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Developer Builds Open-Source Security Framework to Detect Malicious AI Agent Skills

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A developer has released 'agent-skills-guard', a static analysis framework designed to detect security threats hidden inside AI agent skill files used by tools like Claude and GitHub Copilot. The framework scans entire skill definition files, including metadata fields like descriptions, catching injected instructions that silently direct agents to leak data without user awareness. In testing, the tool successfully flagged two high-severity prompt-injection phrases embedded solely within a skill's description field, with no malicious code present elsewhere. Detection rules are stored in a separate JSON file rather than hardcoded, making it easier for users to extend the scanner with custom patterns. The developer acknowledged two current limitations: the tool cannot yet detect skills crafted to over-trigger through persuasive but non-malicious wording, and it has no mechanism to alert users when a previously approved skill is silently updated after installation.

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