Audit OpenClaw agent skills for security vulnerabilities and malicious behavior.
npx skills add https://github.com/NY1024/ClawGuard --skill auditor-skill
Please help me install this Agent Skill. Skill: auditor-skill Source: https://github.com/NY1024/ClawGuard/tree/main/auditor-skill Command: npx skills add https://github.com/NY1024/ClawGuard --skill auditor-skill
Autonomous AI agents executing untrusted third-party skills risk critical security threats including prompt injection, credential exfiltration, privilege escalation, and resource exhaustion. ClawGuard Auditor solves this by providing comprehensive pre-installation and runtime security vetting for OpenClaw agent skills, blocking malicious code before it can cause harm.
Use the auditor-skill to audit the skill located at ./downloads/suspicious-tool before installing it to your OpenClaw workspace.
High-intent search queries and answers about installing and using this skill.
To audit an OpenClaw agent skill for security vulnerabilities before installing, use the auditor-skill to run static application security testing, semantic intent matching, and supply chain dependency verification against the target skill code.
Sandbox execution for malware detection isolates and executes untrusted AI agent skills in a controlled environment, monitoring runtime behavior to identify malicious actions like data exfiltration or privilege escalation before installation.
Yes, you can check third-party skill dependencies for supply chain security risks. The auditor-skill verifies dependencies against known vulnerabilities, typosquatting patterns, and malicious packages to ensure safe installation.
To detect prompt injection and deceptive behavior in untrusted agent skills, apply semantic intent matching. This technique compares a skill's stated purpose against its actual code behavior to flag deceptive tools.
Yes, static analysis detects dynamic code execution and reverse shells in skill repositories. Advanced SAST analysis scans skill code for dangerous patterns including sensitive file access and unauthorized network connections.
The best way to vet unknown GitHub repositories for AI agent resource exhaustion attacks is combining static code analysis with ML-based anomaly detection to identify novel attack patterns not covered by static rule sets.
AytsuuAudit source code files, detect third-party libraries, and generate four-level module verdicts.
UseAI-proAudit npm, pip, and Go dependencies for vulnerabilities and typosquatting.
MonumentalSystemsScan application code, dependencies, and container images for security vulnerabilities.
hypnguyen1209Dissect malware samples to produce threat intelligence and IOCs.
