AI Agent Skill Verification Framework Proposed
A novel framework aimed at verifying the behavioral integrity of AI agent skills has been introduced. This initiative, known as the Behavioral Integrity Verification (BIV) problem, tackles the disparity between the stated and actual abilities of LLM agent skills, which are enhanced by privileged third-party functionalities such as filesystem access, credentials, network interactions, and shell execution. While current safety protocols effectively identify harmful prompts and dangerous runtime actions, they fail to validate the skill artifacts themselves. The BIV framework integrates deterministic code analysis with LLM-aided capability extraction, generating structured evidence for further analysis: deviation taxonomy, root-cause classification, and malicious-skill identification. An examination of 49,943 skills from the OpenClaw registry uncovers a widespread description-implementation gap, with 80.0% of skills diverging from their declared behavior, including four new compound threats.
Key facts
- BIV problem formalizes typed set comparison between declared and actual capabilities.
- Framework pairs deterministic code analysis with LLM-assisted capability extraction.
- Analysis of 49,943 skills from OpenClaw registry shows 80.0% deviation rate.
- Four novel compound threats identified in the deviation taxonomy.
- Skill artifacts previously unverified in existing safety approaches.
- BIV supports three downstream analyses: deviation taxonomy, root-cause classification, malicious-skill detection.
- Shared taxonomy bridges code, instructions, and metadata.
- Published on arXiv with ID 2605.11770.
Entities
Institutions
- arXiv
- OpenClaw