MobSF returns 'files' as a dict (not list):
{"filename": "line_numbers"}
The parser was treating it as a list, causing zero findings
to be extracted. Now properly iterates over the dict and
creates one finding per affected file with correct line numbers
and metadata (CWE, OWASP, MASVS, CVSS).
Fixed in both code_analysis and behaviour sections.
* feat: seed governance config and responses routing
* Add env-configurable timeout for proxy providers
* Integrate LiteLLM OTEL collector and update docs
* Make .env.litellm optional for LiteLLM proxy
* Add LiteLLM proxy integration with model-agnostic virtual keys
Changes:
- Bootstrap generates 3 virtual keys with individual budgets (CLI: $100, Task-Agent: $25, Cognee: $50)
- Task-agent loads config at runtime via entrypoint script to wait for bootstrap completion
- All keys are model-agnostic by default (no LITELLM_DEFAULT_MODELS restrictions)
- Bootstrap handles database/env mismatch after docker prune by deleting stale aliases
- CLI and Cognee configured to use LiteLLM proxy with virtual keys
- Added comprehensive documentation in volumes/env/README.md
Technical details:
- task-agent entrypoint waits for keys in .env file before starting uvicorn
- Bootstrap creates/updates TASK_AGENT_API_KEY, COGNEE_API_KEY, and OPENAI_API_KEY
- Removed hardcoded API keys from docker-compose.yml
- All services route through http://localhost:10999 proxy
* Fix CLI not loading virtual keys from global .env
Project .env files with empty OPENAI_API_KEY values were overriding
the global virtual keys. Updated _load_env_file_if_exists to only
override with non-empty values.
* Fix agent executor not passing API key to LiteLLM
The agent was initializing LiteLlm without api_key or api_base,
causing authentication errors when using the LiteLLM proxy. Now
reads from OPENAI_API_KEY/LLM_API_KEY and LLM_ENDPOINT environment
variables and passes them to LiteLlm constructor.
* Auto-populate project .env with virtual key from global config
When running 'ff init', the command now checks for a global
volumes/env/.env file and automatically uses the OPENAI_API_KEY
virtual key if found. This ensures projects work with LiteLLM
proxy out of the box without manual key configuration.
* docs: Update README with LiteLLM configuration instructions
Add note about LITELLM_GEMINI_API_KEY configuration and clarify that OPENAI_API_KEY default value should not be changed as it's used for the LLM proxy.
* Refactor workflow parameters to use JSON Schema defaults
Consolidates parameter defaults into JSON Schema format, removing the separate default_parameters field. Adds extract_defaults_from_json_schema() helper to extract defaults from the standard schema structure. Updates LiteLLM proxy config to use LITELLM_OPENAI_API_KEY environment variable.
* Remove .env.example from task_agent
* Fix MDX syntax error in llm-proxy.md
* fix: apply default parameters from metadata.yaml automatically
Fixed TemporalManager.run_workflow() to correctly apply default parameter
values from workflow metadata.yaml files when parameters are not provided
by the caller.
Previous behavior:
- When workflow_params was empty {}, the condition
`if workflow_params and 'parameters' in metadata` would fail
- Parameters would not be extracted from schema, resulting in workflows
receiving only target_id with no other parameters
New behavior:
- Removed the `workflow_params and` requirement from the condition
- Now explicitly checks for defaults in parameter spec
- Applies defaults from metadata.yaml automatically when param not provided
- Workflows receive all parameters with proper fallback:
provided value > metadata default > None
This makes metadata.yaml the single source of truth for parameter defaults,
removing the need for workflows to implement defensive default handling.
Affected workflows:
- llm_secret_detection (was failing with KeyError)
- All other workflows now benefit from automatic default application
Co-authored-by: tduhamel42 <tduhamel@fuzzinglabs.com>
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
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.
LLM Analysis Workflow:
- Add llm_analyzer module for AI-powered code security analysis
- Add llm_analysis workflow with SARIF output support
- Mount AI module in Python worker for A2A wrapper access
- Add a2a-sdk dependency to Python worker requirements
- Fix workflow parameter ordering in Temporal manager
Ruff Linter Fixes:
- Fix bare except clauses (E722) across AI and CLI modules
- Add noqa comments for intentional late imports (E402)
- Replace undefined get_ai_status_async with TODO placeholder
- Remove unused imports and variables
- Remove container diagnostics display from exception handler
MCP Configuration:
- Reactivate FUZZFORGE_MCP_URL with default value
- Set default MCP URL to http://localhost:8010/mcp in init
