Files
fuzzforge_ai/README.md
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

6.9 KiB
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FuzzForge Banner

🚧 FuzzForge is under active development

AI-powered workflow automation and AI Agents for AppSec, Fuzzing & Offensive Security

Discord License: BSL + Apache Python 3.11+ Website Version GitHub Stars

OverviewFeaturesInstallationQuickstartAI DemoContributingRoadmap


🚀 Overview

FuzzForge helps security researchers and engineers automate application security and offensive security workflows with the power of AI and fuzzing frameworks.

  • Orchestrate static & dynamic analysis
  • Automate vulnerability research
  • Scale AppSec testing with AI agents
  • Build, share & reuse workflows across teams

FuzzForge is open source, built to empower security teams, researchers, and the community.

🚧 FuzzForge is under active development. Expect breaking changes.


Support the Project

GitHub Stars

If you find FuzzForge useful, please star the repo to support development 🚀


Key Features

  • 🤖 AI Agents for Security Specialized agents for AppSec, reversing, and fuzzing
  • 🛠 Workflow Automation Define & execute AppSec workflows as code
  • 📈 Vulnerability Research at Scale Rediscover 1-days & find 0-days with automation
  • 🔗 Fuzzer Integration AFL, Honggfuzz, AFLnet, StateAFL & more
  • 🌐 Community Marketplace Share workflows, corpora, PoCs, and modules
  • 🔒 Enterprise Ready Team/Corp cloud tiers for scaling offensive security

📦 Installation

Requirements

Python 3.11+ Python 3.11 or higher is required.

uv Package Manager

curl -LsSf https://astral.sh/uv/install.sh | sh

Docker For containerized workflows, see the Docker Installation Guide.

Configure Docker Daemon

Before running docker compose up, configure Docker to allow insecure registries (required for the local registry).

Add the following to your Docker daemon configuration:

{
  "insecure-registries": [
    "localhost:5000",
    "host.docker.internal:5001",
    "registry:5000"
  ]
}

macOS (Docker Desktop):

  1. Open Docker Desktop
  2. Go to Settings → Docker Engine
  3. Add the insecure-registries configuration to the JSON
  4. Click "Apply & Restart"

Linux:

  1. Edit /etc/docker/daemon.json (create if it doesn't exist):
    sudo nano /etc/docker/daemon.json
    
  2. Add the configuration above
  3. Restart Docker:
    sudo systemctl restart docker
    

CLI Installation

After installing the requirements, install the FuzzForge CLI:

# Clone the repository
git clone https://github.com/fuzzinglabs/fuzzforge_ai.git
cd fuzzforge_ai

# Install CLI with uv (from the root directory)
uv tool install --python python3.12 .

Quickstart

Run your first workflow with Temporal orchestration and automatic file upload:

# 1. Clone the repo
git clone https://github.com/fuzzinglabs/fuzzforge_ai.git
cd fuzzforge_ai

# 2. Start FuzzForge with Temporal
docker-compose -f docker-compose.temporal.yaml up -d

The first launch can take 2-3 minutes for services to initialize

# 3. Run your first workflow (files are automatically uploaded)
cd test_projects/vulnerable_app/
fuzzforge init                           # Initialize FuzzForge project
ff workflow run security_assessment .    # Start workflow - CLI uploads files automatically!

# The CLI will:
# - Detect the local directory
# - Create a compressed tarball
# - Upload to backend (via MinIO)
# - Start the workflow on vertical worker

What's running:

Manual Workflow Setup

Manual Workflow Demo

Setting up and running security workflows through the interface

👉 More installation options in the Documentation.


AI-Powered Workflow Execution

LLM Workflow Demo

AI agents automatically analyzing code and providing security insights

📚 Resources


🤝 Contributing

We welcome contributions from the community!
There are many ways to help:

  • Report bugs by opening an issue
  • Suggest new features or improvements
  • Submit pull requests with fixes or enhancements
  • Share workflows, corpora, or modules with the community

See our Contributing Guide for details.


🗺️ Roadmap

Planned features and improvements:

  • 📦 Public workflow & module marketplace
  • 🤖 New specialized AI agents (Rust, Go, Android, Automotive)
  • 🔗 Expanded fuzzer integrations (LibFuzzer, Jazzer, more network fuzzers)
  • ☁️ Multi-tenant SaaS platform with team collaboration
  • 📊 Advanced reporting & analytics

👉 Follow updates in the GitHub issues and Discord.


📜 License

FuzzForge is released under the Business Source License (BSL) 1.1, with an automatic fallback to Apache 2.0 after 4 years.
See LICENSE and LICENSE-APACHE for details.