Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale
The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend agent capabilities. While this architecture enables powerful customization, skills execute with implicit trust and minimal vetting, creating a significant yet uncharacterized attack surface. We conduct the first large-scale empirical security analysis of this emerging ecosystem, collecting 42,447 skills from two major marketplaces and systematically analyzing 31,132 using SkillScan, a multi-stage detection framework integrating static analysis with LLM-based semantic classification. Our findings reveal pervasive security risks: 26.1% of skills contain at least one vulnerability, spanning 14 distinct patterns across four categories: prompt injection, data exfiltration, privilege escalation, and supply chain risks. Data exfiltration (13.3%) and privilege escalation (11.8%) are most prevalent, while 5.2% of skills exhibit high-severity patterns strongly suggesting malicious intent. We find that skills bundling executable scripts are 2.12x more likely to contain vulnerabilities than instruction-only skills (OR=2.12, p<0.001). Our contributions include: (1) a grounded vulnerability taxonomy derived from 8,126 vulnerable skills, (2) a validated detection methodology achieving 86.7% precision and 82.5% recall, and (3) an open dataset and detection toolkit to support future research. These results demonstrate an urgent need for capability-based permission systems and mandatory security vetting before this attack vector is further exploited.
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Yi Liu
admin_assigned
2026-01-16T15:41:52.558Z
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YI LIU
sumleo
user
sumleo
696a2fa2b23c50a8fcb39c2e
Weizhe Wang
696a2fa2b23c50a8fcb39c2f
Ruitao Feng
696a2fa2b23c50a8fcb39c30
Yao Zhang
admin_assigned
2026-01-16T15:42:12.937Z
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Zhang Yao
yaozhang
user
yaozhang
696a2fa2b23c50a8fcb39c31
Guangquan Xu
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Gelei Deng
admin_assigned
2026-01-16T15:42:29.738Z
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Deng
Gelei
user
Gelei
696a2fa2b23c50a8fcb39c33
Yuekang Li
696a2fa2b23c50a8fcb39c34
Leo Zhang
Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale
The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend agent capabilities. While this architecture enables powerful customization, skills execute with implicit trust and minimal vetting, creating a significant yet uncharacterized attack surface. We conduct the first large-scale empirical security analysis of this emerging ecosystem, collecting 42,447 skills from two major marketplaces and systematically analyzing 31,132 using SkillScan, a multi-stage detection framework integrating static analysis with LLM-based semantic classification. Our findings reveal pervasive security risks: 26.1% of skills contain at least one vulnerability, spanning 14 distinct patterns across four categories: prompt injection, data exfiltration, privilege escalation, and supply chain risks. Data exfiltration (13.3%) and privilege escalation (11.8%) are most prevalent, while 5.2% of skills exhibit high-severity patterns strongly suggesting malicious intent. We find that skills bundling executable scripts are 2.12x more likely to contain vulnerabilities than instruction-only skills (OR=2.12, p<0.001). Our contributions include: (1) a grounded vulnerability taxonomy derived from 8,126 vulnerable skills, (2) a validated detection methodology achieving 86.7% precision and 82.5% recall, and (3) an open dataset and detection toolkit to support future research. These results demonstrate an urgent need for capability-based permission systems and mandatory security vetting before this attack vector is further exploited.
Large-scale security analysis of AI agent skills reveals widespread vulnerabilities including prompt injection, data exfiltration, and privilege escalation risks.
AI agent frameworks agent skills security analysis vulnerability detection prompt injection data exfiltration privilege escalation supply chain risks LLM-based semantic classification static analysis SkillScan vulnerability taxonomy permission systems