Measuring Security Without Fooling Ourselves: Why Benchmarking Agents Is Hard

TL;DR

This paper discusses the challenges in reliably benchmarking AI agents in security-critical roles, highlighting vulnerabilities, staleness, and uncertainty, and proposes directions for more trustworthy evaluation frameworks.

Contribution

It identifies core challenges in security benchmarking and suggests practical solutions to improve evaluation robustness and trustworthiness.

Findings

Benchmarks are vulnerable to manipulation and exploitation.

Security evaluations suffer from temporal staleness and runtime uncertainty.

Proposes practical directions for more robust evaluation frameworks.

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.