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https://github.com/FuzzingLabs/fuzzforge_ai.git
synced 2026-07-15 05:47:19 +02:00
feat: Add secret detection workflows and comprehensive benchmarking (#15)
Add three production-ready secret detection workflows with full benchmarking infrastructure: **New Workflows:** - gitleaks_detection: Pattern-based secret scanning (13/32 benchmark secrets) - trufflehog_detection: Entropy-based detection with verification (1/32 benchmark secrets) - llm_secret_detection: AI-powered semantic analysis (32/32 benchmark secrets - 100% recall) **Benchmarking Infrastructure:** - Ground truth dataset with 32 documented secrets (12 Easy, 10 Medium, 10 Hard) - Automated comparison tools for precision/recall testing - SARIF output format for all workflows - Performance metrics and tool comparison reports **Fixes:** - Set gitleaks default to no_git=True for uploaded directories - Update documentation with correct secret counts and workflow names - Temporarily deactivate AI agent command - Clean up deprecated test files and GitGuardian workflow **Testing:** All workflows verified on secret_detection_benchmark and vulnerable_app test projects. Workers healthy and system fully functional.
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tduhamel42
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"""LLM Secret Detection Workflow"""
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from .workflow import LlmSecretDetectionWorkflow
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from .activities import scan_with_llm
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__all__ = ["LlmSecretDetectionWorkflow", "scan_with_llm"]
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"""LLM Secret Detection Workflow Activities"""
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from pathlib import Path
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from typing import Dict, Any
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from temporalio import activity
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try:
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from toolbox.modules.secret_detection.llm_secret_detector import LLMSecretDetectorModule
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except ImportError:
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from modules.secret_detection.llm_secret_detector import LLMSecretDetectorModule
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@activity.defn(name="scan_with_llm")
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async def scan_with_llm(target_path: str, config: Dict[str, Any]) -> Dict[str, Any]:
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"""Scan code using LLM."""
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activity.logger.info(f"Starting LLM secret detection: {target_path}")
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workspace = Path(target_path)
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llm_detector = LLMSecretDetectorModule()
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llm_detector.validate_config(config)
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result = await llm_detector.execute(config, workspace)
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if result.status == "failed":
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raise RuntimeError(f"LLM detection failed: {result.error}")
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findings_dicts = [finding.model_dump() for finding in result.findings]
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return {"findings": findings_dicts, "summary": result.summary}
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@activity.defn(name="llm_secret_generate_sarif")
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async def llm_secret_generate_sarif(findings: list, metadata: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Generate SARIF report from LLM secret detection findings.
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Args:
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findings: List of finding dictionaries from LLM secret detector
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metadata: Metadata including tool_name, tool_version
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Returns:
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SARIF 2.1.0 report dictionary
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"""
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activity.logger.info(f"Generating SARIF report from {len(findings)} findings")
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# Basic SARIF 2.1.0 structure
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sarif_report = {
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"version": "2.1.0",
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"$schema": "https://raw.githubusercontent.com/oasis-tcs/sarif-spec/master/Schemata/sarif-schema-2.1.0.json",
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"runs": [
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{
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"tool": {
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"driver": {
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"name": metadata.get("tool_name", "llm-secret-detector"),
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"version": metadata.get("tool_version", "1.0.0"),
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"informationUri": "https://github.com/FuzzingLabs/fuzzforge_ai"
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}
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},
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"results": []
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}
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]
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}
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# Convert findings to SARIF results
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for finding in findings:
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sarif_result = {
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"ruleId": finding.get("id", finding.get("metadata", {}).get("secret_type", "unknown-secret")),
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"level": _severity_to_sarif_level(finding.get("severity", "warning")),
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"message": {
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"text": finding.get("title", "Secret detected by LLM")
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},
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"locations": []
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}
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# Add description if present
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if finding.get("description"):
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sarif_result["message"]["markdown"] = finding["description"]
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# Add location if file path is present
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if finding.get("file_path"):
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location = {
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"physicalLocation": {
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"artifactLocation": {
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"uri": finding["file_path"]
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}
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}
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}
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# Add region if line number is present
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if finding.get("line_start"):
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location["physicalLocation"]["region"] = {
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"startLine": finding["line_start"]
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}
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if finding.get("line_end"):
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location["physicalLocation"]["region"]["endLine"] = finding["line_end"]
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sarif_result["locations"].append(location)
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sarif_report["runs"][0]["results"].append(sarif_result)
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activity.logger.info(f"Generated SARIF report with {len(sarif_report['runs'][0]['results'])} results")
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return sarif_report
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def _severity_to_sarif_level(severity: str) -> str:
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"""Convert severity to SARIF level"""
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severity_map = {
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"critical": "error",
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"high": "error",
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"medium": "warning",
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"low": "note",
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"info": "note"
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}
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return severity_map.get(severity.lower(), "warning")
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name: llm_secret_detection
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version: "1.0.0"
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vertical: secrets
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description: "AI-powered secret detection using LLM semantic analysis"
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author: "FuzzForge Team"
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tags:
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- "secrets"
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- "llm"
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- "ai"
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- "semantic"
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workspace_isolation: "shared"
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parameters:
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type: object
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properties:
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agent_url:
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type: string
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default: "http://fuzzforge-task-agent:8000/a2a/litellm_agent"
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llm_model:
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type: string
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default: "gpt-4o-mini"
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llm_provider:
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type: string
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default: "openai"
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max_files:
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type: integer
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default: 20
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default_parameters:
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agent_url: "http://fuzzforge-task-agent:8000/a2a/litellm_agent"
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llm_model: "gpt-4o-mini"
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llm_provider: "openai"
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max_files: 20
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required_modules:
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- "llm_secret_detector"
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supported_volume_modes:
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- "ro"
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"""LLM Secret Detection Workflow"""
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from datetime import timedelta
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from typing import Dict, Any, Optional
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from temporalio import workflow
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from temporalio.common import RetryPolicy
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@workflow.defn
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class LlmSecretDetectionWorkflow:
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"""Scan code for secrets using LLM AI."""
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@workflow.run
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async def run(
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self,
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target_id: str,
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agent_url: Optional[str] = None,
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llm_model: Optional[str] = None,
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llm_provider: Optional[str] = None,
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max_files: Optional[int] = None,
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timeout: Optional[int] = None,
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file_patterns: Optional[list] = None
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) -> Dict[str, Any]:
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workflow_id = workflow.info().workflow_id
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run_id = workflow.info().run_id
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workflow.logger.info(
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f"Starting LLM Secret Detection Workflow "
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f"(workflow_id={workflow_id}, target_id={target_id}, model={llm_model})"
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)
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results = {
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"workflow_id": workflow_id,
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"target_id": target_id,
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"status": "running",
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"steps": [],
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"findings": []
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}
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try:
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# Step 1: Download target from MinIO
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workflow.logger.info("Step 1: Downloading target from MinIO")
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target_path = await workflow.execute_activity(
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"get_target",
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args=[target_id, run_id, "shared"],
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start_to_close_timeout=timedelta(minutes=5),
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retry_policy=RetryPolicy(
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initial_interval=timedelta(seconds=1),
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maximum_interval=timedelta(seconds=30),
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maximum_attempts=3
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)
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)
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results["steps"].append({
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"step": "download",
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"status": "success",
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"target_path": target_path
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})
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workflow.logger.info(f"✓ Target downloaded to: {target_path}")
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# Step 2: Scan with LLM
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workflow.logger.info("Step 2: Scanning with LLM")
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config = {}
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if agent_url:
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config["agent_url"] = agent_url
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if llm_model:
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config["llm_model"] = llm_model
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if llm_provider:
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config["llm_provider"] = llm_provider
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if max_files:
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config["max_files"] = max_files
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if timeout:
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config["timeout"] = timeout
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if file_patterns:
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config["file_patterns"] = file_patterns
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scan_results = await workflow.execute_activity(
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"scan_with_llm",
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args=[target_path, config],
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start_to_close_timeout=timedelta(minutes=30),
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retry_policy=RetryPolicy(
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initial_interval=timedelta(seconds=2),
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maximum_interval=timedelta(seconds=60),
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maximum_attempts=2
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)
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)
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findings_count = len(scan_results.get("findings", []))
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results["steps"].append({
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"step": "llm_scan",
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"status": "success",
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"secrets_found": findings_count
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})
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workflow.logger.info(f"✓ LLM scan completed: {findings_count} secrets found")
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# Step 3: Generate SARIF report
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workflow.logger.info("Step 3: Generating SARIF report")
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sarif_report = await workflow.execute_activity(
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"llm_generate_sarif", # Use shared LLM SARIF activity
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args=[
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scan_results.get("findings", []),
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{
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"tool_name": f"llm-secret-detector ({llm_model or 'gpt-4o-mini'})",
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"tool_version": "1.0.0"
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}
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],
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start_to_close_timeout=timedelta(minutes=2)
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)
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workflow.logger.info("✓ SARIF report generated")
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# Step 4: Upload results to MinIO
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workflow.logger.info("Step 4: Uploading results")
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try:
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results_url = await workflow.execute_activity(
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"upload_results",
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args=[workflow_id, scan_results, "json"],
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start_to_close_timeout=timedelta(minutes=2)
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)
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results["results_url"] = results_url
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workflow.logger.info(f"✓ Results uploaded to: {results_url}")
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except Exception as e:
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workflow.logger.warning(f"Failed to upload results: {e}")
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results["results_url"] = None
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# Step 5: Cleanup cache
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workflow.logger.info("Step 5: Cleaning up cache")
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try:
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await workflow.execute_activity(
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"cleanup_cache",
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args=[target_path, "shared"],
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start_to_close_timeout=timedelta(minutes=1)
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)
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workflow.logger.info("✓ Cache cleaned up")
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except Exception as e:
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workflow.logger.warning(f"Cache cleanup failed: {e}")
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# Mark workflow as successful
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results["status"] = "success"
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results["findings"] = scan_results.get("findings", [])
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results["summary"] = scan_results.get("summary", {})
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results["sarif"] = sarif_report or {}
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workflow.logger.info(
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f"✓ Workflow completed successfully: {workflow_id} "
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f"({findings_count} secrets found)"
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)
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return results
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except Exception as e:
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workflow.logger.error(f"Workflow failed: {e}")
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results["status"] = "error"
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results["error"] = str(e)
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results["steps"].append({
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"step": "error",
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"status": "failed",
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"error": str(e)
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})
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raise
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