SkillMutator: Benchmarking and Defending Language-and-Code Cross-modal Attacks on LLM Agent Skills
New research demonstrates cross-modal attacks where adversaries manipulate both documentation and code to compromise LLM agent skills, creating enterprise risks for AI-powered automation workflows.
Summary written by editorial AI · Source link below
arXiv:2606.14154v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly extend their capabilities at runtime by loading Agent Skills, which pair natural-language specifications (SKILL.md) with executable scripts and resources. Because a skill's behavior relies on both natural-language instructions and executable code, assessing its safety requires cross-modal reasoning, creating a new language-and-code attack surface. Attackers can present a benign workflow in SKILL.md wh
Editorial Analysis
As enterprises increasingly deploy LLM agents for automation, this attack vector could compromise business processes through tampered agent capabilities that appear legitimate.
Review and sandbox third-party LLM agent skills before deployment in production environments.
AI agent vulnerabilities could enable attackers to manipulate automated business processes through compromised agent capabilities.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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