Inadequate Skill Scanning Vulnerability in Agentic Coding
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Description
Organizations that govern AI assistant skills rarely review every submission by hand, so they put a scanner in front of the intake process instead and let a clean result decide what enters the catalog. Poor Scanning is the lack of, or inadequate, security scanning for agent skills.
Impact
The most direct effect is false assurance: a clean verdict ends human review, so a malicious skill that passes the scan actually receives more trust.
There is also a slower cost. A scanner that flags too much trains reviewers to dismiss its output, which ends up being the same as missing the finding.
Scenarios
Your organization runs an internal skills registry that supports versioning and security scanning of submitted community skills. Too bad the scanner only examines the skill’s main instruction file. A submitted skill is small and clean, and points to a bundled reference document for its detailed steps. That document was never in scope, and it is where the hostile instructions live.
Prevention
If you install and use skills, do not treat a clean scan as the end of review, especially for a skill that executes commands or touches secrets. Keep a human in the loop for anything privileged, and assume something will still get through regardless of what the scanner says. Pair scanning with runtime sandboxing, egress restrictions, and monitoring of what installed skills actually do.
If you build or operate the scanner, the coverage and the judgment calls are yours to make.
- Scan the whole artifact: Scan instruction files, metadata, referenced documents, helper scripts, and any bundled asset, not just the code. Add a semantic reviewer that can reason about intent alongside signature rules. Tools such as NVIDIA SkillSpector are built for this.
- Block when scanning fails: A scanner error, a timeout, or an unsupported file type must block the install rather than fall through to approval.
- Flag capability, not just malice: Surface what the skill can reach, such as network destinations, credentials, and command execution, and let a human judge whether that fits its stated purpose. Confirm regularly, against a corpus of known-bad samples, that the scanner still catches them.