Big Sleep

AI in Vulnerability Management: Assistant, Weapon, and Emerging Target

Artificial intelligence now plays three simultaneous roles in vulnerability management: it assists defenders with prioritization and analysis, arms attackers with faster discovery tools, and introduces entirely new classes of assets and vulnerabilities that must be managed. Mature applications include ML-driven risk scoring in platforms such as Tenable VPR, Qualys TruRisk and Rapid7 Active Risk, which cut priority lists by 40-50 percent, and Google’s Big Sleep project that discovered a real-world SQLite flaw (CVE-2025-6965) before exploitation occurred. On the offensive side, systems like XBOW completed 104 test scenarios in 28 minutes versus 40 hours for human testers, shrinking the average time-to-exploit window to negative seven days according to Mandiant M-Trends 2025. New AI-specific attack surfaces are documented in the OWASP Top 10 for LLM applications and the emerging MCP Top 10, covering prompt injection, tool poisoning, and command injection against Model Context Protocol servers. Real incidents already include Air Canada’s chatbot inventing a refund policy and New York City’s MyCity bot dispensing illegal advice for nearly two years. Researchers have also demonstrated sandbox escapes in coding agents from Cursor, OpenAI Codex and Claude Code through classic file-write-and-execute patterns rather than any sentient behavior.