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* 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.
FuzzForge SDK
A comprehensive Python SDK for the FuzzForge security testing workflow orchestration platform.
Features
- Complete API Coverage: All FuzzForge API endpoints supported
- File Upload: Automatic tarball creation and multipart upload for local files
- Async & Sync: Both synchronous and asynchronous client methods
- Real-time Monitoring: WebSocket and Server-Sent Events for live fuzzing updates
- Type Safety: Full Pydantic model validation for all data structures
- Error Handling: Comprehensive exception hierarchy with detailed error information
- Utility Functions: Helper functions for path validation, SARIF processing, and more
Installation
Install using uv (recommended):
uv add fuzzforge-sdk
Or with pip:
pip install fuzzforge-sdk
Quick Start
Method 1: File Upload (Recommended)
from fuzzforge_sdk import FuzzForgeClient
from pathlib import Path
# Initialize client
client = FuzzForgeClient(base_url="http://localhost:8000")
# List available workflows
workflows = client.list_workflows()
# Submit a workflow with automatic file upload
target_path = Path("/path/to/your/project")
response = client.submit_workflow_with_upload(
workflow_name="security_assessment",
target_path=target_path,
volume_mode="ro",
timeout=300
)
# The SDK automatically:
# - Creates a tarball if target_path is a directory
# - Uploads the file to the backend via HTTP
# - Backend stores it in MinIO
# - Returns the workflow run_id
# Wait for completion and get results
final_status = client.wait_for_completion(response.run_id)
findings = client.get_run_findings(response.run_id)
client.close()
Method 2: Path-Based Submission (Legacy)
from fuzzforge_sdk import FuzzForgeClient
from fuzzforge_sdk.utils import create_workflow_submission
# Initialize client
client = FuzzForgeClient(base_url="http://localhost:8000")
# Submit a workflow with path (only works if backend can access the path)
submission = create_workflow_submission(
target_path="/path/on/backend/filesystem",
volume_mode="ro",
timeout=300
)
response = client.submit_workflow("security_assessment", submission)
client.close()
Examples
The examples/ directory contains complete working examples:
basic_workflow.py: Simple workflow submission and monitoringfuzzing_monitor.py: Real-time fuzzing monitoring with WebSocket/SSEbatch_analysis.py: Batch analysis of multiple projects
File Upload API Reference
submit_workflow_with_upload()
Submit a workflow with automatic file upload from local filesystem.
def submit_workflow_with_upload(
self,
workflow_name: str,
target_path: Union[str, Path],
parameters: Optional[Dict[str, Any]] = None,
volume_mode: str = "ro",
timeout: Optional[int] = None,
progress_callback: Optional[Callable[[int, int], None]] = None
) -> RunSubmissionResponse:
"""
Submit workflow with file upload.
Args:
workflow_name: Name of the workflow to execute
target_path: Path to file or directory to upload
parameters: Optional workflow parameters
volume_mode: Volume mount mode ('ro' or 'rw')
timeout: Optional execution timeout in seconds
progress_callback: Optional callback(bytes_sent, total_bytes)
Returns:
RunSubmissionResponse with run_id and status
Raises:
FileNotFoundError: If target_path doesn't exist
ValidationError: If parameters are invalid
FuzzForgeHTTPError: If upload fails
"""
Example with progress tracking:
from fuzzforge_sdk import FuzzForgeClient
from pathlib import Path
def upload_progress(bytes_sent, total_bytes):
pct = (bytes_sent / total_bytes) * 100
print(f"Upload progress: {pct:.1f}% ({bytes_sent}/{total_bytes} bytes)")
client = FuzzForgeClient(base_url="http://localhost:8000")
response = client.submit_workflow_with_upload(
workflow_name="security_assessment",
target_path=Path("./my-project"),
parameters={"check_secrets": True},
volume_mode="ro",
progress_callback=upload_progress
)
print(f"Workflow started: {response.run_id}")
asubmit_workflow_with_upload()
Async version of submit_workflow_with_upload().
import asyncio
from fuzzforge_sdk import FuzzForgeClient
async def main():
client = FuzzForgeClient(base_url="http://localhost:8000")
response = await client.asubmit_workflow_with_upload(
workflow_name="security_assessment",
target_path="/path/to/project",
parameters={"timeout": 3600}
)
print(f"Workflow started: {response.run_id}")
await client.aclose()
asyncio.run(main())
Internal: _create_tarball()
Creates a compressed tarball from a file or directory.
def _create_tarball(
self,
source_path: Path,
progress_callback: Optional[Callable[[int], None]] = None
) -> Path:
"""
Create compressed tarball (.tar.gz) from source.
Args:
source_path: Path to file or directory
progress_callback: Optional callback(files_added)
Returns:
Path to created tarball in temp directory
Note:
Caller is responsible for cleaning up the tarball
"""
How it works:
-
Directory: Creates tarball with all files, preserving structure
# For directory: /path/to/project/ # Creates: /tmp/tmpXXXXXX.tar.gz containing: # project/file1.py # project/subdir/file2.py -
Single file: Creates tarball with just that file
# For file: /path/to/binary.elf # Creates: /tmp/tmpXXXXXX.tar.gz containing: # binary.elf
Upload Flow Diagram
User Code
↓
submit_workflow_with_upload()
↓
_create_tarball() ───→ Compress files
↓
HTTP POST multipart/form-data
↓
Backend API (/workflows/{name}/upload-and-submit)
↓
MinIO Storage (S3) ───→ Store with target_id
↓
Temporal Workflow
↓
Worker downloads from MinIO
↓
Workflow execution
Error Handling
The SDK provides detailed error context:
from fuzzforge_sdk import FuzzForgeClient
from fuzzforge_sdk.exceptions import (
FuzzForgeHTTPError,
ValidationError,
ConnectionError
)
client = FuzzForgeClient(base_url="http://localhost:8000")
try:
response = client.submit_workflow_with_upload(
workflow_name="security_assessment",
target_path="./nonexistent",
)
except FileNotFoundError as e:
print(f"Target not found: {e}")
except ValidationError as e:
print(f"Invalid parameters: {e}")
except FuzzForgeHTTPError as e:
print(f"Upload failed (HTTP {e.status_code}): {e.message}")
if e.context.response_data:
print(f"Server response: {e.context.response_data}")
except ConnectionError as e:
print(f"Cannot connect to backend: {e}")
Development
Install with development dependencies:
uv sync --extra dev