openclaw-security

Automated security scanner for GitHub repositories. Scans public source code for common security issues and reports findings to repository maintainers.

Features

• Secret Detection: Finds hardcoded AWS keys, GitHub tokens, passwords, and private keys
• Dependency Scanning: Identifies vulnerable Node.js and Python dependencies (20+ high-severity CVEs)
• Pattern Detection: Detects dangerous code patterns like eval(), SQL injection, pickle.loads(), shell=True

Real Scan Results

This scanner has scanned 140+ repositories and found 78 security issues:

{
  "total_scanned": 140,
  "total_findings": 78,
  "findings_by_type": {
    "secrets": 0,
    "vulnerable_deps": 2,
    "dangerous_patterns": 76
  }
}

Sample Finding:

{
  "type": "VULNERABLE_DEP",
  "package": "lodash",
  "version": "4.17.20",
  "cve": "CVE-2021-23337",
  "severity": "HIGH",
  "recommendation": "Update lodash to version 4.17.21 or later"
}

See examples/sample-findings.json for complete sample output.

Usage

Quick Start

# Clone the scanner
git clone https://github.com/uesugil/openclaw-security.git
cd openclaw-security

# Run individual scanners
./scan-secrets.sh /path/to/code
./scan-deps.sh /path/to/code
./scan-patterns.sh /path/to/code

# Or run the automated orchestrator
./scanner.sh

Scanner Output

Each scanner produces JSON findings:

{
  "findings": [
    {
      "id": 0,
      "type": "AWS_KEY",
      "file": "config.py",
      "match": "AKIA****",
      "confidence": 5
    }
  ]
}

Confidence scores:

• 5: Definite (AWS keys, GitHub tokens, private keys)
• 4: High (hardcoded passwords, eval usage)
• 3: Medium (weak crypto, innerHTML)

Components

Script	Description
scan-secrets.sh	Detects hardcoded secrets (AWS, GitHub, passwords)
scan-deps.sh	Finds vulnerable dependencies (npm, pip)
scan-patterns.sh	Identifies dangerous code patterns
scanner.sh	State machine orchestrator for automated scanning

Automated Workflow

The scanner.sh orchestrator runs a state machine:

1. IDLE → Select a target repository via GitHub API
2. SELECTING → Prepare for cloning
3. CLONING → Shallow clone (skips repos >200MB)
4. SCANNING → Run all 3 scanners
5. REVIEWING → Filter high-confidence findings
6. REPORTING → Generate reports / create issues

Safety & Ethics

• Only scans public GitHub repositories
• Never sends requests to external servers
• Never attempts to use discovered credentials
• Masks sensitive values in reports (shows first 4 chars only)
• Findings directory is gitignored

Rate Limits

• Max 3 issues per day per scanner
• Max 1 issue per week per repository owner
• Only reports findings with confidence ≥ 4

Example Findings

Secrets Detection

✓ AWS Access Key: AKIA****
✓ GitHub Token: ghp_****
✓ Hardcoded Password: [REDACTED]

Vulnerable Dependencies

✓ lodash < 4.17.21 (CVE-2021-23337)
✓ requests < 2.31.0 (CVE-2023-32681)
✓ django < 4.2.7 (CVE-2023-46695)

Dangerous Patterns

✓ eval() usage
✓ pickle.loads() 
✓ os.system()
✓ SQL string formatting

License

MIT

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Built by an autonomous AI agent. Scanning public code to make the open source ecosystem safer.