80% of agent skills in a 49,943-skill registry don't do what they claim — most of it is sloppiness, not malice

Notes on Behavioral Integrity Verification for AI Agent Skills (arXiv:2605.11770) — Yuhao Wu, Tung-Ling Li, Hongliang Liu · May 2026

Note published July 25, 2026 · written by SkillFed’s research pipeline from the paper above · how these notes are made

AI-assisted notes · reviewed by SkillFed Skill security Bridge: security × benchmarks

Most skill safety work checks prompts and runtime actions, never the skill artifact itself against its own description. Behavioral integrity verification (BIV) names that missing check: a typed comparison between what a skill declares and what it actually does, run over a shared capability taxonomy of 29 capability types spanning code, natural-language instructions, and metadata. The declared side comes from parsing manifests plus LLM-assisted extraction, constrained by a hallucination-control filter that requires a quoted source span for every claimed capability. The actual side comes from deterministic analysis — inter-procedural taint analysis for Python, regex extraction for JavaScript and shell — plus LLM parsing of natural-language instructions. Both sides land in the same typed taxonomy, producing one structured evidence record per skill that feeds three downstream jobs: a deviation taxonomy, a root-cause classifier, and a malicious-skill detector.

Run across 49,943 skills pulled from the OpenClaw registry, the deviation taxonomy turns up a wide description-implementation gap: 80.0% of skills deviate from what they declare, roughly split between undeclared capabilities quietly present in the code and declared capabilities that were never implemented. Root-cause classification then sorts those deviations by intent: 81.1% trace to ordinary developer oversight, 18.9% look adversarial, and 5.0% of all skills carry a predicted multi-stage attack chain. Clustering surfaces four compound-threat patterns worth naming individually: exfiltration chains, remote-code-execution chains, code obfuscation, and data-lineage violations. How adversarial each one is varies sharply by category — it's not one registry-wide rate. Feeding this structured evidence into an LLM judge, with an explicit override rule for cases where compound-threat flags coincide with undeclared high-risk capabilities, gets malicious-skill detection to an F1 of 0.946 on a 906-skill benchmark — ahead of both the rule-based and single-pass LLM baselines it's tested against.

Key numbers

skills audited from the OpenClaw registry	49,943
of skills deviate from their declared behavior	80.0%
of deviations trace to adversarial intent (vs. 81.1% oversight)	18.9%
of skills carry a predicted multi-stage attack chain	5.0%
malicious-skill detection F1, 906-skill benchmark	0.946

What this means for skill authors

The headline number reads alarming until the root-cause split lands: four in five deviations are oversight, not malice. That's the more useful fact for anyone deciding what to do about it. A skill that quietly touches the filesystem it never mentioned is usually a stale manifest, not a trojan. Treat every gap as a security incident and reviewers drown — and learn to ignore the real ones. Separating "needs a documentation fix" from "needs a security review" is what makes auditing at registry scale tractable at all.

The category-level detail is the part worth sitting with longer than the topline rate. Adversarial fraction swings from roughly 1 in 10 deviations for filesystem-related gaps up to 9 or 10 in 10 for instruction-level threats and code obfuscation. A single risk score per skill would flatten all of that into noise. The compound-threat categories matter because they're combinations, not single flags: a skill that both reads credentials and phones out is a different animal than one that does either alone. That distinction only shows up if declared and actual capabilities get compared as structured, typed sets — not eyeballed.

The general shape here — verify the artifact against its own claims, not just its runtime behavior — is the same logic behind scanning a skill through more than one independent method before it reaches a catalog. A manifest is a claim the author makes about their own code; nothing in it is self-verifying, and these numbers show how often that gap opens, at real registry scale.

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References

Wu, Y., Li, T.-L., & Liu, H. (2026). Behavioral Integrity Verification for AI Agent Skills. arXiv:2605.11770.