Files
fuzzforge_ai/sdk
tduhamel42 60ca088ecf 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.
2025-10-14 10:13:45 +02:00
..
2025-09-29 21:26:41 +02:00

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

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 monitoring
  • fuzzing_monitor.py: Real-time fuzzing monitoring with WebSocket/SSE
  • batch_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:

  1. 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
    
  2. 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