11.7Key Takeaways

• The OWASP Top 10 for LLM Applications is the essential framework for AI security testing -- know all ten items and have automated tests for each
• Prompt injection is the SQL injection of AI -- the most exploited vulnerability
• Jailbreak testing requires a maintained library of evolving techniques
• Data leakage has more dimensions in AI: training data memorization, system prompt extraction, cross-session contamination
• RAG systems add retrieval poisoning and citation fabrication to the threat model
• Traditional vulnerabilities are amplified, not replaced, by AI features
• Shift-left tools should include AI-specific rules
• Threat modeling must extend STRIDE with AI-specific categories
• Regulation compliance is an ongoing testing practice, not a one-time audit

Interview Talking Point: "Security testing for AI applications requires a dual focus. First, the classic web security fundamentals -- OWASP Top 10, SAST, DAST, SCA in CI -- because an AI app is still a web app. Second, the AI-specific attack surface: prompt injection, jailbreaks, data leakage, and RAG poisoning. I build layered security testing programs where every commit gets static analysis with AI-specific Semgrep rules, every deployment runs our prompt injection and jailbreak test suites, and production has continuous output monitoring for PII leakage. For regulated industries, I align the test program with the EU AI Act requirements -- bias testing, explainability, human oversight, and audit trails. The key insight is that AI security testing is not a one-time activity. New jailbreak techniques emerge weekly, so the test suite must evolve as fast as the attack surface."