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CI/CD Integration with Ephemeral Deployment Model (#14)
* feat: Complete migration from Prefect to Temporal BREAKING CHANGE: Replaces Prefect workflow orchestration with Temporal ## Major Changes - Replace Prefect with Temporal for workflow orchestration - Implement vertical worker architecture (rust, android) - Replace Docker registry with MinIO for unified storage - Refactor activities to be co-located with workflows - Update all API endpoints for Temporal compatibility ## Infrastructure - New: docker-compose.temporal.yaml (Temporal + MinIO + workers) - New: workers/ directory with rust and android vertical workers - New: backend/src/temporal/ (manager, discovery) - New: backend/src/storage/ (S3-cached storage with MinIO) - New: backend/toolbox/common/ (shared storage activities) - Deleted: docker-compose.yaml (old Prefect setup) - Deleted: backend/src/core/prefect_manager.py - Deleted: backend/src/services/prefect_stats_monitor.py - Deleted: Docker registry and insecure-registries requirement ## Workflows - Migrated: security_assessment workflow to Temporal - New: rust_test workflow (example/test workflow) - Deleted: secret_detection_scan (Prefect-based, to be reimplemented) - Activities now co-located with workflows for independent testing ## API Changes - Updated: backend/src/api/workflows.py (Temporal submission) - Updated: backend/src/api/runs.py (Temporal status/results) - Updated: backend/src/main.py (727 lines, TemporalManager integration) - Updated: All 16 MCP tools to use TemporalManager ## Testing - ✅ All services healthy (Temporal, PostgreSQL, MinIO, workers, backend) - ✅ All API endpoints functional - ✅ End-to-end workflow test passed (72 findings from vulnerable_app) - ✅ MinIO storage integration working (target upload/download, results) - ✅ Worker activity discovery working (6 activities registered) - ✅ Tarball extraction working - ✅ SARIF report generation working ## Documentation - ARCHITECTURE.md: Complete Temporal architecture documentation - QUICKSTART_TEMPORAL.md: Getting started guide - MIGRATION_DECISION.md: Why we chose Temporal over Prefect - IMPLEMENTATION_STATUS.md: Migration progress tracking - workers/README.md: Worker development guide ## Dependencies - Added: temporalio>=1.6.0 - Added: boto3>=1.34.0 (MinIO S3 client) - Removed: prefect>=3.4.18 * feat: Add Python fuzzing vertical with Atheris integration This commit implements a complete Python fuzzing workflow using Atheris: ## Python Worker (workers/python/) - Dockerfile with Python 3.11, Atheris, and build tools - Generic worker.py for dynamic workflow discovery - requirements.txt with temporalio, boto3, atheris dependencies - Added to docker-compose.temporal.yaml with dedicated cache volume ## AtherisFuzzer Module (backend/toolbox/modules/fuzzer/) - Reusable module extending BaseModule - Auto-discovers fuzz targets (fuzz_*.py, *_fuzz.py, fuzz_target.py) - Recursive search to find targets in nested directories - Dynamically loads TestOneInput() function - Configurable max_iterations and timeout - Real-time stats callback support for live monitoring - Returns findings as ModuleFinding objects ## Atheris Fuzzing Workflow (backend/toolbox/workflows/atheris_fuzzing/) - Temporal workflow for orchestrating fuzzing - Downloads user code from MinIO - Executes AtherisFuzzer module - Uploads results to MinIO - Cleans up cache after execution - metadata.yaml with vertical: python for routing ## Test Project (test_projects/python_fuzz_waterfall/) - Demonstrates stateful waterfall vulnerability - main.py with check_secret() that leaks progress - fuzz_target.py with Atheris TestOneInput() harness - Complete README with usage instructions ## Backend Fixes - Fixed parameter merging in REST API endpoints (workflows.py) - Changed workflow parameter passing from positional args to kwargs (manager.py) - Default parameters now properly merged with user parameters ## Testing ✅ Worker discovered AtherisFuzzingWorkflow ✅ Workflow executed end-to-end successfully ✅ Fuzz target auto-discovered in nested directories ✅ Atheris ran 100,000 iterations ✅ Results uploaded and cache cleaned * chore: Complete Temporal migration with updated CLI/SDK/docs This commit includes all remaining Temporal migration changes: ## CLI Updates (cli/) - Updated workflow execution commands for Temporal - Enhanced error handling and exceptions - Updated dependencies in uv.lock ## SDK Updates (sdk/) - Client methods updated for Temporal workflows - Updated models for new workflow execution - Updated dependencies in uv.lock ## Documentation Updates (docs/) - Architecture documentation for Temporal - Workflow concept documentation - Resource management documentation (new) - Debugging guide (new) - Updated tutorials and how-to guides - Troubleshooting updates ## README Updates - Main README with Temporal instructions - Backend README - CLI README - SDK README ## Other - Updated IMPLEMENTATION_STATUS.md - Removed old vulnerable_app.tar.gz These changes complete the Temporal migration and ensure the CLI/SDK work correctly with the new backend. * fix: Use positional args instead of kwargs for Temporal workflows The Temporal Python SDK's start_workflow() method doesn't accept a 'kwargs' parameter. Workflows must receive parameters as positional arguments via the 'args' parameter. Changed from: args=workflow_args # Positional arguments This fixes the error: TypeError: Client.start_workflow() got an unexpected keyword argument 'kwargs' Workflows now correctly receive parameters in order: - security_assessment: [target_id, scanner_config, analyzer_config, reporter_config] - atheris_fuzzing: [target_id, target_file, max_iterations, timeout_seconds] - rust_test: [target_id, test_message] * fix: Filter metadata-only parameters from workflow arguments SecurityAssessmentWorkflow was receiving 7 arguments instead of 2-5. The issue was that target_path and volume_mode from default_parameters were being passed to the workflow, when they should only be used by the system for configuration. Now filters out metadata-only parameters (target_path, volume_mode) before passing arguments to workflow execution. * refactor: Remove Prefect leftovers and volume mounting legacy Complete cleanup of Prefect migration artifacts: Backend: - Delete registry.py and workflow_discovery.py (Prefect-specific files) - Remove Docker validation from setup.py (no longer needed) - Remove ResourceLimits and VolumeMount models - Remove target_path and volume_mode from WorkflowSubmission - Remove supported_volume_modes from API and discovery - Clean up metadata.yaml files (remove volume/path fields) - Simplify parameter filtering in manager.py SDK: - Remove volume_mode parameter from client methods - Remove ResourceLimits and VolumeMount models - Remove Prefect error patterns from docker_logs.py - Clean up WorkflowSubmission and WorkflowMetadata models CLI: - Remove Volume Modes display from workflow info All removed features are Prefect-specific or Docker volume mounting artifacts. Temporal workflows use MinIO storage exclusively. * feat: Add comprehensive test suite and benchmark infrastructure - Add 68 unit tests for fuzzer, scanner, and analyzer modules - Implement pytest-based test infrastructure with fixtures - Add 6 performance benchmarks with category-specific thresholds - Configure GitHub Actions for automated testing and benchmarking - Add test and benchmark documentation Test coverage: - AtherisFuzzer: 8 tests - CargoFuzzer: 14 tests - FileScanner: 22 tests - SecurityAnalyzer: 24 tests All tests passing (68/68) All benchmarks passing (6/6) * fix: Resolve all ruff linting violations across codebase Fixed 27 ruff violations in 12 files: - Removed unused imports (Depends, Dict, Any, Optional, etc.) - Fixed undefined workflow_info variable in workflows.py - Removed dead code with undefined variables in atheris_fuzzer.py - Changed f-string to regular string where no placeholders used All files now pass ruff checks for CI/CD compliance. * fix: Configure CI for unit tests only - Renamed docker-compose.temporal.yaml → docker-compose.yml for CI compatibility - Commented out integration-tests job (no integration tests yet) - Updated test-summary to only depend on lint and unit-tests CI will now run successfully with 68 unit tests. Integration tests can be added later. * feat: Add CI/CD integration with ephemeral deployment model Implements comprehensive CI/CD support for FuzzForge with on-demand worker management: **Worker Management (v0.7.0)** - Add WorkerManager for automatic worker lifecycle control - Auto-start workers from stopped state when workflows execute - Auto-stop workers after workflow completion - Health checks and startup timeout handling (90s default) **CI/CD Features** - `--fail-on` flag: Fail builds based on SARIF severity levels (error/warning/note/info) - `--export-sarif` flag: Export findings in SARIF 2.1.0 format - `--auto-start`/`--auto-stop` flags: Control worker lifecycle - Exit code propagation: Returns 1 on blocking findings, 0 on success **Exit Code Fix** - Add `except typer.Exit: raise` handlers at 3 critical locations - Move worker cleanup to finally block for guaranteed execution - Exit codes now propagate correctly even when build fails **CI Scripts & Examples** - ci-start.sh: Start FuzzForge services with health checks - ci-stop.sh: Clean shutdown with volume preservation option - GitHub Actions workflow example (security-scan.yml) - GitLab CI pipeline example (.gitlab-ci.example.yml) - docker-compose.ci.yml: CI-optimized compose file with profiles **OSS-Fuzz Integration** - New ossfuzz_campaign workflow for running OSS-Fuzz projects - OSS-Fuzz worker with Docker-in-Docker support - Configurable campaign duration and project selection **Documentation** - Comprehensive CI/CD integration guide (docs/how-to/cicd-integration.md) - Updated architecture docs with worker lifecycle details - Updated workspace isolation documentation - CLI README with worker management examples **SDK Enhancements** - Add get_workflow_worker_info() endpoint - Worker vertical metadata in workflow responses **Testing** - All workflows tested: security_assessment, atheris_fuzzing, secret_detection, cargo_fuzzing - All monitoring commands tested: stats, crashes, status, finding - Full CI pipeline simulation verified - Exit codes verified for success/failure scenarios Ephemeral CI/CD model: ~3-4GB RAM, ~60-90s startup, runs entirely in CI containers. * fix: Resolve ruff linting violations in CI/CD code - Remove unused variables (run_id, defaults, result) - Remove unused imports - Fix f-string without placeholders All CI/CD integration files now pass ruff checks.
This commit is contained in:
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test_projects/python_fuzz_waterfall/README.md
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# Python Fuzzing Test - Waterfall Vulnerability
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This project demonstrates a **stateful vulnerability** that Atheris can discover through fuzzing.
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## Vulnerability Description
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The `check_secret()` function in `main.py` validates input character by character against the secret string "FUZZINGLABS". This creates a **waterfall vulnerability** where:
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1. State leaks through the global `progress` variable
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2. Each correct character advances the progress counter
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3. When all 11 characters are provided in order, the function crashes with `SystemError`
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This pattern is analogous to:
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- Timing attacks on password checkers
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- Protocol state machines with sequential validation
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- Multi-step authentication flows
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## Files
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- `main.py` - Main application with vulnerable `check_secret()` function
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- `fuzz_target.py` - Atheris fuzzing harness (contains `TestOneInput()`)
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- `README.md` - This file
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## How to Fuzz
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### Using FuzzForge CLI
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```bash
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# Initialize FuzzForge in this directory
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cd test_projects/python_fuzz_waterfall/
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ff init
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# Run fuzzing workflow (uploads code to MinIO)
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ff workflow run atheris_fuzzing .
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# The workflow will:
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# 1. Upload this directory to MinIO
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# 2. Worker downloads and extracts the code
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# 3. Worker discovers fuzz_target.py (has TestOneInput)
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# 4. Worker runs Atheris fuzzing
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# 5. Reports real-time stats every 5 seconds
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# 6. Finds crash when "FUZZINGLABS" is discovered
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```
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### Using FuzzForge SDK
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```python
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from fuzzforge_sdk import FuzzForgeClient
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from pathlib import Path
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client = FuzzForgeClient(base_url="http://localhost:8000")
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# Upload and run fuzzing
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response = client.submit_workflow_with_upload(
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workflow_name="atheris_fuzzing",
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target_path=Path("./"),
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parameters={
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"max_iterations": 100000,
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"timeout_seconds": 300
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}
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)
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print(f"Workflow started: {response.run_id}")
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# Wait for completion
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final_status = client.wait_for_completion(response.run_id)
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findings = client.get_run_findings(response.run_id)
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for finding in findings:
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print(f"Crash: {finding.title}")
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print(f"Input: {finding.metadata.get('crash_input_hex')}")
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```
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### Standalone (Without FuzzForge)
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```bash
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# Install Atheris
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pip install atheris
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# Run fuzzing directly
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python fuzz_target.py
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```
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## Expected Behavior
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When fuzzing:
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1. **Initial phase**: Random exploration, progress = 0
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2. **Discovery phase**: Atheris finds 'F' (first char), progress = 1
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3. **Incremental progress**: Finds 'U', then 'Z', etc.
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4. **Crash**: When full "FUZZINGLABS" discovered, crashes with:
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```
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SystemError: SECRET COMPROMISED: FUZZINGLABS
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```
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## Monitoring
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Watch real-time fuzzing stats:
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```bash
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docker logs fuzzforge-worker-python -f | grep LIVE_STATS
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```
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Output example:
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```
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INFO - LIVE_STATS - executions=1523 execs_per_sec=1523.0 crashes=0
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INFO - LIVE_STATS - executions=7842 execs_per_sec=2104.2 crashes=0
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INFO - LIVE_STATS - executions=15234 execs_per_sec=2167.0 crashes=1 ← Crash found!
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```
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## Vulnerability Details
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**CVE**: N/A (demonstration vulnerability)
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**CWE**: CWE-208 (Observable Timing Discrepancy)
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**Severity**: Critical (in real systems)
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**Fix**: Remove state-based checking or implement constant-time comparison:
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```python
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def check_secret_safe(input_data: bytes) -> bool:
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"""Constant-time comparison"""
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import hmac
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return hmac.compare_digest(input_data, SECRET.encode())
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```
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## Adjusting Difficulty
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If fuzzing finds the crash too quickly, extend the secret:
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```python
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# In main.py, change:
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SECRET = "FUZZINGLABSSECURITYTESTING" # 26 characters instead of 11
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```
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## License
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MIT License - This is a demonstration project for educational purposes.
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