Implements Issue #5 - Python SAST workflow that combines:
- Dependency scanning (pip-audit) for CVE detection
- Security linting (Bandit) for vulnerability patterns
- Type checking (Mypy) for type safety issues
## Changes
**New Modules:**
- `DependencyScanner`: Scans Python dependencies for known CVEs using pip-audit
- `BanditAnalyzer`: Analyzes Python code for security issues using Bandit
- `MypyAnalyzer`: Checks Python code for type safety issues using Mypy
**New Workflow:**
- `python_sast`: Temporal workflow that orchestrates all three SAST tools
- Runs tools in parallel for fast feedback (3-5 min vs hours for fuzzing)
- Generates unified SARIF report with findings from all tools
- Supports configurable severity/confidence thresholds
**Updates:**
- Added SAST dependencies to Python worker (bandit, pip-audit, mypy)
- Updated module __init__.py files to export new analyzers
- Added type_errors.py test file to vulnerable_app for Mypy validation
## Testing
Workflow tested successfully on vulnerable_app:
- ✅ Bandit: Detected 9 security issues (command injection, unsafe functions)
- ✅ Mypy: Detected 5 type errors
- ✅ DependencyScanner: Ran successfully (no CVEs in test dependencies)
- ✅ SARIF export: Generated valid SARIF with 14 total findings
The volume_mode parameter is no longer used since workflows now upload files to MinIO storage instead of mounting volumes directly. This commit removes all references to volume_mode from:
- Backend API documentation (README.md)
- Tutorial getting started guide
- MCP integration guide
- CLI AI reference documentation
- SDK documentation and examples
- Test project documentation
All curl examples and code samples have been updated to reflect the current MinIO-based file upload approach.
Add three production-ready secret detection workflows with full benchmarking infrastructure:
**New Workflows:**
- gitleaks_detection: Pattern-based secret scanning (13/32 benchmark secrets)
- trufflehog_detection: Entropy-based detection with verification (1/32 benchmark secrets)
- llm_secret_detection: AI-powered semantic analysis (32/32 benchmark secrets - 100% recall)
**Benchmarking Infrastructure:**
- Ground truth dataset with 32 documented secrets (12 Easy, 10 Medium, 10 Hard)
- Automated comparison tools for precision/recall testing
- SARIF output format for all workflows
- Performance metrics and tool comparison reports
**Fixes:**
- Set gitleaks default to no_git=True for uploaded directories
- Update documentation with correct secret counts and workflow names
- Temporarily deactivate AI agent command
- Clean up deprecated test files and GitGuardian workflow
**Testing:**
All workflows verified on secret_detection_benchmark and vulnerable_app test projects.
Workers healthy and system fully functional.
- Remove obsolete docker_logs.py module and container diagnostics from SDK
- Fix security_assessment workflow metadata (vertical: rust -> python)
- Remove all Prefect references from documentation
- Add SDK exception handling test suite
- Clean up old test artifacts
