Measuring Security Without Fooling Ourselves: Why Benchmarking Agents Is Hard
For researchers and practitioners evaluating AI agents in security-critical roles, the paper highlights fundamental flaws in current benchmarks and suggests improvements.
The paper identifies three core weaknesses in security benchmarks for AI agents—vulnerability, staleness, and runtime uncertainty—and proposes directions for more robust evaluation frameworks.
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.