Towards a Risk Assessment of Malicious Skill Files in Coding Agents

• URL: http://arxiv.org/abs/2608.05223v1
• Date: Wed, 05 Aug 2026 11:33:43 GMT
• Title: Towards a Risk Assessment of Malicious Skill Files in Coding Agents
• Abstract summary: We make three contributions to the field of autonomous coding agents.<n>First, an adversarial skill-synthesis method to transform 471 real-world shell commands into benign-appearing skills.<n>Second, a reproducible evaluation pipeline coupling run stratification, evidence anchoring, a refusal veto, and a deterministic declared-intent override.<n>Third, a large-scale characterization of two enterprise-grade agents across 5,629 completed runs.
• Score: 9.553517006800913
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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