ClawSecure - The AI-Powered Antivirus for AI Agents

@divyanshu17 Great question. Pre-install, our engine analyzes what data surfaces a skill is attempting to access and whether those access patterns are justified by what the skill claims to do. A skill declaring it needs clipboard access for a code formatting task but also reaching into MEMORY.md and making outbound requests to external endpoints gets flagged because the data access doesn't match the declared intent. Post-install, runtime monitoring maps the full permission surface and blast radius of your entire agent environment, showing exactly what each component can reach, so you have visibility into the data exposure layer, not just the threat detection layer.

@zerotox We open-source the research, not the detection rules. Our public GitHub repo has full OWASP ASI mapping, findings methodology, and security documentation. We also publish a Trust Center, vulnerability disclosure policy with safe harbor, NIST AI RMF alignment, and our CSA STAR Registry listing. Transparency of methodology and results is something we take seriously.

But the proprietary detection signatures and behavioral analysis logic stay closed, and that's intentional. Publishing the exact patterns our engine uses to catch threats would give malicious skill authors a blueprint to craft evasions. That's the same reason every serious security company keeps detection logic proprietary while making their tools widely accessible. Our scanner is free with zero restrictions. The research is public. The detection engine that keeps users safe stays protected.