Measuring Security Without Fooling Ourselves: Why Benchmarking Agents Is Hard
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Abstract (arXiv)
The benchmarks used to evaluate AI agents in security-critical roles suffer from crucial weaknesses. Building on recent empirical evidence, we characterize three core challenges that undermine security evaluations: benchmark vulnerabilities, temporal staleness, and runtime uncertainty. We then outline practical directions toward building more robust and trustworthy evaluation frameworks.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 14 references that are in the map. First posted on arXiv 21 May 2026.
Builds on
6 of 14 references.
References · 6
- 2024Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Modelscited by 164
- 2025Establishing Best Practices for Building Rigorous Agentic Benchmarkscited by 102
- 2025AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agentscited by 90
- 2025CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scalecited by 78
- 2024Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Datacited by 50
- 2026Beyond Rewards in Reinforcement Learning for Cyber Defencecited by 3
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