LitterBox

LitterBox is an MCP-enabled server that provides a controlled sandbox environment for security professionals to develop, test, and analyze payloads and malware behavior. It exposes a range of analysis capabilities similar to those described in its documentation, including static and dynamic analysis tooling, BYOVD scanning using HolyGrail workflows, and Doppelganger analysis modules (Blender and FuzzyHash) for IOC and file similarity analysis. The server is designed to help red teams validate evasion techniques and blue teams verify detections within an isolated environment, while also offering LLM-assisted analytical insights via the MCP interface.

To use LitterBox, run the MCP server (via Docker in the recommended deployment) and connect to it through your MCP client or orchestration tool. Once active, you can submit samples for analysis, initiate BYOVD scans, and request detailed results for static, dynamic, and BYOVD analyses. The Doppelganger endpoints enable you to benchmark similarities between payloads and system processes, helping identify novel behaviors or potential stealth techniques. Consult the included API reference and integrated tools list to understand the available endpoints and their usage. The server is Docker-enabled, Python-based, and designed to integrate with the surrounding MCP ecosystem for streamlined workflows.

Prerequisites:

• Docker (recommended) or Python environment if you opt for a non-Docker deployment
• MCP client or orchestration environment compatible with MCP
• Basic familiarity with containerized deployments

Option A: Run with Docker (recommended)

1. Ensure Docker is installed and running on your host.
2. Pull the LitterBox image and run it via MCP configuration:

# If you are manually running for testing (not via MCP):
docker pull blacksnufkin/litterbox:latest
docker run -it --rm blacksnufkin/litterbox:latest

3. Integrate with your MCP setup by pointing the mcp_config to the Docker command (as shown in the configuration example).

Option B: Run directly with Python (if you have a Python deployment)

1. Ensure Python 3.11+ is installed.
2. Install dependencies (virtual environment recommended):

python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate     # Windows
pip install -r requirements.txt

3. Run the server directly (adjust module/name as appropriate for your package):

python -m litterbox        # or the appropriate package/module name

Option C: Other deployment methods

• Follow the MCP documentation to deploy via npx, uv, or pipx if you have the corresponding packaging available.
• Update the mcp_config with the chosen command/arguments.

Post-installation: verify the server is reachable via your MCP client, submit a sample for analysis, and review the analysis results in your MCP UI or API client.

Tips and considerations:

• Ensure you configure appropriate resource limits for the sandbox (CPU, memory) to prevent runaway analysis tasks.
• If BYOVD analysis is used, ensure the environment has access to necessary driver datasets and that you comply with internal security policies.
• The included endpoints expose static, dynamic, BYOVD, and Doppelganger analytics; reference the API guide in the README for exact endpoint usage.
• If you encounter container permissions issues, verify that your Docker user has necessary privileges on the host.
• For environment variables, you can set specific paths or tokens in the env block of the MCP config if your deployment requires them (e.g., API keys, config paths).