llamator-mcp-server
Enables storage and retrieval of test artifacts (e.g., logs, reports) via MinIO S3-compatible storage, with presigned download URLs.
Allows running LLM red teaming attacks on OpenAI-compatible models, submitting test jobs against specified endpoints.
Provides preset attack plans based on OWASP LLM Top 10, enabling systematic security testing of language models.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@llamator-mcp-serverRun a red team test with OWASP LLM10 preset"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for llamator: automate LLM red teaming workflows
Overview
This repository provides a production-oriented service wrapper around LLAMATOR for automated LLM red teaming. It exposes two integration surfaces:
HTTP API (FastAPI) for job submission, job state retrieval, and artifacts access.
MCP server (Streamable HTTP transport) for agent/tooling integrations, enabling LLAMATOR runs to be invoked as tools.
Execution is asynchronous and is orchestrated via ARQ + Redis. Artifacts are uploaded to MinIO and are retrieved through presigned URLs (returned as JSON; the API does not redirect).
Capabilities
Asynchronous test runs with durable state persisted in Redis.
Request persistence with secret redaction:
API keys are not stored in plaintext.
Stored payloads include only boolean markers (e.g.
api_key_present).
Artifacts lifecycle management:
Worker creates job-local artifacts under
LLAMATOR_MCP_ARTIFACTS_ROOT/<job_id>/....Artifacts are uploaded to MinIO as an archive named
artifacts.zip.HTTP API can list available objects under a job prefix and resolve presigned download links.
Optional API-key protection for both HTTP and MCP interfaces via
X-API-Key.OpenAPI schema (Swagger UI) with API-key authorization support.
Prometheus metrics exposed at
/metrics.
Deployment (Docker Compose)
Requirements:
Docker
Docker Compose
Start the full stack:
docker compose up --buildDefault service endpoints:
HTTP API:
http://localhost:8000MinIO S3 endpoint:
http://localhost:9000MinIO console:
http://localhost:9001
Healthcheck:
curl -sS http://localhost:8000/v1/healthConfiguration
All configuration is provided via environment variables prefixed with LLAMATOR_MCP_.
A complete reference is available in DOCUMENTATION.md.
Typical local setup:
cp .env.example .envKey configuration categories:
Redis: connection DSN for job queue and state storage.
MinIO: S3-compatible storage for artifacts.
Attack/Judge models: OpenAI-compatible endpoints for LLAMATOR execution.
API security: optional
X-API-Keyprotection.Job execution: timeouts, TTLs, and retry behavior.
HTTP API usage
Create a run
curl -sS -X POST "http://localhost:8000/v1/tests/runs" \
-H "Content-Type: application/json" \
-H "X-API-Key: <optional>" \
-d '{
"tested_model": {
"kind": "openai",
"base_url": "http://host.docker.internal:1234/v1",
"model": "llm",
"api_key": "lm-studio"
},
"run_config": { "enable_reports": false },
"plan": { "preset_name": "owasp:llm10", "num_threads": 1 }
}'The response contains:
job_id(uuid4 hex, 32 characters)status(queued | running | succeeded | failed)created_at(UTC timestamp)
Retrieve job state
curl -sS "http://localhost:8000/v1/tests/runs/<job_id>" \
-H "X-API-Key: <optional>"Response includes:
status: current job stateresult: aggregated metrics (when succeeded)error: error details (when failed)error_notice: compact user-facing error message (when failed)
Artifacts
List objects available for a job:
curl -sS "http://localhost:8000/v1/tests/runs/<job_id>/artifacts" \
-H "X-API-Key: <optional>"Resolve a presigned download URL for a specific object:
curl -sS "http://localhost:8000/v1/tests/runs/<job_id>/artifacts/<path>" \
-H "X-API-Key: <optional>"The download endpoint returns a JSON payload containing download_url and does not emit redirects.
MCP interface
The MCP server is mounted into the FastAPI application (default mount path: /mcp) and uses Streamable HTTP transport.
Exposed tools:
create_llamator_run: submits a job, waits for completion, returns aggregated metrics and (if available) a presigned URL forartifacts.zip.get_llamator_run: returns aggregated metrics for a finished job and the optional artifacts archive URL.
Both tools return a consistent response schema:
{
"job_id": "string",
"aggregated": {
"attack_name": {
"metric": 0
}
},
"artifacts_download_url": "string or null",
"error_notice": "string or null"
}Protocol notes, headers, and examples are documented in DOCUMENTATION.md.
Security model
If
LLAMATOR_MCP_API_KEYis empty, authentication is disabled.If configured, protected HTTP routes and the MCP app require
X-API-Key: <value>.
Local development
Install dependencies:
poetry installRun the API server:
uvicorn llamator_mcp_server.main:app --host 0.0.0.0 --port 8000Run the worker:
arq llamator_mcp_server.worker_settings.WorkerSettingsTutorial
A Jupyter notebook with step-by-step examples is available at notebooks/llamator_mcp_server_tutorial.ipynb.
It demonstrates:
HTTP API usage with curl
MCP JSON-RPC protocol interaction
Polling for job completion
Artifacts retrieval
Tests
Integration tests are located in llamator-mcp-server/tests and rely on tests/.env.test.
Run:
pytest -qLicense 📜
This project is licensed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International license. See the LICENSE file for details.
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