Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents
The rapid deployment of LLM-based autonomous agents has introduced safety risks that extend far beyond traditional LLM concerns, prompting a proliferation of safety benchmarks since late 2023. However, these benchmarks have developed independently, with inconsistent threat models, incompatible metrics, and overlapping yet incomplete risk coverage. We present the first systematic analysis dedicated to agent safety benchmarks as evaluation instruments. We catalog 40 behavioral agent-safety benchmarks (2023-2026), plus 5 adjacent evaluator, defense, and dataset artifacts, propose a six-axis taxonomy of benchmark evaluation methodology, and apply it across the corpus to characterize how metho...
| Authors | Miles Q. Li, Benjamin C. M. Fung, Boyang Li et al. |
|---|---|
| Primary category | cs.CY |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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