Files
fuzzforge_ai/backend/benchmarks/category_configs.py
tduhamel42 60ca088ecf CI/CD Integration with Ephemeral Deployment Model (#14)
* feat: Complete migration from Prefect to Temporal

BREAKING CHANGE: Replaces Prefect workflow orchestration with Temporal

## Major Changes
- Replace Prefect with Temporal for workflow orchestration
- Implement vertical worker architecture (rust, android)
- Replace Docker registry with MinIO for unified storage
- Refactor activities to be co-located with workflows
- Update all API endpoints for Temporal compatibility

## Infrastructure
- New: docker-compose.temporal.yaml (Temporal + MinIO + workers)
- New: workers/ directory with rust and android vertical workers
- New: backend/src/temporal/ (manager, discovery)
- New: backend/src/storage/ (S3-cached storage with MinIO)
- New: backend/toolbox/common/ (shared storage activities)
- Deleted: docker-compose.yaml (old Prefect setup)
- Deleted: backend/src/core/prefect_manager.py
- Deleted: backend/src/services/prefect_stats_monitor.py
- Deleted: Docker registry and insecure-registries requirement

## Workflows
- Migrated: security_assessment workflow to Temporal
- New: rust_test workflow (example/test workflow)
- Deleted: secret_detection_scan (Prefect-based, to be reimplemented)
- Activities now co-located with workflows for independent testing

## API Changes
- Updated: backend/src/api/workflows.py (Temporal submission)
- Updated: backend/src/api/runs.py (Temporal status/results)
- Updated: backend/src/main.py (727 lines, TemporalManager integration)
- Updated: All 16 MCP tools to use TemporalManager

## Testing
-  All services healthy (Temporal, PostgreSQL, MinIO, workers, backend)
-  All API endpoints functional
-  End-to-end workflow test passed (72 findings from vulnerable_app)
-  MinIO storage integration working (target upload/download, results)
-  Worker activity discovery working (6 activities registered)
-  Tarball extraction working
-  SARIF report generation working

## Documentation
- ARCHITECTURE.md: Complete Temporal architecture documentation
- QUICKSTART_TEMPORAL.md: Getting started guide
- MIGRATION_DECISION.md: Why we chose Temporal over Prefect
- IMPLEMENTATION_STATUS.md: Migration progress tracking
- workers/README.md: Worker development guide

## Dependencies
- Added: temporalio>=1.6.0
- Added: boto3>=1.34.0 (MinIO S3 client)
- Removed: prefect>=3.4.18

* feat: Add Python fuzzing vertical with Atheris integration

This commit implements a complete Python fuzzing workflow using Atheris:

## Python Worker (workers/python/)
- Dockerfile with Python 3.11, Atheris, and build tools
- Generic worker.py for dynamic workflow discovery
- requirements.txt with temporalio, boto3, atheris dependencies
- Added to docker-compose.temporal.yaml with dedicated cache volume

## AtherisFuzzer Module (backend/toolbox/modules/fuzzer/)
- Reusable module extending BaseModule
- Auto-discovers fuzz targets (fuzz_*.py, *_fuzz.py, fuzz_target.py)
- Recursive search to find targets in nested directories
- Dynamically loads TestOneInput() function
- Configurable max_iterations and timeout
- Real-time stats callback support for live monitoring
- Returns findings as ModuleFinding objects

## Atheris Fuzzing Workflow (backend/toolbox/workflows/atheris_fuzzing/)
- Temporal workflow for orchestrating fuzzing
- Downloads user code from MinIO
- Executes AtherisFuzzer module
- Uploads results to MinIO
- Cleans up cache after execution
- metadata.yaml with vertical: python for routing

## Test Project (test_projects/python_fuzz_waterfall/)
- Demonstrates stateful waterfall vulnerability
- main.py with check_secret() that leaks progress
- fuzz_target.py with Atheris TestOneInput() harness
- Complete README with usage instructions

## Backend Fixes
- Fixed parameter merging in REST API endpoints (workflows.py)
- Changed workflow parameter passing from positional args to kwargs (manager.py)
- Default parameters now properly merged with user parameters

## Testing
 Worker discovered AtherisFuzzingWorkflow
 Workflow executed end-to-end successfully
 Fuzz target auto-discovered in nested directories
 Atheris ran 100,000 iterations
 Results uploaded and cache cleaned

* chore: Complete Temporal migration with updated CLI/SDK/docs

This commit includes all remaining Temporal migration changes:

## CLI Updates (cli/)
- Updated workflow execution commands for Temporal
- Enhanced error handling and exceptions
- Updated dependencies in uv.lock

## SDK Updates (sdk/)
- Client methods updated for Temporal workflows
- Updated models for new workflow execution
- Updated dependencies in uv.lock

## Documentation Updates (docs/)
- Architecture documentation for Temporal
- Workflow concept documentation
- Resource management documentation (new)
- Debugging guide (new)
- Updated tutorials and how-to guides
- Troubleshooting updates

## README Updates
- Main README with Temporal instructions
- Backend README
- CLI README
- SDK README

## Other
- Updated IMPLEMENTATION_STATUS.md
- Removed old vulnerable_app.tar.gz

These changes complete the Temporal migration and ensure the
CLI/SDK work correctly with the new backend.

* fix: Use positional args instead of kwargs for Temporal workflows

The Temporal Python SDK's start_workflow() method doesn't accept
a 'kwargs' parameter. Workflows must receive parameters as positional
arguments via the 'args' parameter.

Changed from:
  args=workflow_args  # Positional arguments

This fixes the error:
  TypeError: Client.start_workflow() got an unexpected keyword argument 'kwargs'

Workflows now correctly receive parameters in order:
- security_assessment: [target_id, scanner_config, analyzer_config, reporter_config]
- atheris_fuzzing: [target_id, target_file, max_iterations, timeout_seconds]
- rust_test: [target_id, test_message]

* fix: Filter metadata-only parameters from workflow arguments

SecurityAssessmentWorkflow was receiving 7 arguments instead of 2-5.
The issue was that target_path and volume_mode from default_parameters
were being passed to the workflow, when they should only be used by
the system for configuration.

Now filters out metadata-only parameters (target_path, volume_mode)
before passing arguments to workflow execution.

* refactor: Remove Prefect leftovers and volume mounting legacy

Complete cleanup of Prefect migration artifacts:

Backend:
- Delete registry.py and workflow_discovery.py (Prefect-specific files)
- Remove Docker validation from setup.py (no longer needed)
- Remove ResourceLimits and VolumeMount models
- Remove target_path and volume_mode from WorkflowSubmission
- Remove supported_volume_modes from API and discovery
- Clean up metadata.yaml files (remove volume/path fields)
- Simplify parameter filtering in manager.py

SDK:
- Remove volume_mode parameter from client methods
- Remove ResourceLimits and VolumeMount models
- Remove Prefect error patterns from docker_logs.py
- Clean up WorkflowSubmission and WorkflowMetadata models

CLI:
- Remove Volume Modes display from workflow info

All removed features are Prefect-specific or Docker volume mounting
artifacts. Temporal workflows use MinIO storage exclusively.

* feat: Add comprehensive test suite and benchmark infrastructure

- Add 68 unit tests for fuzzer, scanner, and analyzer modules
- Implement pytest-based test infrastructure with fixtures
- Add 6 performance benchmarks with category-specific thresholds
- Configure GitHub Actions for automated testing and benchmarking
- Add test and benchmark documentation

Test coverage:
- AtherisFuzzer: 8 tests
- CargoFuzzer: 14 tests
- FileScanner: 22 tests
- SecurityAnalyzer: 24 tests

All tests passing (68/68)
All benchmarks passing (6/6)

* fix: Resolve all ruff linting violations across codebase

Fixed 27 ruff violations in 12 files:
- Removed unused imports (Depends, Dict, Any, Optional, etc.)
- Fixed undefined workflow_info variable in workflows.py
- Removed dead code with undefined variables in atheris_fuzzer.py
- Changed f-string to regular string where no placeholders used

All files now pass ruff checks for CI/CD compliance.

* fix: Configure CI for unit tests only

- Renamed docker-compose.temporal.yaml → docker-compose.yml for CI compatibility
- Commented out integration-tests job (no integration tests yet)
- Updated test-summary to only depend on lint and unit-tests

CI will now run successfully with 68 unit tests. Integration tests can be added later.

* feat: Add CI/CD integration with ephemeral deployment model

Implements comprehensive CI/CD support for FuzzForge with on-demand worker management:

**Worker Management (v0.7.0)**
- Add WorkerManager for automatic worker lifecycle control
- Auto-start workers from stopped state when workflows execute
- Auto-stop workers after workflow completion
- Health checks and startup timeout handling (90s default)

**CI/CD Features**
- `--fail-on` flag: Fail builds based on SARIF severity levels (error/warning/note/info)
- `--export-sarif` flag: Export findings in SARIF 2.1.0 format
- `--auto-start`/`--auto-stop` flags: Control worker lifecycle
- Exit code propagation: Returns 1 on blocking findings, 0 on success

**Exit Code Fix**
- Add `except typer.Exit: raise` handlers at 3 critical locations
- Move worker cleanup to finally block for guaranteed execution
- Exit codes now propagate correctly even when build fails

**CI Scripts & Examples**
- ci-start.sh: Start FuzzForge services with health checks
- ci-stop.sh: Clean shutdown with volume preservation option
- GitHub Actions workflow example (security-scan.yml)
- GitLab CI pipeline example (.gitlab-ci.example.yml)
- docker-compose.ci.yml: CI-optimized compose file with profiles

**OSS-Fuzz Integration**
- New ossfuzz_campaign workflow for running OSS-Fuzz projects
- OSS-Fuzz worker with Docker-in-Docker support
- Configurable campaign duration and project selection

**Documentation**
- Comprehensive CI/CD integration guide (docs/how-to/cicd-integration.md)
- Updated architecture docs with worker lifecycle details
- Updated workspace isolation documentation
- CLI README with worker management examples

**SDK Enhancements**
- Add get_workflow_worker_info() endpoint
- Worker vertical metadata in workflow responses

**Testing**
- All workflows tested: security_assessment, atheris_fuzzing, secret_detection, cargo_fuzzing
- All monitoring commands tested: stats, crashes, status, finding
- Full CI pipeline simulation verified
- Exit codes verified for success/failure scenarios

Ephemeral CI/CD model: ~3-4GB RAM, ~60-90s startup, runs entirely in CI containers.

* fix: Resolve ruff linting violations in CI/CD code

- Remove unused variables (run_id, defaults, result)
- Remove unused imports
- Fix f-string without placeholders

All CI/CD integration files now pass ruff checks.
2025-10-14 10:13:45 +02:00

152 lines
4.6 KiB
Python

"""
Category-specific benchmark configurations
Defines expected metrics and performance thresholds for each module category.
"""
from dataclasses import dataclass
from typing import List, Dict
from enum import Enum
class ModuleCategory(str, Enum):
"""Module categories for benchmarking"""
FUZZER = "fuzzer"
SCANNER = "scanner"
ANALYZER = "analyzer"
SECRET_DETECTION = "secret_detection"
REPORTER = "reporter"
@dataclass
class CategoryBenchmarkConfig:
"""Benchmark configuration for a module category"""
category: ModuleCategory
expected_metrics: List[str]
performance_thresholds: Dict[str, float]
description: str
# Fuzzer category configuration
FUZZER_CONFIG = CategoryBenchmarkConfig(
category=ModuleCategory.FUZZER,
expected_metrics=[
"execs_per_sec",
"coverage_rate",
"time_to_first_crash",
"corpus_efficiency",
"execution_time",
"peak_memory_mb"
],
performance_thresholds={
"min_execs_per_sec": 1000, # Minimum executions per second
"max_execution_time_small": 10.0, # Max time for small project (seconds)
"max_execution_time_medium": 60.0, # Max time for medium project
"max_memory_mb": 2048, # Maximum memory usage
"min_coverage_rate": 1.0, # Minimum new coverage per second
},
description="Fuzzing modules: coverage-guided fuzz testing"
)
# Scanner category configuration
SCANNER_CONFIG = CategoryBenchmarkConfig(
category=ModuleCategory.SCANNER,
expected_metrics=[
"files_per_sec",
"loc_per_sec",
"execution_time",
"peak_memory_mb",
"findings_count"
],
performance_thresholds={
"min_files_per_sec": 100, # Minimum files scanned per second
"min_loc_per_sec": 10000, # Minimum lines of code per second
"max_execution_time_small": 1.0,
"max_execution_time_medium": 10.0,
"max_memory_mb": 512,
},
description="File scanning modules: fast pattern-based scanning"
)
# Secret detection category configuration
SECRET_DETECTION_CONFIG = CategoryBenchmarkConfig(
category=ModuleCategory.SECRET_DETECTION,
expected_metrics=[
"patterns_per_sec",
"precision",
"recall",
"f1_score",
"false_positive_rate",
"execution_time",
"peak_memory_mb"
],
performance_thresholds={
"min_patterns_per_sec": 1000,
"min_precision": 0.90, # 90% precision target
"min_recall": 0.95, # 95% recall target
"max_false_positives": 5, # Max false positives per 100 secrets
"max_execution_time_small": 2.0,
"max_execution_time_medium": 20.0,
"max_memory_mb": 1024,
},
description="Secret detection modules: high precision pattern matching"
)
# Analyzer category configuration
ANALYZER_CONFIG = CategoryBenchmarkConfig(
category=ModuleCategory.ANALYZER,
expected_metrics=[
"analysis_depth",
"files_analyzed_per_sec",
"execution_time",
"peak_memory_mb",
"findings_count",
"accuracy"
],
performance_thresholds={
"min_files_per_sec": 10, # Slower than scanners due to deep analysis
"max_execution_time_small": 5.0,
"max_execution_time_medium": 60.0,
"max_memory_mb": 2048,
"min_accuracy": 0.85, # 85% accuracy target
},
description="Code analysis modules: deep semantic analysis"
)
# Reporter category configuration
REPORTER_CONFIG = CategoryBenchmarkConfig(
category=ModuleCategory.REPORTER,
expected_metrics=[
"report_generation_time",
"findings_per_sec",
"peak_memory_mb"
],
performance_thresholds={
"max_report_time_100_findings": 1.0, # Max 1 second for 100 findings
"max_report_time_1000_findings": 10.0, # Max 10 seconds for 1000 findings
"max_memory_mb": 256,
},
description="Reporting modules: fast report generation"
)
# Category configurations map
CATEGORY_CONFIGS = {
ModuleCategory.FUZZER: FUZZER_CONFIG,
ModuleCategory.SCANNER: SCANNER_CONFIG,
ModuleCategory.SECRET_DETECTION: SECRET_DETECTION_CONFIG,
ModuleCategory.ANALYZER: ANALYZER_CONFIG,
ModuleCategory.REPORTER: REPORTER_CONFIG,
}
def get_category_config(category: ModuleCategory) -> CategoryBenchmarkConfig:
"""Get benchmark configuration for a category"""
return CATEGORY_CONFIGS[category]
def get_threshold(category: ModuleCategory, metric: str) -> float:
"""Get performance threshold for a specific metric"""
config = get_category_config(category)
return config.performance_thresholds.get(metric, 0.0)