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AWS Security Blog · September 9, 2026

AWS Deception Benchmark tests false positives in AI security review

analysis Featured · 92/100 practical score Model EvaluationAI Red TeamingAI Engineering
Why it matters

AWS releases a benchmark and methodology for distinguishing vulnerable code from suspicious-looking code protected by effective mitigations. Its single-turn model evaluation compares direct classification with exploit-oriented prompting and exposes tradeoffs between false positives and missed flaws.

My takeaway: Evaluate safe and vulnerable examples together, including deployment context. Report false-positive and false-negative rates by model and prompting method; these single-turn results do not establish the performance of a complete agentic review tool.
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