Why Your “Skill Scanner” Is Just False Security (and Maybe Malware

AI Skill Scanners, designed to mitigate security risks such as data exfiltration and prompt injection in AI systems, face significant limitations when relying on simple pattern matching like regex. Traditional methods of identifying vulnerabilities based on structured code do not translate well to the dynamic and nuanced nature of AI skills, which blend natural language prompts with code execution. This inadequacy is highlighted by the failure of current scanners like SkillGuard, Skill Defender, and Ferret Scan to effectively identify malicious skills, as they either mistakenly flag themselves as threats or overlook new patterns of malicious behavior. The text advocates for a shift from static pattern detection to a behavioral analysis approach, utilizing AI-native security solutions such as Snyk's Evo platform, which employs LLM-based intent analysis to understand the underlying capabilities and potential risks of AI skills. This approach emphasizes the necessity of constant monitoring and understanding of the intent behind AI commands to ensure comprehensive security, surpassing the limitations of traditional keyword-based scanning.