Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale

This academic study presents the first large-scale empirical security analysis of AI agent skills—modular packages that extend agent capabilities through instructions and executable code. Analyzing 31,132 unique skills from two major marketplaces (skills.rest and skillsmp.com) using SKILLSCAN, a multi-stage detection framework combining static analysis with LLM-based classification, the researchers found that 26.1% of skills contain at least one vulnerability across 14 distinct patterns in four categories: prompt injection, data exfiltration, privilege escalation, and supply chain risks. 

Data exfiltration (13.3%) and privilege escalation (11.8%) were most prevalent, while 5.2% exhibited high-severity patterns suggesting malicious intent. Skills bundling executable scripts were 2.12× more likely to contain vulnerabilities than instruction-only skills (OR=2.12, p<0.001). 

The framework achieved 86.7% precision and 82.5% recall against manually annotated ground truth. The study provides a grounded vulnerability taxonomy, validated detection methodology, and open dataset. It concludes that the agent skills ecosystem mirrors early browser extension security risks, with implicit trust and minimal vetting creating urgent needs for capability-based permission systems, mandatory security reviews, and runtime sandboxing. 

https://arxiv.org/html/2601.10338v1

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