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sast-configuration

Configure Static Application Security Testing (SAST) tools for automated vulnerability detection in application code. Use when setting up security scanning, implementing DevSecOps practices, or automating code vulnerability detection.

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wshobson/agents
最近来源活动
2026年9月28日 23:06
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SKILL.md
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name
sast-configuration
description
Configure Static Application Security Testing (SAST) tools for automated vulnerability detection in application code. Use when setting up security scanning, implementing DevSecOps practices, or automating code vulnerability detection.
# SAST Configuration Static Application Security Testing (SAST) tool setup, configuration, and custom rule creation for comprehensive security scanning across multiple programming languages. ## Overview This skill provides comprehensive guidance for setting up and configuring SAST tools including Semgrep, SonarQube, and CodeQL. Use this skill when you need to: - Set up SAST scanning in CI/CD pipelines - Create custom security rules for your codebase - Configure quality gates and compliance policies - Optimize scan performance and reduce false positives - Integrate multiple SAST tools for defense-in-depth ## Core Capabilities ### 1. Semgrep Configuration - Custom rule creation with pattern matching - Language-specific security rules (Python, JavaScript, Go, Java, etc.) - CI/CD integration (GitHub Actions, GitLab CI, Jenkins) - False positive tuning and rule optimization - Organizational policy enforcement ### 2. SonarQube Setup - Quality gate configuration - Security hotspot analysis - Code coverage and technical debt tracking - Custom quality profiles for languages - Enterprise integration with LDAP/SAML ### 3. CodeQL Analysis - GitHub Advanced Security integration - Custom query development - Vulnerability variant analysis - Security research workflows - SARIF result processing ## Quick Start ### Initial Assessment 1. Identify primary programming languages in your codebase 2. Determine compliance requirements (PCI-DSS, SOC 2, etc.) 3. Choose SAST tool based on language support and integration needs 4. Review baseline scan to understand current security posture ### Basic Setup ```bash # Semgrep quick start pip install semgrep semgrep --config=auto --error # SonarQube with Docker docker run -d --name sonarqube -p 9000:9000 sonarqube:10.8-community # CodeQL CLI setup gh extension install github/gh-codeql codeql database create mydb --language=python ``` ## Integration Patterns ### CI/CD Pipeline Integration ```yaml # GitHub Actions example - name: Run Semgrep uses: returntocorp/semgrep-action@v1 with: config: >- p/security-audit p/owasp-top-ten ``` ### Pre-commit Hook ```bash # .pre-commit-config.yaml - repo: https://github.com/returntocorp/semgrep rev: v1.45.0 hooks: - id: semgrep args: ['--config=auto', '--error'] ``` ## Best Practices 1. **Start with Baseline** - Run initial scan to establish security baseline - Prioritize critical and high severity findings - Create remediation roadmap 2. **Incremental Adoption** - Begin with security-focused rules - Gradually add code quality rules - Implement blocking only for critical issues 3. **False Positive Management** - Document legitimate suppressions - Create allow lists for known safe patterns - Regularly review suppressed findings 4. **Performance Optimization** - Exclude test files and generated code - Use incremental scanning for large codebases - Cache scan results in CI/CD 5. **Team Enablement** - Provide security training for developers - Create internal documentation for common patterns - Establish security champions program ## Common Use Cases ### New Project Setup ```bash ./scripts/run-sast.sh --setup --language python --tools semgrep,sonarqube ``` ### Custom Rule Development ```yaml # See references/semgrep-rules.md for detailed examples rules: - id: hardcoded-jwt-secret pattern: jwt.encode($DATA, "...", ...) message: JWT secret should not be hardcoded severity: ERROR ``` ### Compliance Scanning ```bash # PCI-DSS focused scan semgrep --config p/pci-dss --json -o pci-scan-results.json ``` ## Troubleshooting ### High False Positive Rate - Review and tune rule sensitivity - Add path filters to exclude test files - Use nostmt metadata for noisy patterns - Create organization-specific rule exceptions ### Performance Issues - Enable incremental scanning - Parallelize scans across modules - Optimize rule patterns for efficiency - Cache dependencies and scan results ### Integration Failures - Verify API tokens and credentials - Check network connectivity and proxy settings - Review SARIF output format compatibility - Validate CI/CD runner permissions ## Related Skills - [STRIDE Analysis Patterns](../stride-analysis-patterns/SKILL.md) - [Threat Mitigation Mapping](../threat-mitigation-mapping/SKILL.md) ## Tool Comparison | Tool | Best For | Language Support | Cost | Integration | | --------- | ------------------------ | ---------------- | --------------- | ------------- | | Semgrep | Custom rules, fast scans | 30+ languages | Free/Enterprise | Excellent | | SonarQube | Code quality + security | 25+ languages | Free/Commercial | Good | | CodeQL | Deep analysis, research | 10+ languages | Free (OSS) | GitHub native | ## Next Steps 1. Complete initial SAST tool setup 2. Run baseline security scan 3. Create custom rules for organization-specific patterns 4. Integrate into CI/CD pipeline 5. Establish security gate policies 6. Train development team on findings and remediation
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