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Behavioral Integrity Verification for AI Agent Skills

This paper introduces the behavioral integrity verification (BIV) problem, which checks whether AI agent skills' actual capabilities match their declared ones. By combining deterministic code analysis and LLM-assisted capability extraction, the authors analyzed 49,943 skills from the OpenClaw registry, finding that 80% of skills deviate from declared behavior, with 81.1% due to developer oversight, 18.9% due to adversarial intent, and 5% containing multi-stage attack chains. On a malicious-skill detection benchmark, BIV achieves an F1 of 0.946, outperforming existing methods.

SourceHacker News AIAuthor: Timofeibu
Behavioral Integrity Verification for AI Agent Skills
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Key points

  • Introduces the Behavioral Integrity Verification (BIV) framework to systematically compare declared and actual capabilities of AI agent skills.
  • Analysis of 49,943 skills shows 80% deviate from declared behavior, with four novel compound-threat categories identified.
  • 81.1% of deviations stem from developer oversight, 18.9% from adversarial intent, and 5% of skills contain multi-stage attack chains.
  • BIV achieves an F1 of 0.946 on malicious-skill detection, outperforming rule-based and single-pass LLM baselines.

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