mirror of
https://github.com/FuzzingLabs/fuzzforge_ai.git
synced 2026-07-08 22:58:43 +02:00
feat: Add LLM analysis workflow and ruff linter fixes
LLM Analysis Workflow: - Add llm_analyzer module for AI-powered code security analysis - Add llm_analysis workflow with SARIF output support - Mount AI module in Python worker for A2A wrapper access - Add a2a-sdk dependency to Python worker requirements - Fix workflow parameter ordering in Temporal manager Ruff Linter Fixes: - Fix bare except clauses (E722) across AI and CLI modules - Add noqa comments for intentional late imports (E402) - Replace undefined get_ai_status_async with TODO placeholder - Remove unused imports and variables - Remove container diagnostics display from exception handler MCP Configuration: - Reactivate FUZZFORGE_MCP_URL with default value - Set default MCP URL to http://localhost:8010/mcp in init
This commit is contained in:
tduhamel42
@@ -0,0 +1,19 @@
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"""
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LLM Analysis Workflow
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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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from .workflow import LlmAnalysisWorkflow
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from .activities import analyze_with_llm
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__all__ = ["LlmAnalysisWorkflow", "analyze_with_llm"]
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"""
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LLM Analysis Workflow Activities
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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 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.analyzer.llm_analyzer import LLMAnalyzer
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except ImportError:
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try:
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from modules.analyzer.llm_analyzer import LLMAnalyzer
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except ImportError:
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from src.toolbox.modules.analyzer.llm_analyzer import LLMAnalyzer
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logger = logging.getLogger(__name__)
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@activity.defn(name="llm_generate_sarif")
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async def llm_generate_sarif(findings: list, metadata: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Generate SARIF report from LLM findings.
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Args:
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findings: List of finding dictionaries
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metadata: Metadata including tool_name, tool_version, run_id
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Returns:
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SARIF 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-analyzer"),
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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", "unknown"),
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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", "Security issue detected")
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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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@activity.defn(name="analyze_with_llm")
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async def analyze_with_llm(target_path: str, config: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Analyze code using LLM.
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Args:
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target_path: Path to the workspace containing code
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config: LLM analyzer configuration
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Returns:
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Dictionary containing findings and summary
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"""
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activity.logger.info(f"Starting LLM analysis: {target_path}")
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activity.logger.info(f"Config: {config}")
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workspace = Path(target_path)
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if not workspace.exists():
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raise FileNotFoundError(f"Workspace not found: {target_path}")
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# Create and execute LLM analyzer
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analyzer = LLMAnalyzer()
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# Validate configuration
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analyzer.validate_config(config)
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# Execute analysis
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result = await analyzer.execute(config, workspace)
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if result.status == "failed":
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raise RuntimeError(f"LLM analysis failed: {result.error or 'Unknown error'}")
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activity.logger.info(
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f"LLM analysis completed: {len(result.findings)} findings from "
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f"{result.summary.get('files_analyzed', 0)} files"
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)
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# Convert ModuleFinding objects to dicts for serialization
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findings_dicts = [finding.model_dump() for finding in result.findings]
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return {
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"findings": findings_dicts,
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"summary": result.summary
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}
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@@ -0,0 +1,64 @@
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name: llm_analysis
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version: "1.0.0"
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vertical: python
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description: "Uses AI/LLM to analyze code for security vulnerabilities and code quality issues"
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author: "FuzzForge Team"
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tags:
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- "llm"
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- "ai"
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- "security"
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- "static-analysis"
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- "code-quality"
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# Workspace isolation mode
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workspace_isolation: "shared"
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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: 5
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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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description: "A2A agent endpoint URL"
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llm_model:
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type: string
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description: "LLM model to use (e.g., gpt-4o-mini, claude-3-5-sonnet)"
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llm_provider:
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type: string
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description: "LLM provider (openai, anthropic, etc.)"
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file_patterns:
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type: array
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items:
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type: string
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description: "File patterns to analyze (e.g., ['*.py', '*.js'])"
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max_files:
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type: integer
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description: "Maximum number of files to analyze"
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max_file_size:
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type: integer
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description: "Maximum file size in bytes"
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timeout:
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type: integer
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description: "Timeout per file in seconds"
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output_schema:
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type: object
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properties:
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sarif:
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type: object
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description: "SARIF-formatted security findings from LLM"
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summary:
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type: object
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description: "Analysis summary"
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properties:
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files_analyzed:
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type: integer
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total_findings:
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type: integer
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model_used:
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type: string
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@@ -0,0 +1,236 @@
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"""
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LLM Analysis Workflow - Temporal Version
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Uses AI/LLM to analyze code for security issues.
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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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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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# Import for type hints (will be executed by worker)
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with workflow.unsafe.imports_passed_through():
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import logging
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logger = logging.getLogger(__name__)
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@workflow.defn
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class LlmAnalysisWorkflow:
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"""
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Analyze code using AI/LLM for security vulnerabilities.
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User workflow:
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1. User runs: ff workflow run llm_analysis .
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2. CLI uploads project to MinIO
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3. Worker downloads project
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4. Worker calls LLM analyzer module
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5. LLM analyzes code files and reports findings
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6. Results returned in SARIF format
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"""
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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, # MinIO UUID of uploaded user code
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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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file_patterns: Optional[list] = None,
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max_files: Optional[int] = None,
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max_file_size: Optional[int] = None,
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timeout: Optional[int] = None
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) -> Dict[str, Any]:
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"""
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Main workflow execution.
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Args:
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target_id: UUID of the uploaded target in MinIO
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agent_url: A2A agent endpoint URL
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llm_model: LLM model to use
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llm_provider: LLM provider
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file_patterns: File patterns to analyze
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max_files: Maximum number of files to analyze
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max_file_size: Maximum file size in bytes
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timeout: Timeout per file in seconds
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Returns:
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Dictionary containing findings and summary
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"""
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workflow_id = workflow.info().workflow_id
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workflow.logger.info(
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f"Starting LLMAnalysisWorkflow "
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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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# Get run ID for workspace isolation
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run_id = workflow.info().run_id
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# Step 1: Download user's project from MinIO
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workflow.logger.info("Step 1: Downloading user code 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: Run LLM analysis
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workflow.logger.info("Step 2: Analyzing code with LLM")
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# Build analyzer config
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analyzer_config = {}
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if agent_url:
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analyzer_config["agent_url"] = agent_url
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if llm_model:
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analyzer_config["llm_model"] = llm_model
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if llm_provider:
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analyzer_config["llm_provider"] = llm_provider
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if file_patterns:
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analyzer_config["file_patterns"] = file_patterns
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if max_files is not None:
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analyzer_config["max_files"] = max_files
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if max_file_size is not None:
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analyzer_config["max_file_size"] = max_file_size
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if timeout is not None:
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analyzer_config["timeout"] = timeout
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analysis_results = await workflow.execute_activity(
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"analyze_with_llm",
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args=[target_path, analyzer_config],
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start_to_close_timeout=timedelta(minutes=30), # LLM calls can be slow
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retry_policy=RetryPolicy(
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initial_interval=timedelta(seconds=5),
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maximum_interval=timedelta(minutes=1),
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maximum_attempts=2
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)
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)
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findings = analysis_results.get("findings", [])
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summary = analysis_results.get("summary", {})
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results["steps"].append({
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"step": "llm_analysis",
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"status": "success",
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"files_analyzed": summary.get("files_analyzed", 0),
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"findings_count": len(findings)
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})
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workflow.logger.info(
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f"✓ LLM analysis completed: "
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f"{summary.get('files_analyzed', 0)} files, "
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f"{len(findings)} findings"
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)
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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",
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args=[findings, {
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"tool_name": "llm-analyzer",
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"tool_version": "1.0.0",
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"run_id": run_id
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}],
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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": "sarif_generation",
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"status": "success",
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"results_count": len(sarif_report.get("runs", [{}])[0].get("results", []))
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})
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workflow.logger.info(
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f"✓ SARIF report generated: "
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f"{len(sarif_report.get('runs', [{}])[0].get('results', []))} results"
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)
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# Step 4: Upload results to MinIO
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workflow.logger.info("Step 4: Uploading results to MinIO")
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# Upload SARIF report
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if sarif_report:
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results_url = await workflow.execute_activity(
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"upload_results",
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args=[run_id, sarif_report],
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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["results_url"] = results_url
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workflow.logger.info(f"✓ Results uploaded to: {results_url}")
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# Step 5: Cleanup cache
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workflow.logger.info("Step 5: Cleaning up cache")
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await workflow.execute_activity(
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"cleanup_cache",
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args=[target_id],
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start_to_close_timeout=timedelta(minutes=2),
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retry_policy=RetryPolicy(
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initial_interval=timedelta(seconds=1),
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maximum_interval=timedelta(seconds=10),
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maximum_attempts=2
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)
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)
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workflow.logger.info("✓ Cache cleaned up")
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# Mark workflow as successful
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results["status"] = "success"
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results["sarif"] = sarif_report
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results["summary"] = summary
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results["findings"] = findings
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workflow.logger.info(
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f"✅ LLMAnalysisWorkflow completed successfully: "
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f"{len(findings)} findings"
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)
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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"] = "failed"
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results["error"] = str(e)
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raise
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return results
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