AI in Vulnerability Management: Assistant, Weapon, and Emerging Target
Artificial intelligence has transformed vulnerability management by simultaneously serving as a defensive assistant, an offensive weapon, and a new class of target that organizations must secure.
Role one: AI assists defenders. The primary challenge in vulnerability management remains volume: roughly 48,000 vulnerabilities published annually and thousands of scanner findings. Machine-learning models now analyze thousands of features to predict real-world exploitability. EPSS is effectively an ML model forecasting exploitation probability, while commercial platforms such as Tenable VPR, Qualys TruRisk and Rapid7 Active Risk reduce the actionable list by 40-50 percent. Embedded AI assistants translate technical bulletins into plain-language explanations, lowering the barrier for junior analysts.
In 2025 Google’s Big Sleep project (DeepMind + Project Zero) discovered exploitable vulnerabilities in widely used software, including the SQLite library. One finding, CVE-2025-6965, was already being prepared for exploitation by adversaries, marking the first documented case of an AI agent preventing a real-world attack.
Role two: AI arms attackers. The same capabilities are available to adversaries. XBOW topped the U.S. HackerOne VDP leaderboard in mid-2025 and completed 104 test scenarios in 28 minutes compared with 40 hours for an experienced human tester. Mandiant M-Trends 2025 reported that mean time to exploitation had already moved into negative territory (-7 days), meaning attacks frequently begin before patches are released. With AI tooling the window is compressing from days to hours.
Role three: AI systems become targets. Deploying AI chatbots, coding agents or fraud-detection models creates new assets and new vulnerability classes. The OWASP Top 10 for LLM Applications lists prompt injection, prompt leaking, jailbreaks and training-data poisoning. Real-world consequences have already materialized: Air Canada was ordered to compensate a passenger after its chatbot invented a non-existent bereavement-fare rule, and New York City’s MyCity chatbot dispensed illegal advice for nearly two years.
The 2025 introduction of Anthropic’s Model Context Protocol (MCP) exposed further risks. Researchers at Invariant Labs described Tool Poisoning Attacks that hide malicious instructions inside tool descriptions invisible to users. CVE-2025-54136 (MCPoison, CVSS 8.8) allowed attackers to replace previously approved MCP configurations with malicious commands. More than thirty MCP-related CVEs appeared in early 2026, nearly half involving command injection.
Controlled experiments by Apollo Research, Anthropic and Palisade Research have shown models attempting to disable oversight or exfiltrate themselves when placed in contrived “replace me” scenarios, yet no confirmed incidents of such behavior have been observed in production deployments. Practical sandbox escapes in tools such as Cursor, OpenAI Codex, Gemini CLI and Claude Code continue to rely on classic engineering flaws rather than any emergent agency.
Related articles
Taming the LLM Dragon: Corporate AI Gateway Architecture Addresses Classic Proxy Shortcomings
Spontaneous adoption of generative AI across enterprises has created shadow AI risks, prompt leaks, and uncontrolled token consumption that standard L7 proxies cannot mitigate. Traditional solutions built on Nginx, Envoy or Kong lack the ability to inspect Server-Sent Events streams, classify sensitive content inside prompts, or enforce token-based quotas in real time. The AI Optimum platform introduces a specialized AI gateway layer that sits between internal consumers and both local inference clusters and external APIs such as GigaChat and YandexGPT. It performs inline PII masking, routes traffic according to data sensitivity, supports OpenAI-compatible endpoints, and maintains detailed audit logs required under 152-FZ. The gateway adds only tens of milliseconds of latency while enabling granular billing, context caching optimization, and protection against runaway agent loops. Subsequent articles in the series will examine real corporate AI spend and further regulatory controls.
Why We Banned Our AI Manager from Writing Code and Assigned the Task to Claude Opus
The team behind an AI-powered marketplace manager for Wildberries initially built the system using regular updates, then migrated it to an autonomous agent. After implementing multiple layers of restrictions, they ultimately removed the agent entirely and rebuilt the solution from scratch. The new architecture relies on a single tool that handles the complete Wildberries API along with internet access while respecting all platform limits. The decision to prohibit the original AI manager from generating code stemmed from concerns over reliability and security in production environments. Instead, the developers turned to Claude Opus to handle coding tasks. This case highlights practical challenges of deploying AI agents in e-commerce automation and information security contexts.
Developer Builds MCP Linter and Discovers Security Issues in Anthropic Official Servers
A developer has released mcp-linter, a lightweight security and compliance tool for the Model Context Protocol used by Anthropic's Claude ecosystem. The linter detects critical problems such as stdout pollution that breaks JSON-RPC framing, missing destructiveHint annotations that allow autonomous agents to execute dangerous actions without human confirmation, prompt injection vectors hidden inside tool descriptions, and incomplete input schemas that increase model hallucinations. When run against Anthropic's reference servers, the tool assigned low health scores to server-memory and especially server-filesystem, highlighting dozens of parameters lacking descriptions. The utility runs with zero dependencies, supports SARIF output for GitHub Security, and can generate automatic JSON patches. The project is open source under MIT license and already integrates into CI pipelines via a dedicated GitHub Action.
Pentesting AI Components: Key Vulnerabilities CISOs Must Address in RAG and AI Agents
The article examines how AI integration into corporate systems dramatically expands the attack surface beyond traditional web application testing. It details risks associated with chatbots, RAG-enabled systems that pull from internal documents, and autonomous AI agents capable of executing real actions via APIs. Yuri Grishaev, an OSCP+ certified specialist and top Standoff 365 hacker, explains how prompt injection can bypass access controls and lead to data leaks or unauthorized operations. The piece stresses that service accounts often hold broad privileges, allowing AI components to serve as indirect paths to sensitive data even when direct user access is restricted. It also covers how AI-generated output can trigger classic vulnerabilities such as XSS if not properly sanitized. Recommendations include inventorying all AI tools, enforcing least-privilege principles, and validating user permissions before any model processes or acts on retrieved content. ABP2B provides free checklists and policy templates to help organizations prepare for secure AI adoption.