer enough, and how should organizations rethink their security strategy? Traditional vulnerability management remains essential, but patching alone is not enough when attackers may act before a fix becomes available. Organizations need to know which internet-facing systems they operate, understand their exposure and have additional controls in place. These include limiting access to vulnerable services, monitoring suspicious activity, applying temporary mitigations and preparing incident response procedures. They should also investigate whether systems were targeted before the vulnerability was disclosed. The goal is to reduce the opportunity for exploitation and detect attacks quickly, even when a patch is not yet available. Below is another interview I gave to TRT World discussing critical PHP vulnerabilities, the risks they pose to web servers, and the growing number of exploitation attempts. I explain why rapid patching and effective security measures are essential to minimize potential damage. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
U.S. Cybersecurity and Infrastructure Security Agency (CISA) adds Collabora Online flaw to its Known Exploited Vulnerabilities catalog. The U.S. Cybersecurity and Infrastructure Security Agency (CISA) added a Collabora Collabora Online – Built-in CODE Server flaw, tracked as CVE-2026-110447 (CVSS score of 9.2), to its Known Exploited Vulnerabilities (KEV) catalog. Collabora Online – Built-in CODE Server is an app for Nextcloud that lets you open, edit and collaborate on documents, spreadsheets and presentations directly in your browser, without needing Microsoft Office installed on your computer. It is based on Collabora Online and LibreOffice technology. CVE-2026-110447 is a critical OS command injection flaw in Nextcloud’s proxy.php that affects Collabora Online Built-in CODE Server before 26.4.303. Unauthenticated attackers can execute commands when the built-in server is stopped. Standalone Collabora servers are not affected. Update immediately. In versions up to 26.4.302, proxy.php included request data in a Bash command without proper sanitization, allowing attackers to execute OS commands as the web server user. Exploitation is possible only when the local CODE server is stopped, such as during a restart, upgrade or crash. The script runs directly through the web server without loading Nextcloud’s code. Exploitation has been reported since September 2026, with attackers deploying PHP web shells and stealing configuration files containing credentials and application secrets. Three administrators reported the vulnerability on GitHub, while the first recorded attack dates back to September 10. The first attack was reported by a Nextcloud administrator whose servers had been compromised. Logs showed malicious requests on September 10 and 11, 2026, from the same IP, targeting proxy.php on version 26.4.302. The attackers deployed a PHP web shell and searched one server for credential stores and cryptocurrency wallets. Although reviewed logs showed no file exfiltration commands, data accessible to the web server account must be considered potentially exposed. Two other administrators reported similar incidents, but none found evidence of root access. After updating to 26.4.303, one administrator recorded another attempt on September 14, but no new shell appeared. Project maintainer mmeeks urged users to update and remove leftover shx*.php files. The CVE record confirms exploitation in September and October 2026, but the total number of compromised servers remains unknown. The flaw affects only the Development Edition’s Built-in CODE Server deployment. According to Binding Operational Directive (BOD) 22-01: Reducing the Significant Risk of Known Exploited Vulnerabilities, FCEB agencies have to address the identified vulnerabilities by the due date to protect their networks against attacks exploiting the flaws in the catalog. Experts also recommend that private organizations review the Catalog and address the vulnerabilities in their infrastructure. CISA orders federal agencies to fix the flaw by October 26, 2026. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, CISA)
AI companies need usage data to align pricing with costs, improve products, identify churn risks, and grow revenue through informed decisions. The AI Paradox There is a paradox: AI companies exist to extract intelligence from data, yet many have almost no visibility into how their own software is being used. They design complex models, introduce their product, set a price, and then hope that customers renew their contracts. In a space as fast-paced as AI, such a strategy is no longer safe. Without usage as a guide, price decisions are made on assumptions rather than facts. Product roadmaps are driven by story, not by usage. Customer success teams learn about customer churn when renewal dates are approaching. In reality, usage intelligence has become one of the most important signals in an AI business, guiding everything from pricing strategy to product development and long-term monetization. Why AI Changes the Stakes for Usage Data AI changes the economics of software and, with it, the relevance of usage visibility. The economics of existing software products are linear in cost, whereas those of AI products are not. Compute, energy consumption, as well as third-party model licenses, can all be material variable costs. If usage increases rapidly (through enterprise adoption, agents, or workflows), the cost can grow faster than the revenue generated if pricing is not aligned with consumption. The opposite scenario is just as dangerous. If customers are not using key capabilities, churn may be approaching long before a renewal conversation begins. Research shows the extent of this visibility gap. For AI vendors, visibility is important on the commercial side, too. They need to know how customers are using the AI capabilities they sell. IDC research found that 48% of software buyers want better visibility into their usage. The pace of change is also a concern, with 70% ranking rapid change in the AI ecosystem among their top three AI-related security risks. Pricing, packaging, and business models must evolve at the same pace, and that requires reliable usage intelligence. What Usage Intelligence Actually Looks Like Of all the capabilities that make up a software monetization strategy, usage intelligence is the one most commonly treated as optional — and the one that most consistently separates companies that adapt from those that don’t. It is the feedback loop that connects real-world product consumption back to the decisions that shape pricing, packaging, and product direction. At the instrumentation layer, usage intelligence starts with telemetry data. This includes activation events, entitlements consumed, feature-level data, daily and monthly active users, and time-to-value metrics that track how quickly customers get value from the product. Thales Sentinel can also collect usage data from on-premises, offline, and disconnected environments, so vendors are not limited to tracking always-connected software. The operational layer is where all the telemetry data is made actionable. Usage dashboards in CRM systems enable account teams to see how customers use the software. Health scores can even detect reduced engagement well before any risk of renewal is seen. The enforcement layer makes sure that usage is consistent with contract rights. This is particularly important for consumption-based or token-based pricing schemes. Tracking accuracy is essential for determining customer fairness and vendor revenue. Finally, there is the feedback loop. The insights from usage need to flow back to those responsible for pricing, packaging, product development, and customer success. Without the feedback loop, the data is just more information. Pricing specialists have long argued for incremental experimentation. Research from Simon-Kucher reveals that companies that make frequent, data-informed pricing adjustments tend to achieve stronger growth than those that depend on static pricing models. Usage intelligence provides the evidence required to make those adjustments with confidence. How Usage Data Enables Multiple Business Models Business model agility is increasingly becoming a competitive advantage for AI companies, yet flexible business models cannot exist without trustworthy usage data beneath them. Consumption-based pricing, outcome-based pricing, token models, and hybrid subscription structures all depend on accurate measurement of how customers use the product. There is demand for these models, too. IDC research found that 45% of software buyers prefer usage-based models. In practice, most AI companies adopt a gradual sequence. They begin with subscription licensing to establish predictable recurring revenue. As adoption grows, they instrument feature usage and engagement patterns. That data then enables the introduction of consumption or token-based layers tied to high-value AI capabilities. One AI audio-recognition company did just that. It moved from perpetual licensing to several subscription tiers using Thales Sentinel. Its usage data showed which features customers used most, which helped the company decide how to structure those tiers without making changes to the core technology. Another AI recognition vendor delivering SDKs for embedded systems took a different approach using Sentinel. Their trial licenses expire after a defined number of executions instead of after a fixed time period. This usage-based trial model demonstrates genuine customer interest, giving them a clearer indicator of product-market fit than a simple time-limited evaluation would. Both examples show that usage intelligence transforms monetization strategies from guesswork into evidence-based decision-making. From Data to Decision: Three Practical Applications The real value of usage intelligence lies in informing concrete business decisions. Preventing churn is one of the most immediate uses. Feature-level engagement metrics can detect declining usage several months before a renewal date. This enables customer success teams to step in and show the value or introduce underutilized features, preventing a customer from leaving. Upsell and expansion opportunities can also show up in the usage data. Seat utilization may show that a team is close to capacity and needs more licenses. Feature usage can show when a customer is getting enough value from certain capabilities to justify moving to a higher tier or adding consumption-based features. Usage data can also inform the product roadmap. Rather than relying on assumptions about what customers want, AI companies can see which features people use and which get little attention. This is key in a market full of similar AI capabilities. Companies need to know which parts of their product customers value and are willing to pay for. There is also a need for the collection of usage intelligence to be privacy-conscious. For vendors operating under frameworks such as GDPR or the EU’s AI Act, it is necessary to design telemetry systems that prioritize aggregated data collection over personal data collection whenever possible. The Organizational Challenge However, technology alone is not enough to create usage intelligence. Businesses must also ensure that the intelligence is available to those who can act on it. Usage intelligence only delivers value if it reaches the people who can act on it. Pricing teams need consumption data to make confident adjustments to their models. Product teams need feature engagement data to inform roadmap decisions rather than rely on assumptions. Sales teams need account-level signals to time their conversations correctly. Customer success teams need early warning indicators to intervene before disengagement becomes churn. Finance teams need usage trends to build credible revenue forecasts. Getting that data to all of those functions consistently and in a usable form requires intentional data architecture and a shared organizational commitment to treating monetization as a cross-functional capability rather than a single team’s responsibility. This becomes harder as companies add more AI products. If usage data is split across separate products, teams may struggle to see how a customer is using the wider portfolio. Bringing the data together can show where products are being heavily used, where adoption is low, and where there may be room to expand an account. Building Systems Around Innovation The AI companies that sustain long-term commercial success are the ones who built a system around it that can learn, adapt, and improve as markets shift and customer needs evolve. Usage intelligence is what makes that system adaptive. Without it, pricing decisions are made on assumptions, packaging tiers are defined by intuition, and churn arrives without warning. With it, every commercial decision — what to charge, how to package, what to build next, which customers need attention — is grounded in evidence rather than expectation. In an industry defined by rapid innovation, the AI companies that listen to their own usage data will be the ones best positioned to turn experimentation into sustainable growth. About the Author: Kirsten Doyle has been in the technology journalism and editing space for nearly 24 years, during which time she has developed a great love for all aspects of technology, as well as words themselves. Her experience spans B2B tech, with a lot of focus on cybersecurity, cloud, enterprise, digital transformation, and data centre. Her specialties are in news, thought leadership, features, white papers, and PR writing, and she is an experienced editor for both print and online publications. She is also a regular writer at Bora. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, artificial intelligence)
A new UEFI bootkit named BlackX, deployed in an espionage campaign in early 2026, exploited the old Baton Drop vulnerability to bypass Secure Boot and load a malicious driver designed to interfere with security solutions from Kaspersky and Avast. The operation has been attributed with medium confidence to APT41, a Chinese state-sponsored threat group also known as Brass Typhoon, Wicked Panda, and Barium. It was discovered in early 2026 and targeted diplomatic agencies in Central Asia, particularly in Tajikistan, Uzbekistan, and Turkmenistan. Kaspersky researchers analyzed the BlackX bootkit and found similarities with BlackLotus, which has been circulating on hacker forums since October 2022 and leveraged the Baton Drop vulnerability (CVE-2022-21894) to bypass Secure Boot protection. ESET confirmed in March 2023 that BlackLotus exploited Baton Drop, and at the time it could compromise even fully patched Windows 11 systems.
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