Skill Security Audit

Detect malicious patterns in installed Claude and OpenClaw skills. Based on SlowMist’s analysis of 472+ malicious skills on ClawHub platform.

Triggers

Use this skill when the user mentions: 安全审计, security audit, skill 检查, 技能安全, scan skills, supply chain security, 扫描技能, 恶意检测, malicious skill, skill 安全扫描

Quick Audit Workflow

When the user requests a security audit, follow these 5 steps:

Step 1: Run the Scanner

python3 ~/.claude/skills/skill-security-audit/scripts/skill_audit.py

This auto-discovers and scans all skills in:

• ~/.claude/skills/
• ~/.openclaw/workspace/skills/
• Extra directories from ~/.openclaw/openclaw.json → skills.load.extraDirs

Step 2: Analyze Results

Read the scanner output. Findings are grouped by skill and sorted by severity:

Step 3: Report to User

Present findings in this format:

## Audit Summary
- Skills scanned: N
- Files scanned: N
- CRITICAL: N | HIGH: N | MEDIUM: N | LOW: N

## Critical/High Findings (if any)
For each finding:
- Skill name and file path
- What was detected and why it's dangerous
- Recommended action

## Medium/Low Findings (if any)
Brief summary, noting which are likely false positives

Step 4: Recommend Actions

For CRITICAL findings:

1. Read references/remediation-guide.md for incident response steps
2. Guide user through credential rotation if credential theft was detected
3. Help quarantine the malicious skill

For HIGH findings:

1. Help user manually review the flagged code
2. Determine if the pattern is legitimate or malicious in context

Step 5: Follow Up

• Offer to scan a specific skill in detail: python3 skill_audit.py --path /path/to/skill
• Offer to explain any finding in depth using references/threat-patterns.md

Scanner Command Reference

# Scan all discovered skills
python3 ~/.claude/skills/skill-security-audit/scripts/skill_audit.py

# Scan a single skill directory
python3 ~/.claude/skills/skill-security-audit/scripts/skill_audit.py --path /path/to/skill

# JSON output (for programmatic use)
python3 ~/.claude/skills/skill-security-audit/scripts/skill_audit.py --json

# Filter by minimum severity
python3 ~/.claude/skills/skill-security-audit/scripts/skill_audit.py --severity high

# Disable colored output
python3 ~/.claude/skills/skill-security-audit/scripts/skill_audit.py --no-color

# Use custom IOC database
python3 ~/.claude/skills/skill-security-audit/scripts/skill_audit.py --ioc-db /path/to/ioc.json

Exit codes: 0 = clean, 1 = low/medium risk, 2 = high risk, 3 = critical, 4 = scanner error

13 Detection Categories

Understanding Confidence Scores

Each finding includes a confidence score (0-100):

• 80-100: Very likely a genuine threat
• 50-79: Suspicious, manual review recommended
• 30-49: Possible false positive, check context
• <30: Informational, low confidence

Manual Review Checklist

When the scanner flags something, also check:

1. Source verification — Is the skill from an official/verified source? Check author reputation.
2. Permission scope — Does the skill request more permissions than its stated functionality needs?
3. Script audit — Read all .sh, .py, .js files. Look for obfuscation, unexpected network calls.
4. Dependency check — Run npm audit or pip-audit if the skill has package dependencies.
5. Changelog review — Were suspicious changes introduced in a recent update?

Updating the IOC Database

The IOC database is at scripts/ioc_database.json. To add new indicators:

1. Edit the JSON file following the existing schema
2. Run the scanner to verify your new IOCs are detected
3. Update references/ioc-database.md to keep the human-readable version in sync

Reference Documents

For detailed information, read these files as needed:

• references/ioc-database.md — Full IOC list with context and attribution
• references/threat-patterns.md — 9 attack patterns in detail (two-stage payload, Base64 backdoor, password phishing, etc.)
• references/remediation-guide.md — Step-by-step incident response (quarantine, credential rotation, persistence cleanup, reporting)