YaraFlux MCP Server: YARA-Driven Threat Detection & Enterprise Scale

YaraFlux MCP Server

A Model Context Protocol (MCP) server for YARA scanning, providing LLMs with capabilities to analyze files with YARA rules.

📋 Overview

YaraFlux MCP Server enables AI assistants to perform YARA rule-based threat analysis through the standardized Model Context Protocol interface. The server integrates YARA scanning with modern AI assistants, supporting comprehensive rule management, secure scanning, and detailed result analysis through a modular architecture.

🧩 Architecture Overview

+------------------------------------------+
|              AI Assistant                |
+--------------------+---------------------+
                    |
                    | Model Context Protocol
                    |
+--------------------v---------------------+
|              YaraFlux MCP Server         |
|                                          |
|  +----------------+    +---------------+ |
|  | MCP Server     |    | Tool Registry | |
|  +-------+--------+    +-------+-------+ |
|          |                     |         |
|  +-------v--------+    +-------v-------+ |
|  | YARA Service   |    | Storage Layer | |
|  +----------------+    +---------------+ |
|                                          |
+------------------------------------------+
          |                   |
 +-----------------+  +---------------+
 | YARA Engine     |  | Storage       |
 | - Rule Compiling|  | - Local FS    |
 | - File Scanning |  | - MinIO/S3    |
 +-----------------+  +---------------+

YaraFlux follows a modular architecture that separates concerns between:

• MCP Integration Layer : Handles communication with AI assistants
• Tool Implementation Layer : Implements YARA scanning and management functionality
• Storage Abstraction Layer : Provides flexible storage options
• YARA Engine Integration : Leverages YARA for scanning and rule management

For detailed architecture diagrams, see the Architecture Documentation.

✨ Features

• 🔄 Modular Architecture

  • Clean separation of MCP integration, tool implementation, and storage
  • Standardized parameter parsing and error handling
  • Flexible storage backend with local and S3/MinIO options

• 🤖 MCP Integration

  • 19 integrated MCP tools for comprehensive functionality
  • Optimized for Claude Desktop integration
  • Direct file analysis from within conversations
  • Compatible with latest MCP protocol specification

• 🔍 YARA Scanning

  • URL and file content scanning
  • Detailed match information with context
  • Scan result storage and retrieval
  • Performance-optimized scanning engine

• 📝 Rule Management

  • Create, read, update, delete YARA rules
  • Rule validation with detailed error reporting
  • Import rules from ThreatFlux repository
  • Categorization by source (custom vs. community)

• 📊 File Analysis

  • Hexadecimal view for binary analysis
  • String extraction with configurable parameters
  • File metadata and hash information
  • Secure file upload and storage

• 🔐 Security Features

  • JWT authentication for API access
  • Non-root container execution
  • Secure storage isolation
  • Configurable access controls

🚀 Quick Start

Using Docker Image

# Pull the latest Docker image
docker pull threatflux/yaraflux-mcp-server:latest
# Run the container
docker run -p 8000:8000 \
  -e JWT_SECRET_KEY=your-secret-key \
  -e ADMIN_PASSWORD=your-admin-password \
  -e DEBUG=true \
  threatflux/yaraflux-mcp-server:latest
### Using Docker building from source

```bash
# Clone the repository
git clone https://github.com/ThreatFlux/YaraFlux.git
cd YaraFlux/

# Build the Docker image
docker build -t yaraflux-mcp-server:latest .

# Run the container
docker run -p 8000:8000 \
  -e JWT_SECRET_KEY=your-secret-key \
  -e ADMIN_PASSWORD=your-admin-password \
  -e DEBUG=true \
  yaraflux-mcp-server:latest

Installation from Source

# Clone the repository
git clone https://github.com/ThreatFlux/YaraFlux.git
cd YaraFlux/

# Install dependencies (requires Python 3.13+)
make install

# Run the server
make run

🧩 Claude Desktop Integration

YaraFlux is designed for seamless integration with Claude Desktop through the Model Context Protocol.

1. Build the Docker image:

docker build -t yaraflux-mcp-server:latest .

2. Add to Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "yaraflux-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--env",
        "JWT_SECRET_KEY=your-secret-key",
        "--env",
        "ADMIN_PASSWORD=your-admin-password",
        "--env",
        "DEBUG=true",
        "--env",
        "PYTHONUNBUFFERED=1",
        "threatflux/yaraflux-mcp-server:latest"
      ],
      "disabled": false,
      "autoApprove": [
        "scan_url",
        "scan_data",
        "list_yara_rules",
        "get_yara_rule"
      ]
    }
  }
}

3. Restart Claude Desktop to activate the server.

🛠️ Available MCP Tools

YaraFlux exposes 19 integrated MCP tools:

Rule Management Tools

• list_yara_rules : List available YARA rules with filtering options
• get_yara_rule : Get a specific YARA rule's content and metadata
• validate_yara_rule : Validate YARA rule syntax with detailed error reporting
• add_yara_rule : Create a new YARA rule
• update_yara_rule : Update an existing YARA rule
• delete_yara_rule : Delete a YARA rule
• import_threatflux_rules : Import rules from ThreatFlux GitHub repository

Scanning Tools

• scan_url : Scan content from a URL with specified YARA rules
• scan_data : Scan provided data (base64 encoded) with specified rules
• get_scan_result : Retrieve detailed results from a previous scan

File Management Tools

• upload_file : Upload a file for analysis or scanning
• get_file_info : Get metadata about an uploaded file
• list_files : List uploaded files with pagination and sorting
• delete_file : Delete an uploaded file
• extract_strings : Extract ASCII/Unicode strings from a file
• get_hex_view : Get hexadecimal view of file content
• download_file : Download an uploaded file

Storage Management Tools

• get_storage_info : Get storage usage statistics
• clean_storage : Remove old files to free up storage space

📚 Documentation

Comprehensive documentation is available in the docs/ directory:

• Architecture Diagrams - Visual representation of system architecture
• Code Analysis - Detailed code structure and recommendations
• Installation Guide - Detailed setup instructions
• CLI Usage Guide - Command-line interface documentation
• API Reference - REST API endpoints and usage
• YARA Rules Guide - Creating and managing YARA rules
• MCP Integration - Model Context Protocol integration details
• File Management - File handling capabilities
• Examples - Real-world usage examples

🗂️ Project Structure

yaraflux_mcp_server/
├── src/
│   └── yaraflux_mcp_server/
│       ├── app.py                 # FastAPI application
│       ├── auth.py                # JWT authentication and user management
│       ├── config.py              # Configuration settings loader
│       ├── models.py              # Pydantic models for requests/responses
│       ├── mcp_server.py          # MCP server implementation
│       ├── utils/                 # Utility functions package
│       │   ├── __init__.py        # Package initialization
│       │   ├── error_handling.py  # Standardized error handling
│       │   ├── param_parsing.py   # Parameter parsing utilities
│       │   └── wrapper_generator.py # Tool wrapper generation
│       ├── mcp_tools/             # Modular MCP tools package
│       │   ├── __init__.py        # Package initialization
│       │   ├── base.py            # Base tool registration utilities
│       │   ├── file_tools.py      # File management tools
│       │   ├── rule_tools.py      # YARA rule management tools
│       │   ├── scan_tools.py      # Scanning tools
│       │   └── storage_tools.py   # Storage management tools
│       ├── storage/               # Storage implementation package
│       │   ├── __init__.py        # Package initialization
│       │   ├── base.py            # Base storage interface
│       │   ├── factory.py         # Storage client factory
│       │   ├── local.py           # Local filesystem storage
│       │   └── minio.py           # MinIO/S3 storage
│       ├── routers/               # API route definitions
│       │   ├── __init__.py        # Package initialization
│       │   ├── auth.py            # Authentication API routes
│       │   ├── files.py           # File management API routes
│       │   ├── rules.py           # YARA rule management API routes
│       │   └── scan.py            # YARA scanning API routes
│       ├── yara_service.py        # YARA rule management and scanning
│       ├── __init__.py            # Package initialization
│       └── __main__.py            # CLI entry point
├── docs/                          # Documentation
├── tests/                         # Test suite
├── Dockerfile                     # Docker configuration
├── entrypoint.sh                  # Container entrypoint script
├── Makefile                       # Build automation
├── pyproject.toml                 # Project metadata and dependencies
├── requirements.txt               # Core dependencies
└── requirements-dev.txt           # Development dependencies

🧪 Development

Local Development

# Set up development environment
make dev-setup

# Run tests
make test

# Code quality checks
make lint
make format
make security-check

# Generate test coverage report
make coverage

# Run development server
make run

CI/CD Workflows

This project uses GitHub Actions for continuous integration and deployment:

• CI Tests : Runs on every push and pull request to main and develop branches

  • Runs tests, formatting, linting, and type checking
  • Builds and tests Docker images
  • Uploads test coverage reports to Codecov

• Version Auto-increment : Automatically increments version on pushes to main branch

  • Updates version in pyproject.toml, setup.py, and Dockerfile
  • Creates git tag for new version

• Publish Release : Triggered after successful version auto-increment

  • Builds Docker images for multiple stages
  • Generates release notes from git commits
  • Creates GitHub release with artifacts
  • Publishes Docker images to Docker Hub

These workflows ensure code quality and automate the release process.

Status Checks

The following status checks run on pull requests:

• ✅ Format Verification : Ensures code follows Black and isort formatting standards
• ✅ Lint Verification : Validates code quality and compliance with coding standards
• ✅ Test Execution : Runs the full test suite to verify functionality
• ✅ Coverage Report : Ensures sufficient test coverage of the codebase

🌐 API Documentation

Interactive API documentation available at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

For detailed API documentation, see API Reference.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

💖 Donate or Ask for Features

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• PayPal