Towards a Risk Assessment of Malicious Skill Files in Coding Agents
Rui Yang, Michael Fu, Kla Tantithamthavorn, Chetan Arora, Joey Chua
cs.SE, cs.CR
Submitted: 2026-08-05
Comments: 29 pages, 6 figures, 6 tables. Preprint; under review
Code: https://github.com/awsm-research/AgentJailbreak
License: http://creativecommons.org/licenses/by/4.0/
The gist: Autonomous coding agents are increasingly embedded in enterprise software workflows with delegated authority over connected systems.
Terminology
Abstract
Autonomous coding agents are increasingly embedded in enterprise software workflows with delegated authority over connected systems. Central to this architecture is the agent skills interface: folders of instructions and scripts that agents load dynamically to specialize their behavior. This interface also widens the attack surface, letting malicious shell commands hide within natural-language skill files. We make three contributions. First, an adversarial skill-synthesis method using six LLMs across four families to transform 471 real-world shell commands into benign-appearing skills, released as a benchmark of 2,826 skills mapped to 11 MITRE ATT&CK tactics. Second, a reproducible evaluation pipeline coupling run stratification, evidence anchoring, a refusal veto, and a deterministic declared-intent override with a three-judge LLM-as-a-judge panel, validated against a blind human gold standard (Cohen's kappa = 0.85). Third, a large-scale characterization of two enterprise-grade agents across 5,629 completed runs. Gemini CLI is exploited in 95.5-96.1% of runs and Qwen Code in 71.6-74.0% (raw majority vote to declared-intent-corrected estimate, both within the human gold standard), nearly invariant to the generating model. Explicit safety recognition occurs in only 1.99% of runs. Enterprises must assess and mitigate skill-interface risk before adopting coding agents. Our code and dataset are available at https://github.com/awsm-research/AgentJailbreak
Sources
- GPT-4 Technical Report
- On the Opportunities and Risks of Foundation Models
- Evaluating Large Language Models Trained on Code
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
- SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
- Memory in the Age of AI Agents
- Understanding the planning of LLM agents: A survey
- Qwen2.5-Coder Technical Report
- SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
- OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents
- SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
- Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality
- "Do Not Mention This to the User": Detecting and Understanding Malicious Agent Skills in the Wild
- The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey
- Composer 2 Technical Report
- A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents
- Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward
- Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
- Engineering Pitfalls in AI Coding Tools: An Empirical Study of Bugs in Claude Code, Codex, and Gemini CLI
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- PackMonitor: Enabling Zero Package Hallucinations Through Decoding-Time Monitoring
- IntentCoding: Amplifying User Intent in Code Generation