Checking the repo, not just the SKILL.md, cuts flagged 'malicious' skills from 46.8% to 0.52%
Notes on Malicious Or Not: Adding Repository Context to Agent Skill Classification (arXiv:2603.16572) — Florian Holzbauer, David Schmidt, G. Gegenhuber, S. Schrittwieser, Johanna Ullrich · March 2026
Note published · written by SkillFed’s research pipeline from the paper above · how these notes are made
AI-assisted notes · reviewed by SkillFed Skill securityResearchers crawled 238,180 unique agent skills from ClawHub, Skills.sh, SkillDirectory, and GitHub — the largest cross-platform census of the skill ecosystem so far. Every skill passed through three lenses: each marketplace's own scanner, an independent Cisco skill scanner, and the authors' own LLM-based classifier. For skills flagged high-risk by both an external scanner and their model, the team went a step further and checked the skill against the GitHub repository it actually ships from — README, code, commit history — rather than judging the SKILL.md file in isolation.
Marketplace-reported malicious rates swing from 46.8% on ClawHub down to 6.0% on SkillDirectory, and the five scanners tested barely agree with each other: among the 27,111 skills all five tools analyzed in common, only 33 (0.12%) got flagged by every one of them. Aggregating flags at the repository level roughly doubles the apparent risk, too — 19.3% of individual skills get flagged versus 45.9% of the repositories hosting them, since one bad skill is enough to tag an entire repo as malicious. Once flagged skills are checked against their home repository, the suspicious set nearly disappears: of 2,887 scanner-flagged skill-repository pairs fully evaluated, only 15 (0.52%) still looked malicious once repository alignment and maturity were factored in, and manual reviewers who checked a sample of flagged repos found nearly all of them benign. The same repository-level lens surfaced real risk elsewhere — 121 skills route through seven abandoned, hijackable GitHub repositories, and a dozen live API credentials, including NVIDIA, ElevenLabs, and MongoDB tokens, sit exposed inside published skills.
Key numbers
| Unique skills analyzed across 4 platforms | 238,180 |
| ClawHub-reported malicious rate | 46.8% |
| Flagged skills still suspicious after repo context | 0.52% (15 of 2,887) |
| Agreement across all 5 scanners tested | 0.12% (33 skills) |
| Skills exposed to repository hijacking | 121 (via 7 abandoned repos) |
Skills related to this research
Related notes
- 84% of Malicious Agent-Skill Exploits Hide in the Docs, Not the Code →
- 26.1% of Marketplace Agent Skills Carry a Vulnerability →
- Agent-skill security scanners agree on just 0.69% of all skills — each is watching a different layer →
- 1,184 skills fell in one campaign — because Agent Skills has no line between data and instructions →
- Malicious-Skill Detection Hits 0.920 F1 at $0.006 per Skill →
- Wrapping a harmful request inside a skill file nearly triples model harm scores, from 0.27 to 0.76 →
- A self-writing skill library lifts Humanity's Last Exam accuracy 116% — with zero weight updates →
- Self-generated web skills score below no skill at all — contract-based repair triples success to 28.1% →
- 90% of a Skill Marketplace's Most-Downloaded Skills Fail a Rigorous Security Audit →
References
- F. Holzbauer, D. Schmidt, G. K. Gegenhuber, S. Schrittwieser, J. Ullrich, "Context Matters: Repository-Aware Security Analysis of the Agent Skill Ecosystem," arXiv:2603.16572 (2026).
- L. Beurer-Kellner, A. Kudrinskii, M. Milanta, K. B. Nielsen, H. Sarkar, L. Tal, "Snyk Finds Prompt Injection in 36%, 1467 Malicious Payloads in a ToxicSkills Study of Agent Skills Supply Chain Compromise" (2026).
- Y. Liu, W. Wang, R. Feng, Y. Zhang, G. Xu, G. Deng, Y. Li, L. Zhang, "Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale," arXiv:2601.10338 (2026).
- G. Ling, S. Zhong, R. Huang, "Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality," arXiv:2602.08004 (2026).
- V. P. Bhardwaj, "Formal Analysis and Supply Chain Security for Agentic AI Skills," arXiv:2603.00195 (2026).