AI Agent Auditing
Overview
AI agent auditing studies whether autonomous systems follow their instructions and policies as they use tools, exchange information, and change external state. Researchers combine trace analysis, runtime monitoring and authorization, static and dynamic security testing, adversarial benchmarks, and tamper-evident logs to detect violations and reconstruct how they occurred. A central lesson is that correct final answers or benchmark scores do not establish safe or faithful execution; audits increasingly examine intermediate decisions, permissions, evidence provenance, and effects on the environment. The field still faces challenges in obtaining complete, independent records and reliable evaluators, while balancing stronger oversight with task utility, cost, and agent autonomy.
Latest papers
269 papers79 papers in the last four weeks, up 39% on the four weeks before. 0.5% of all new papers.