mirror of
https://github.com/FuzzingLabs/fuzzforge_ai.git
synced 2026-02-13 15:52:46 +00:00
* 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.
456 lines
16 KiB
Python
456 lines
16 KiB
Python
"""
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Cargo Fuzzer Module
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Reusable module for fuzzing Rust code using cargo-fuzz (libFuzzer).
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Discovers and fuzzes user-provided Rust targets with fuzz_target!() macros.
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"""
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import asyncio
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import logging
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import os
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import re
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import time
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from pathlib import Path
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from typing import Dict, Any, List, Optional, Callable
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from modules.base import BaseModule, ModuleMetadata, ModuleResult, ModuleFinding
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logger = logging.getLogger(__name__)
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class CargoFuzzer(BaseModule):
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"""
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Cargo-fuzz (libFuzzer) fuzzer module for Rust code.
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Discovers fuzz targets in user's Rust project and runs cargo-fuzz
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to find crashes, undefined behavior, and memory safety issues.
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"""
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def get_metadata(self) -> ModuleMetadata:
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"""Get module metadata"""
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return ModuleMetadata(
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name="cargo_fuzz",
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version="0.11.2",
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description="Fuzz Rust code using cargo-fuzz with libFuzzer backend",
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author="FuzzForge Team",
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category="fuzzer",
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tags=["fuzzing", "rust", "cargo-fuzz", "libfuzzer", "memory-safety"],
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input_schema={
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"type": "object",
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"properties": {
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"target_name": {
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"type": "string",
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"description": "Fuzz target name (auto-discovered if not specified)"
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},
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"max_iterations": {
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"type": "integer",
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"default": 1000000,
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"description": "Maximum fuzzing iterations"
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},
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"timeout_seconds": {
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"type": "integer",
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"default": 1800,
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"description": "Fuzzing timeout in seconds"
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},
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"sanitizer": {
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"type": "string",
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"enum": ["address", "memory", "undefined"],
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"default": "address",
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"description": "Sanitizer to use (address, memory, undefined)"
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}
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}
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},
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output_schema={
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"type": "object",
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"properties": {
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"findings": {
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"type": "array",
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"description": "Crashes and memory safety issues found"
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},
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"summary": {
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"type": "object",
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"description": "Fuzzing execution summary"
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}
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}
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}
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)
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def validate_config(self, config: Dict[str, Any]) -> bool:
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"""Validate configuration"""
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max_iterations = config.get("max_iterations", 1000000)
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if not isinstance(max_iterations, int) or max_iterations < 1:
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raise ValueError("max_iterations must be a positive integer")
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timeout = config.get("timeout_seconds", 1800)
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if not isinstance(timeout, int) or timeout < 1:
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raise ValueError("timeout_seconds must be a positive integer")
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sanitizer = config.get("sanitizer", "address")
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if sanitizer not in ["address", "memory", "undefined"]:
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raise ValueError("sanitizer must be one of: address, memory, undefined")
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return True
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async def execute(
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self,
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config: Dict[str, Any],
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workspace: Path,
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stats_callback: Optional[Callable] = None
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) -> ModuleResult:
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"""
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Execute cargo-fuzz on user's Rust code.
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Args:
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config: Fuzzer configuration
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workspace: Path to workspace directory containing Rust project
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stats_callback: Optional callback for real-time stats updates
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Returns:
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ModuleResult containing findings and summary
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"""
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self.start_timer()
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try:
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# Validate inputs
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self.validate_config(config)
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self.validate_workspace(workspace)
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logger.info(f"Running cargo-fuzz on {workspace}")
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# Step 1: Discover fuzz targets
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targets = await self._discover_fuzz_targets(workspace)
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if not targets:
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return self.create_result(
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findings=[],
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status="failed",
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error="No fuzz targets found. Expected fuzz targets in fuzz/fuzz_targets/"
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)
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# Get target name from config or use first discovered target
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target_name = config.get("target_name")
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if not target_name:
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target_name = targets[0]
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logger.info(f"No target specified, using first discovered target: {target_name}")
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elif target_name not in targets:
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return self.create_result(
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findings=[],
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status="failed",
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error=f"Target '{target_name}' not found. Available targets: {', '.join(targets)}"
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)
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# Step 2: Build fuzz target
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logger.info(f"Building fuzz target: {target_name}")
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build_success = await self._build_fuzz_target(workspace, target_name, config)
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if not build_success:
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return self.create_result(
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findings=[],
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status="failed",
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error=f"Failed to build fuzz target: {target_name}"
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)
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# Step 3: Run fuzzing
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logger.info(f"Starting fuzzing: {target_name}")
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findings, stats = await self._run_fuzzing(
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workspace,
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target_name,
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config,
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stats_callback
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)
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# Step 4: Parse crash artifacts
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crash_findings = await self._parse_crash_artifacts(workspace, target_name)
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findings.extend(crash_findings)
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logger.info(f"Fuzzing completed: {len(findings)} crashes found")
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return self.create_result(
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findings=findings,
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status="success",
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summary=stats
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)
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except Exception as e:
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logger.error(f"Cargo fuzzer failed: {e}")
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return self.create_result(
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findings=[],
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status="failed",
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error=str(e)
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)
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async def _discover_fuzz_targets(self, workspace: Path) -> List[str]:
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"""
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Discover fuzz targets in the project.
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Looks for fuzz targets in fuzz/fuzz_targets/ directory.
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"""
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fuzz_targets_dir = workspace / "fuzz" / "fuzz_targets"
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if not fuzz_targets_dir.exists():
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logger.warning(f"No fuzz targets directory found: {fuzz_targets_dir}")
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return []
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targets = []
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for file in fuzz_targets_dir.glob("*.rs"):
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target_name = file.stem
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targets.append(target_name)
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logger.info(f"Discovered fuzz target: {target_name}")
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return targets
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async def _build_fuzz_target(
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self,
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workspace: Path,
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target_name: str,
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config: Dict[str, Any]
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) -> bool:
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"""Build the fuzz target with instrumentation"""
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try:
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sanitizer = config.get("sanitizer", "address")
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# Build command
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cmd = [
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"cargo", "fuzz", "build",
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target_name,
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f"--sanitizer={sanitizer}"
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]
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logger.debug(f"Build command: {' '.join(cmd)}")
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proc = await asyncio.create_subprocess_exec(
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*cmd,
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cwd=workspace,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE
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)
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stdout, stderr = await proc.communicate()
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if proc.returncode != 0:
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logger.error(f"Build failed: {stderr.decode()}")
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return False
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logger.info("Build successful")
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return True
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except Exception as e:
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logger.error(f"Build error: {e}")
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return False
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async def _run_fuzzing(
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self,
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workspace: Path,
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target_name: str,
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config: Dict[str, Any],
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stats_callback: Optional[Callable]
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) -> tuple[List[ModuleFinding], Dict[str, Any]]:
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"""
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Run cargo-fuzz and collect statistics.
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Returns:
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Tuple of (findings, stats_dict)
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"""
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max_iterations = config.get("max_iterations", 1000000)
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timeout_seconds = config.get("timeout_seconds", 1800)
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sanitizer = config.get("sanitizer", "address")
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findings = []
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stats = {
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"total_executions": 0,
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"crashes_found": 0,
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"corpus_size": 0,
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"coverage": 0.0,
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"execution_time": 0.0
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}
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try:
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# Cargo fuzz run command
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cmd = [
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"cargo", "fuzz", "run",
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target_name,
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f"--sanitizer={sanitizer}",
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"--",
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f"-runs={max_iterations}",
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f"-max_total_time={timeout_seconds}"
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]
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logger.debug(f"Fuzz command: {' '.join(cmd)}")
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start_time = time.time()
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proc = await asyncio.create_subprocess_exec(
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*cmd,
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cwd=workspace,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.STDOUT
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)
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# Monitor output and extract stats
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last_stats_time = time.time()
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async for line in proc.stdout:
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line_str = line.decode('utf-8', errors='ignore').strip()
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# Parse libFuzzer stats
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# Example: "#12345 NEW cov: 123 ft: 456 corp: 10/234b"
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stats_match = re.match(r'#(\d+)\s+.*cov:\s*(\d+).*corp:\s*(\d+)', line_str)
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if stats_match:
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execs = int(stats_match.group(1))
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cov = int(stats_match.group(2))
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corp = int(stats_match.group(3))
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stats["total_executions"] = execs
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stats["coverage"] = float(cov)
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stats["corpus_size"] = corp
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stats["execution_time"] = time.time() - start_time
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# Invoke stats callback for real-time monitoring
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if stats_callback and time.time() - last_stats_time >= 0.5:
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await stats_callback({
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"total_execs": execs,
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"execs_per_sec": execs / stats["execution_time"] if stats["execution_time"] > 0 else 0,
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"crashes": stats["crashes_found"],
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"coverage": cov,
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"corpus_size": corp,
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"elapsed_time": int(stats["execution_time"])
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})
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last_stats_time = time.time()
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# Detect crash line
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if "SUMMARY:" in line_str or "ERROR:" in line_str:
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logger.info(f"Detected crash: {line_str}")
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stats["crashes_found"] += 1
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await proc.wait()
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stats["execution_time"] = time.time() - start_time
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# Send final stats update
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if stats_callback:
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await stats_callback({
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"total_execs": stats["total_executions"],
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"execs_per_sec": stats["total_executions"] / stats["execution_time"] if stats["execution_time"] > 0 else 0,
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"crashes": stats["crashes_found"],
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"coverage": stats["coverage"],
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"corpus_size": stats["corpus_size"],
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"elapsed_time": int(stats["execution_time"])
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})
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logger.info(
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f"Fuzzing completed: {stats['total_executions']} execs, "
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f"{stats['crashes_found']} crashes"
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)
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except Exception as e:
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logger.error(f"Fuzzing error: {e}")
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return findings, stats
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async def _parse_crash_artifacts(
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self,
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workspace: Path,
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target_name: str
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) -> List[ModuleFinding]:
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"""
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Parse crash artifacts from fuzz/artifacts directory.
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Cargo-fuzz stores crashes in: fuzz/artifacts/<target_name>/
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"""
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findings = []
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artifacts_dir = workspace / "fuzz" / "artifacts" / target_name
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if not artifacts_dir.exists():
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logger.info("No crash artifacts found")
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return findings
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# Find all crash files
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for crash_file in artifacts_dir.glob("crash-*"):
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try:
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finding = await self._analyze_crash(workspace, target_name, crash_file)
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if finding:
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findings.append(finding)
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except Exception as e:
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logger.warning(f"Failed to analyze crash {crash_file}: {e}")
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logger.info(f"Parsed {len(findings)} crash artifacts")
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return findings
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async def _analyze_crash(
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self,
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workspace: Path,
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target_name: str,
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crash_file: Path
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) -> Optional[ModuleFinding]:
|
|
"""
|
|
Analyze a single crash file.
|
|
|
|
Runs cargo-fuzz with the crash input to reproduce and get stack trace.
|
|
"""
|
|
try:
|
|
# Read crash input
|
|
crash_input = crash_file.read_bytes()
|
|
|
|
# Reproduce crash to get stack trace
|
|
cmd = [
|
|
"cargo", "fuzz", "run",
|
|
target_name,
|
|
str(crash_file)
|
|
]
|
|
|
|
proc = await asyncio.create_subprocess_exec(
|
|
*cmd,
|
|
cwd=workspace,
|
|
stdout=asyncio.subprocess.PIPE,
|
|
stderr=asyncio.subprocess.STDOUT,
|
|
env={**os.environ, "RUST_BACKTRACE": "1"}
|
|
)
|
|
|
|
stdout, _ = await proc.communicate()
|
|
output = stdout.decode('utf-8', errors='ignore')
|
|
|
|
# Parse stack trace and error type
|
|
error_type = "Unknown Crash"
|
|
stack_trace = output
|
|
|
|
# Extract error type
|
|
if "SEGV" in output:
|
|
error_type = "Segmentation Fault"
|
|
severity = "critical"
|
|
elif "heap-use-after-free" in output:
|
|
error_type = "Use After Free"
|
|
severity = "critical"
|
|
elif "heap-buffer-overflow" in output:
|
|
error_type = "Heap Buffer Overflow"
|
|
severity = "critical"
|
|
elif "stack-buffer-overflow" in output:
|
|
error_type = "Stack Buffer Overflow"
|
|
severity = "high"
|
|
elif "panic" in output.lower():
|
|
error_type = "Panic"
|
|
severity = "medium"
|
|
else:
|
|
severity = "high"
|
|
|
|
# Create finding
|
|
finding = self.create_finding(
|
|
title=f"Crash: {error_type} in {target_name}",
|
|
description=f"Cargo-fuzz discovered a crash in target '{target_name}'. "
|
|
f"Error type: {error_type}. "
|
|
f"Input size: {len(crash_input)} bytes.",
|
|
severity=severity,
|
|
category="crash",
|
|
file_path=f"fuzz/fuzz_targets/{target_name}.rs",
|
|
code_snippet=stack_trace[:500],
|
|
recommendation="Review the crash details and fix the underlying bug. "
|
|
"Use AddressSanitizer to identify memory safety issues. "
|
|
"Consider adding bounds checks or using safer APIs.",
|
|
metadata={
|
|
"error_type": error_type,
|
|
"crash_file": crash_file.name,
|
|
"input_size": len(crash_input),
|
|
"reproducer": crash_file.name,
|
|
"stack_trace": stack_trace
|
|
}
|
|
)
|
|
|
|
return finding
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Failed to analyze crash {crash_file}: {e}")
|
|
return None
|