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