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

arXiv: https://arxiv.org/abs/2601.10338 · HTML: https://mlsanity.com/paper/2601.10338/

## Key facts

- Cited by 105 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 6 of 6)

- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [Backstabber's Knife Collection: A Review of Open Source Software Supply Chain Attacks](https://mlsanity.com/paper/2005.09535/) (2020)
- [Agent Skills Enable a New Class of Realistic and Trivially Simple Prompt Injections](https://mlsanity.com/paper/2510.26328/) (2025)
- [Killing Two Birds with One Stone: Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence](https://mlsanity.com/paper/2309.02637/) (2023)
- [Developers Are Victims Too : A Comprehensive Analysis of The VS Code Extension Ecosystem](https://mlsanity.com/paper/2411.07479/) (2024)
- [A Study on Malicious Browser Extensions in 2025](https://mlsanity.com/paper/2503.04292/) (2025)

## Built on by (most cited first, 30 of 105)

- [Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward](https://mlsanity.com/paper/2602.12430/) (2026)
- [Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering](https://mlsanity.com/paper/2604.08224/) (2026)
- [Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality](https://mlsanity.com/paper/2602.08004/) (2026)
- [SkillNet: Create, Evaluate, and Connect AI Skills](https://mlsanity.com/paper/2603.04448/) (2026)
- ["Do Not Mention This to the User": Detecting and Understanding Malicious Agent Skills in the Wild](https://mlsanity.com/paper/2602.06547/) (2026)
- [Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems](https://mlsanity.com/paper/2604.03081/) (2026)
- [SkillReducer: Optimizing LLM Agent Skills for Token Efficiency](https://mlsanity.com/paper/2603.29919/) (2026)
- [ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces](https://mlsanity.com/paper/2604.05172/) (2026)
- [SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement](https://mlsanity.com/paper/2604.04989/) (2026)
- [Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats](https://mlsanity.com/paper/2603.11619/) (2026)
- [SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents](https://mlsanity.com/paper/2602.14211/) (2026)
- [Formal Analysis and Supply Chain Security for Agentic AI Skills](https://mlsanity.com/paper/2603.00195/) (2026)
- [SkillProbe: Security Auditing for Emerging Agent Skill Marketplaces via Multi-Agent Collaboration](https://mlsanity.com/paper/2603.21019/) (2026)
- [Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw](https://mlsanity.com/paper/2604.04759/) (2026)
- [SkillSieve: A Hierarchical Triage Framework for Detecting Malicious AI Agent Skills](https://mlsanity.com/paper/2604.06550/) (2026)
- [How Your Credentials Are Leaked by LLM Agent Skills: An Empirical Study](https://mlsanity.com/paper/2604.03070/) (2026)
- [SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces](https://mlsanity.com/paper/2605.12015/) (2026)
- [MalSkillBench: A Runtime-Verified Benchmark of Malicious Agent Skills](https://mlsanity.com/paper/2606.07131/) (2026)
- [When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems](https://mlsanity.com/paper/2606.00448/) (2026)
- [From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills](https://mlsanity.com/paper/2604.24026/) (2026)
- [Towards Secure Agent Skills: Architecture, Threat Taxonomy, and Security Analysis](https://mlsanity.com/paper/2604.02837/) (2026)
- [Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain](https://mlsanity.com/paper/2604.08407/) (2026)
- [Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry](https://mlsanity.com/paper/2605.11418/) (2026)
- [AgentTrap: Measuring Runtime Trust Failures in Third-Party Agent Skills](https://mlsanity.com/paper/2605.13940/) (2026)
- [Context Matters: Repository-Aware Security Analysis of the Agent Skill Ecosystem](https://mlsanity.com/paper/2603.16572/) (2026)
- [Cloak and Detonate: Scanner Evasion and Dynamic Detection of Agent Skill Malware](https://mlsanity.com/paper/2607.02357/) (2026)
- [VIGIL: Runtime Enforcement of Behavioral Specifications in AI Agent Skills](https://mlsanity.com/paper/2606.26524/) (2026)
- [Trojan's Whisper: Stealthy Manipulation of OpenClaw through Injected Bootstrapped Guidance](https://mlsanity.com/paper/2603.19974/) (2026)
- [RouteGuard: Internal-Signal Detection of Skill Poisoning in LLM Agents](https://mlsanity.com/paper/2604.22888/) (2026)
- [Structured Security Auditing and Robustness Enhancement for Untrusted Agent Skills](https://mlsanity.com/paper/2604.25109/) (2026)

## Abstract (arXiv)

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.