# Security Scanning Implementation

This document describes the automated security scanning system implemented for the agentic-devtools repository.

## Overview

An automated security scanning workflow that runs on every merge to the main branch, using specialized AI agents and multiple security tools to detect vulnerabilities and potential threats.

## Components

### 1. Security Scanning Agent

**Location**: `.github/agents/security-scan.agent.md`

A specialized Copilot agent with expertise in security vulnerability detection and analysis.

**Configuration**:

- **Model**: claude-opus-4.5 (high capability for complex security analysis)
- **Temperature**: 0.3 (deterministic for consistent security analysis)
- **Max Tokens**: 8192
- **Tools**: view, grep, glob, bash, web_search, github-mcp-server

**Capabilities**:

- Code vulnerability analysis (SQL injection, XSS, CSRF, command injection)
- Authentication and authorization issue detection
- Data security and encryption assessment
- Dependency vulnerability scanning
- Configuration security review
- Input validation analysis
- API security evaluation

### 2. GitHub Actions Workflow

**Location**: `.github/workflows/security-scan-on-merge.yml`

**Trigger**: Push to main branch (after PR merge)

**Security Tools**:

1. **pip-audit**: Scans Python dependencies for known CVEs
2. **bandit**: Static analysis security testing for Python code
3. **safety scan**: Checks dependencies against vulnerability database

**Workflow Steps**:

1. Checkout code with full history
2. Set up Python 3.11 environment
3. Install dependencies and security tools
4. Capture merge/PR information
5. Run pip-audit for dependency vulnerabilities
6. Run bandit for code security issues
7. Run safety scan for dependency security
8. Create GitHub issue with scan results
9. Upload detailed reports as artifacts (90-day retention)

**Outputs**:

- **GitHub Issue**: Automatically created with scan summary
- **Labels**: `security`, `security-scan`, plus `needs-review` or `all-clear`
- **Artifacts**: JSON reports (pip-audit-report.json, bandit-report.json, safety-report.json)

## Usage

### Automatic Execution

The workflow runs automatically on every merge to main. No manual intervention required.

### Reviewing Scan Results

When a security scan completes:

1. Check for new GitHub issues labeled `security-scan`
2. Review the scan summary in the issue
3. For detailed findings, check the workflow logs or download artifacts
4. Address critical and high-severity issues immediately
5. Tag @copilot in the issue for assistance with remediation

### Manual Security Scan

To run security scans manually:

```bash
# Install security tools
pip install bandit safety pip-audit

# Run dependency vulnerability scan
pip-audit

# Run static security analysis
bandit -r agentic_devtools

# Run dependency safety check
safety scan
```

## Security Issue Labels

- **security**: All security-related issues
- **security-scan**: Identifies automated scan results
- **needs-review**: Findings detected, review required
- **all-clear**: No security issues detected

## Scan Report Format

Each security scan issue includes:

### Summary Section

- Date and commit information
- Associated PR (if applicable)
- Overall security status (✅ all clear or ⚠️ findings detected)

### Scan Results Section

Individual results from each tool:

- **pip-audit**: Dependency vulnerabilities
- **bandit**: Code security issues with severity levels
- **safety**: Dependency safety analysis

### Next Steps Section

Actionable recommendations:

- Review workflow logs for details
- Address critical/high-severity issues
- Create tickets for medium-priority items
- Tag @copilot for remediation assistance

## Security Tool Details

### pip-audit

- **Purpose**: Scans Python dependencies for known security vulnerabilities
- **Database**: PyPI Advisory Database and OSV
- **Output**: JSON format with CVE details
- **Severity Levels**: Based on CVSS scores

### bandit

- **Purpose**: Finds common security issues in Python code
- **Checks**: 100+ security patterns
- **Severity Levels**: Low, Medium, High
- **Confidence Levels**: Low, Medium, High
- **Examples**: Hardcoded passwords, SQL injection, pickle usage, eval usage

### safety

- **Purpose**: Checks dependencies against safety vulnerability database
- **Database**: Safety DB (curated security advisories)
- **Output**: JSON format with vulnerability details
- **Features**: Transitive dependency checking

## Best Practices

1. **Review promptly**: Address security findings within 24 hours
2. **Prioritize by severity**: Focus on Critical and High issues first
3. **Update dependencies**: Keep dependencies current to avoid known vulnerabilities
4. **Use @copilot**: Tag the security agent for remediation guidance
5. **Document exceptions**: If a finding is a false positive, document why
6. **Monitor trends**: Track security issues over time to identify patterns

## Future Enhancements

Potential improvements to the security scanning system:

- **CodeQL integration**: Advanced semantic code analysis
- **Container scanning**: Docker image vulnerability scanning
- **DAST integration**: Dynamic application security testing
- **Secrets scanning**: Advanced secret detection (e.g., TruffleHog)
- **Security dashboards**: Aggregated security metrics and trends
- **Automated remediation**: PR creation for dependency updates
- **Severity thresholds**: Block merges based on severity levels

## References

- [pip-audit documentation](https://github.com/pypa/pip-audit)
- [bandit documentation](https://bandit.readthedocs.io/)
- [safety documentation](https://docs.safetycli.com/)
- [OWASP Top 10](https://owasp.org/www-project-top-ten/)
- [CWE (Common Weakness Enumeration)](https://cwe.mitre.org/)
- [CVE (Common Vulnerabilities and Exposures)](https://cve.mitre.org/)

## Support

For questions or issues with the security scanning system:

1. Review this documentation
2. Check workflow logs in GitHub Actions
3. Create an issue with the `security` label
4. Tag @copilot for assistance

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*Last Updated: 2026-02-13*
