Sealing the Audit-Runtime Gap for LLM Skills
Research formalises the trust gap between audited and runtime behaviour of LLM skill packages in ecosystems like Claude Code, highlighting a supply-chain-like risk that EU AI Act conformity processes must address.
Summary written by editorial AI · Source link below
arXiv:2605.05274v2 Announce Type: replace Abstract: Large language model (LLM) ecosystems such as Claude Code and ChatGPT increasingly rely on skills: packages of natural-language instructions and executable tools. Once in the LLM's context, skill content cannot be reliably separated from trusted instructions, and a skill's executable side can invoke privileged actions, exposing the skill supply chain to injection, tampering, and rug-pull attacks. Existing defenses are stage-bound: centralized
Editorial Analysis
As enterprises adopt LLM-based coding assistants with skill/plugin ecosystems, the inability to enforce trust boundaries between instructions and tools creates a novel supply-chain attack surface.
Audit all LLM skill and plugin packages currently in use for provenance, integrity, and runtime behaviour divergence from their audited specifications.
LLM tool ecosystems have a structural trust gap between what is audited and what executes at runtime, creating governance and supply-chain risks for AI-enabled operations.
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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