How to verify AI-discovered vulnerabilities aren't just training data echoes

Short summary

AI models can conflate memorized CVE knowledge with novel vulnerability discovery—a problem for security teams. The author provides three verification techniques: fuzzy-match findings against NVD historical data to catch recalls, analyze git history for existing fixes, and re-run analysis on anonymized code to confirm genuine reasoning. Treat AI security findings as investigative leads requiring manual validation and proof-of-concept.

• •AI agents struggle to distinguish between recalling CVE training data and discovering new vulnerabilities from code
• •Three validation methods: NVD fuzzy-matching, git history inspection, and code anonymization tests
• •Always require manual review and working proof-of-concept before acting on AI-flagged security issues

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