Files
fuzzforge_ai/backend/src/api/fuzzing.py
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

326 lines
9.9 KiB
Python

"""
API endpoints for fuzzing workflow management and real-time monitoring
"""
# Copyright (c) 2025 FuzzingLabs
#
# Licensed under the Business Source License 1.1 (BSL). See the LICENSE file
# at the root of this repository for details.
#
# After the Change Date (four years from publication), this version of the
# Licensed Work will be made available under the Apache License, Version 2.0.
# See the LICENSE-APACHE file or http://www.apache.org/licenses/LICENSE-2.0
#
# Additional attribution and requirements are provided in the NOTICE file.
import logging
from typing import List, Dict
from fastapi import APIRouter, HTTPException, WebSocket, WebSocketDisconnect
from fastapi.responses import StreamingResponse
import asyncio
import json
from datetime import datetime
from src.models.findings import (
FuzzingStats,
CrashReport
)
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/fuzzing", tags=["fuzzing"])
# In-memory storage for real-time stats (in production, use Redis or similar)
fuzzing_stats: Dict[str, FuzzingStats] = {}
crash_reports: Dict[str, List[CrashReport]] = {}
active_connections: Dict[str, List[WebSocket]] = {}
def initialize_fuzzing_tracking(run_id: str, workflow_name: str):
"""
Initialize fuzzing tracking for a new run.
This function should be called when a workflow is submitted to enable
real-time monitoring and stats collection.
Args:
run_id: The run identifier
workflow_name: Name of the workflow
"""
fuzzing_stats[run_id] = FuzzingStats(
run_id=run_id,
workflow=workflow_name
)
crash_reports[run_id] = []
active_connections[run_id] = []
@router.get("/{run_id}/stats", response_model=FuzzingStats)
async def get_fuzzing_stats(run_id: str) -> FuzzingStats:
"""
Get current fuzzing statistics for a run.
Args:
run_id: The fuzzing run ID
Returns:
Current fuzzing statistics
Raises:
HTTPException: 404 if run not found
"""
if run_id not in fuzzing_stats:
raise HTTPException(
status_code=404,
detail=f"Fuzzing run not found: {run_id}"
)
return fuzzing_stats[run_id]
@router.get("/{run_id}/crashes", response_model=List[CrashReport])
async def get_crash_reports(run_id: str) -> List[CrashReport]:
"""
Get crash reports for a fuzzing run.
Args:
run_id: The fuzzing run ID
Returns:
List of crash reports
Raises:
HTTPException: 404 if run not found
"""
if run_id not in crash_reports:
raise HTTPException(
status_code=404,
detail=f"Fuzzing run not found: {run_id}"
)
return crash_reports[run_id]
@router.post("/{run_id}/stats")
async def update_fuzzing_stats(run_id: str, stats: FuzzingStats):
"""
Update fuzzing statistics (called by fuzzing workflows).
Args:
run_id: The fuzzing run ID
stats: Updated statistics
Raises:
HTTPException: 404 if run not found
"""
if run_id not in fuzzing_stats:
raise HTTPException(
status_code=404,
detail=f"Fuzzing run not found: {run_id}"
)
# Update stats
fuzzing_stats[run_id] = stats
# Debug: log reception for live instrumentation
try:
logger.info(
"Received fuzzing stats update: run_id=%s exec=%s eps=%.2f crashes=%s corpus=%s coverage=%s elapsed=%ss",
run_id,
stats.executions,
stats.executions_per_sec,
stats.crashes,
stats.corpus_size,
stats.coverage,
stats.elapsed_time,
)
except Exception:
pass
# Notify connected WebSocket clients
if run_id in active_connections:
message = {
"type": "stats_update",
"data": stats.model_dump()
}
for websocket in active_connections[run_id][:]: # Copy to avoid modification during iteration
try:
await websocket.send_text(json.dumps(message))
except Exception:
# Remove disconnected clients
active_connections[run_id].remove(websocket)
@router.post("/{run_id}/crash")
async def report_crash(run_id: str, crash: CrashReport):
"""
Report a new crash (called by fuzzing workflows).
Args:
run_id: The fuzzing run ID
crash: Crash report details
"""
if run_id not in crash_reports:
crash_reports[run_id] = []
# Add crash report
crash_reports[run_id].append(crash)
# Update stats
if run_id in fuzzing_stats:
fuzzing_stats[run_id].crashes += 1
fuzzing_stats[run_id].last_crash_time = crash.timestamp
# Notify connected WebSocket clients
if run_id in active_connections:
message = {
"type": "crash_report",
"data": crash.model_dump()
}
for websocket in active_connections[run_id][:]:
try:
await websocket.send_text(json.dumps(message))
except Exception:
active_connections[run_id].remove(websocket)
@router.websocket("/{run_id}/live")
async def websocket_endpoint(websocket: WebSocket, run_id: str):
"""
WebSocket endpoint for real-time fuzzing updates.
Args:
websocket: WebSocket connection
run_id: The fuzzing run ID to monitor
"""
await websocket.accept()
# Initialize connection tracking
if run_id not in active_connections:
active_connections[run_id] = []
active_connections[run_id].append(websocket)
try:
# Send current stats on connection
if run_id in fuzzing_stats:
current = fuzzing_stats[run_id]
if isinstance(current, dict):
payload = current
elif hasattr(current, "model_dump"):
payload = current.model_dump()
elif hasattr(current, "dict"):
payload = current.dict()
else:
payload = getattr(current, "__dict__", {"run_id": run_id})
message = {"type": "stats_update", "data": payload}
await websocket.send_text(json.dumps(message))
# Keep connection alive
while True:
try:
# Wait for ping or handle disconnect
data = await asyncio.wait_for(websocket.receive_text(), timeout=30.0)
# Echo back for ping-pong
if data == "ping":
await websocket.send_text("pong")
except asyncio.TimeoutError:
# Send periodic heartbeat
await websocket.send_text(json.dumps({"type": "heartbeat"}))
except WebSocketDisconnect:
# Clean up connection
if run_id in active_connections and websocket in active_connections[run_id]:
active_connections[run_id].remove(websocket)
except Exception as e:
logger.error(f"WebSocket error for run {run_id}: {e}")
if run_id in active_connections and websocket in active_connections[run_id]:
active_connections[run_id].remove(websocket)
@router.get("/{run_id}/stream")
async def stream_fuzzing_updates(run_id: str):
"""
Server-Sent Events endpoint for real-time fuzzing updates.
Args:
run_id: The fuzzing run ID to monitor
Returns:
Streaming response with real-time updates
"""
if run_id not in fuzzing_stats:
raise HTTPException(
status_code=404,
detail=f"Fuzzing run not found: {run_id}"
)
async def event_stream():
"""Generate server-sent events for fuzzing updates"""
last_stats_time = datetime.utcnow()
while True:
try:
# Send current stats
if run_id in fuzzing_stats:
current_stats = fuzzing_stats[run_id]
if isinstance(current_stats, dict):
stats_payload = current_stats
elif hasattr(current_stats, "model_dump"):
stats_payload = current_stats.model_dump()
elif hasattr(current_stats, "dict"):
stats_payload = current_stats.dict()
else:
stats_payload = getattr(current_stats, "__dict__", {"run_id": run_id})
event_data = f"data: {json.dumps({'type': 'stats', 'data': stats_payload})}\n\n"
yield event_data
# Send recent crashes
if run_id in crash_reports:
recent_crashes = [
crash for crash in crash_reports[run_id]
if crash.timestamp > last_stats_time
]
for crash in recent_crashes:
event_data = f"data: {json.dumps({'type': 'crash', 'data': crash.model_dump()})}\n\n"
yield event_data
last_stats_time = datetime.utcnow()
await asyncio.sleep(5) # Update every 5 seconds
except Exception as e:
logger.error(f"Error in event stream for run {run_id}: {e}")
break
return StreamingResponse(
event_stream(),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
}
)
@router.delete("/{run_id}")
async def cleanup_fuzzing_run(run_id: str):
"""
Clean up fuzzing run data.
Args:
run_id: The fuzzing run ID to clean up
"""
# Clean up tracking data
fuzzing_stats.pop(run_id, None)
crash_reports.pop(run_id, None)
# Close any active WebSocket connections
if run_id in active_connections:
for websocket in active_connections[run_id]:
try:
await websocket.close()
except Exception:
pass
del active_connections[run_id]
return {"message": f"Cleaned up fuzzing run {run_id}"}