In plain words: It examines why tests that judge AI agents for security jobs mislead, finding three problems: the tests can be tricked, they go stale as threats change, and the same agent scores differently each run. It then suggests ways to build more trustworthy tests.

Abstract

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.

Sahar Abdelnabi, Chris Hicks, Konrad Rieck, Ahmad-Reza Sadeghi
arXiv:2605.22568 · cs.CR, cs.AI · submitted May 21, 2026
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