Why Benchmarking AI Security Agents Is Fundamentally Hard: Three Core Challenges

arXiv·medium signal

Meta-analysis of AI agent security benchmarks identifies three structural weaknesses undermining evaluations: benchmark vulnerabilities (the benchmarks themselves are exploitable), temporal staleness (security knowledge decays rapidly making static benchmarks unreliable), and runtime uncertainty (agent behavior is non-deterministic across runs). The paper outlines directions toward more robust evaluation frameworks. Important context for anyone citing agent security benchmark scores — the scores may not mean what they appear to mean.