mirror of
https://github.com/FuzzingLabs/fuzzforge_ai.git
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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.
285 lines
9.8 KiB
Python
285 lines
9.8 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2025 FuzzingLabs
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#
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# Licensed under the Business Source License 1.1 (BSL). See the LICENSE file
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# at the root of this repository for details.
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#
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# After the Change Date (four years from publication), this version of the
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# Licensed Work will be made available under the Apache License, Version 2.0.
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# See the LICENSE-APACHE file or http://www.apache.org/licenses/LICENSE-2.0
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#
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# Additional attribution and requirements are provided in the NOTICE file.
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"""
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Real-time fuzzing monitoring example.
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This example demonstrates how to:
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1. Submit a fuzzing workflow
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2. Monitor fuzzing progress in real-time using WebSocket or SSE
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3. Display live statistics and crash reports
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4. Handle real-time data updates
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"""
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import asyncio
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import signal
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import sys
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from pathlib import Path
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from datetime import datetime
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from fuzzforge_sdk import FuzzForgeClient
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from fuzzforge_sdk.utils import (
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create_workflow_submission,
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create_resource_limits,
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format_duration,
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format_execution_rate
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)
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class FuzzingMonitor:
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"""Real-time fuzzing monitor with graceful shutdown."""
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def __init__(self, client: FuzzForgeClient):
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self.client = client
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self.running = True
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self.run_id = None
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def signal_handler(self, signum, frame):
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"""Handle shutdown signals gracefully."""
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print(f"\n🛑 Received signal {signum}, shutting down...")
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self.running = False
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async def monitor_websocket(self, run_id: str):
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"""Monitor fuzzing via WebSocket."""
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print("🔌 Starting WebSocket monitoring...")
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try:
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async for message in self.client.monitor_fuzzing_websocket(run_id):
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if not self.running:
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break
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if message.type == "stats_update":
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self.display_stats(message.data)
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elif message.type == "crash_report":
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self.display_crash(message.data)
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elif message.type == "heartbeat":
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print("💓 Heartbeat")
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else:
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print(f"📨 Received: {message.type}")
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except KeyboardInterrupt:
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print("\n⏹️ Interrupted by user")
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except Exception as e:
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print(f"❌ WebSocket error: {e}")
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def monitor_sse(self, run_id: str):
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"""Monitor fuzzing via Server-Sent Events."""
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print("📡 Starting SSE monitoring...")
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try:
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for message in self.client.monitor_fuzzing_sse(run_id):
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if not self.running:
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break
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if message.type == "stats":
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self.display_stats(message.data)
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elif message.type == "crash":
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self.display_crash(message.data)
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else:
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print(f"📨 Received: {message.type}")
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except KeyboardInterrupt:
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print("\n⏹️ Interrupted by user")
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except Exception as e:
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print(f"❌ SSE error: {e}")
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def display_stats(self, stats_data):
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"""Display fuzzing statistics."""
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# Clear screen and move cursor to top
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print("\033[2J\033[H", end="")
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print("🎯 FuzzForge Live Fuzzing Monitor")
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print("=" * 50)
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print(f"Run ID: {stats_data.get('run_id', 'unknown')}")
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print(f"Workflow: {stats_data.get('workflow', 'unknown')}")
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print()
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# Statistics
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executions = stats_data.get('executions', 0)
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exec_per_sec = stats_data.get('executions_per_sec', 0.0)
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crashes = stats_data.get('crashes', 0)
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unique_crashes = stats_data.get('unique_crashes', 0)
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coverage = stats_data.get('coverage')
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corpus_size = stats_data.get('corpus_size', 0)
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elapsed_time = stats_data.get('elapsed_time', 0)
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print("📊 Statistics:")
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print(f" Executions: {executions:,}")
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print(f" Rate: {format_execution_rate(exec_per_sec)}")
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print(f" Runtime: {format_duration(elapsed_time)}")
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print(f" Corpus size: {corpus_size:,}")
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if coverage is not None:
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print(f" Coverage: {coverage:.1f}%")
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print()
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print("💥 Crashes:")
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print(f" Total crashes: {crashes}")
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print(f" Unique crashes: {unique_crashes}")
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last_crash = stats_data.get('last_crash_time')
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if last_crash:
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crash_time = datetime.fromisoformat(last_crash.replace('Z', '+00:00'))
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print(f" Last crash: {crash_time.strftime('%H:%M:%S')}")
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print()
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print("Press Ctrl+C to stop monitoring")
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print("-" * 50)
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def display_crash(self, crash_data):
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"""Display new crash report."""
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print("\n🚨 NEW CRASH DETECTED!")
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print(f" Crash ID: {crash_data.get('crash_id')}")
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print(f" Signal: {crash_data.get('signal', 'unknown')}")
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print(f" Type: {crash_data.get('crash_type', 'unknown')}")
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print(f" Severity: {crash_data.get('severity', 'unknown')}")
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if crash_data.get('input_file'):
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print(f" Input file: {crash_data['input_file']}")
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print("-" * 30)
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async def main():
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"""Main fuzzing monitoring example."""
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# Initialize client
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client = FuzzForgeClient(base_url="http://localhost:8000")
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monitor = FuzzingMonitor(client)
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# Set up signal handlers
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signal.signal(signal.SIGINT, monitor.signal_handler)
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signal.signal(signal.SIGTERM, monitor.signal_handler)
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try:
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# Check API status
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print("🔗 Connecting to FuzzForge API...")
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status = await client.aget_api_status()
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print(f"✅ Connected to {status.name} v{status.version}\n")
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# List workflows and find fuzzing ones
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workflows = await client.alist_workflows()
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fuzzing_workflows = [w for w in workflows if "fuzz" in w.name.lower() or "fuzzing" in w.tags]
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if not fuzzing_workflows:
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print("❌ No fuzzing workflows found")
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print("Available workflows:")
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for w in workflows:
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print(f" • {w.name} (tags: {w.tags})")
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return
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# Select first fuzzing workflow
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selected_workflow = fuzzing_workflows[0]
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print(f"🎯 Selected fuzzing workflow: {selected_workflow.name}")
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# Create submission with fuzzing-appropriate settings
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target_path = Path.cwd().absolute()
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# Set longer timeout and resource limits for fuzzing
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resource_limits = create_resource_limits(
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cpu_limit="2", # 2 CPU cores
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memory_limit="4Gi", # 4GB memory
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cpu_request="1", # Guarantee 1 core
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memory_request="2Gi" # Guarantee 2GB
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)
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submission = create_workflow_submission(
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target_path=target_path,
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volume_mode="rw", # Fuzzing may need to write files
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timeout=3600, # 1 hour timeout
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resource_limits=resource_limits,
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parameters={
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"max_len": 1024, # Maximum input length
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"timeout": 10, # Per-execution timeout
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"runs": 1000000, # Number of executions
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}
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)
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print("🚀 Submitting fuzzing workflow...")
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response = await client.asubmit_workflow(selected_workflow.name, submission)
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monitor.run_id = response.run_id
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print("✅ Fuzzing started!")
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print(f" Run ID: {response.run_id}")
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print(f" Initial status: {response.status}")
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print()
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# Wait a moment for fuzzing to initialize
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await asyncio.sleep(5)
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# Get initial stats to verify fuzzing is tracked
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try:
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stats = await client.aget_fuzzing_stats(response.run_id)
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print(f"📊 Fuzzing tracking initialized for workflow: {stats.workflow}")
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except Exception as e:
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print(f"⚠️ Warning: Fuzzing tracking not available: {e}")
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print(" Monitoring will show run status updates only")
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# Choose monitoring method
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if len(sys.argv) > 1 and sys.argv[1] == "--sse":
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print("📡 Using Server-Sent Events for monitoring...")
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monitor.monitor_sse(response.run_id)
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else:
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print("🔌 Using WebSocket for monitoring...")
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await monitor.monitor_websocket(response.run_id)
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except KeyboardInterrupt:
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print("\n⏹️ Interrupted by user")
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except Exception as e:
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print(f"❌ Error: {e}")
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finally:
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# Cleanup
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if monitor.run_id:
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try:
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print(f"\n🧹 Cleaning up fuzzing run {monitor.run_id}...")
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await client.acleanup_fuzzing_run(monitor.run_id)
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print("✅ Cleanup completed")
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except Exception as e:
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print(f"⚠️ Cleanup failed: {e}")
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await client.aclose()
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def sync_monitor_example():
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"""Example of synchronous SSE monitoring."""
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client = FuzzForgeClient(base_url="http://localhost:8000")
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try:
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# This would require a pre-existing fuzzing run
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run_id = input("Enter fuzzing run ID to monitor: ").strip()
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if not run_id:
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print("❌ Run ID required")
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return
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print(f"📡 Monitoring fuzzing run: {run_id}")
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print("Press Ctrl+C to stop")
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print()
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monitor = FuzzingMonitor(client)
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monitor.monitor_sse(run_id)
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except KeyboardInterrupt:
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print("\n⏹️ Monitoring stopped")
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except Exception as e:
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print(f"❌ Error: {e}")
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finally:
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client.close()
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if __name__ == "__main__":
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if len(sys.argv) > 1 and sys.argv[1] == "--sync":
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print("🔄 Running synchronous SSE monitoring...")
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sync_monitor_example()
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else:
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print("🔄 Running async WebSocket monitoring...")
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print("💡 Use --sse flag for Server-Sent Events")
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print("💡 Use --sync flag for synchronous monitoring")
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asyncio.run(main()) |