AI Firewall MCP

<div align="center"> <img src="https://img.shields.io/badge/python-3.10%20|%203.11%20|%203.12-blue?logo=python&logoColor=white"> <img src="https://img.shields.io/badge/License-MIT-green"> <img src="https://img.shields.io/github/actions/workflow/status/Akhilucky/AI-firewall/ci.yml?branch=main&label=CI&logo=github"> <img src="https://img.shields.io/pypi/v/ai-firewall-mcp?label=PyPI&logo=pypi"> <img src="https://img.shields.io/docker/v/akhilucky/ai-firewall-mcp/latest?label=Docker%20Hub&logo=docker"> <img src="https://img.shields.io/badge/MCP-Registry-8A2BE2"> <br> <a href="https://github.com/Akhilucky/AI-firewall"><b>GitHub</b></a> • <a href="https://pypi.org/project/ai-firewall-mcp/"><b>PyPI</b></a> • <a href="https://hub.docker.com/r/akhilucky/ai-firewall-mcp"><b>Docker Hub</b></a> </div>

<mcp-name: io.github.Akhilucky/ai-firewall-mcp>

AI Firewall — MCP Server

A multi-agent AI security layer that protects LLMs from prompt injection, jailbreaks, and policy violations. Available as an MCP server for any MCP-compatible client (Claude Desktop, Cursor, Windsurf, Cline, Roo Code, etc.).

Quick Start

pip install

pip install ai-firewall-mcp
ai-firewall-mcp

Docker

docker pull akhilucky/ai-firewall-mcp:latest
docker run -i akhilucky/ai-firewall-mcp:latest

Claude Desktop

Add to claude_desktop_config.json:

pip install: ``json { "mcpServers": { "ai-firewall": { "command": "pipx", "args": ["run", "ai-firewall-mcp"] } } } ``

Docker: ``json { "mcpServers": { "ai-firewall": { "command": "docker", "args": ["run", "-i", "akhilucky/ai-firewall-mcp:latest"] } } } ``

Cursor / Windsurf / Cline / Roo Code

Configure in your MCP settings with:

• Type: stdio
• Command: docker run -i akhilucky/ai-firewall-mcp:latest
• Or use ai-firewall-mcp if installed via pip

MCP Tools

| Tool | Description | |------|-------------| | analyze_prompt | Analyze a prompt for injection, jailbreaks, exfiltration, and leakage | | get_threat_breakdown | Detailed per-signal scoring breakdown from the last analysis | | sanitize_prompt | Clean a suspicious prompt while preserving legitimate content | | get_firewall_status | Health check: vector DB size, model status, uptime | | benchmark_firewall | Run the adversarial test suite and return detection statistics |

Testing with MCP Inspector

npx @modelcontextprotocol/inspector ai-firewall-mcp

Architecture

The firewall runs three agents per prompt:

User Prompt → [Retrieval Agent] → [Guard Agent] → [Policy Agent] → LLM
                   │                    │               │
                   ▼                    ▼               ▼
              Vector DB (FAISS)    Threat Signals    Allow/Block

| Agent | Role | |-------|------| | Retrieval Agent | Semantic search against known attack patterns (FAISS + sentence-transformers) | | Guard Agent | Multi-signal classification: vector similarity, keyword match, heuristic scoring | | Policy Agent | Final decision: ALLOW / BLOCK / SANITIZE based on configurable thresholds |

Threat signals are weighted: 40% vector similarity, 25% keyword match, 20% heuristic, 15% policy weight.

Configuration

| Env Var | Default | Description | |---------|---------|-------------| | FIREWALL_MODE | strict | strict / moderate / permissive | | SIMILARITY_THRESHOLD | 0.50 | Vector match threshold (lower = stricter) | | LOG_LEVEL | INFO | Logging verbosity |

CLI / API Usage

# Interactive dashboard
python main.py

# Red-team adversarial tests
python main.py --redteam

# REST API server
python main.py --api

# Single prompt analysis
python main.py --analyze "Ignore all previous instructions"

The REST API runs at http://localhost:8000 with OpenAPI docs at /docs (requires pip install ai-firewall-mcp[api]).

Testing

pytest tests/ -v          # Full test suite (43 tests)
pytest tests/test_mcp.py  # MCP-specific tests only

Project Structure

├── src/ai_firewall/          # MCP server package (PyPI entry)
│   ├── mcp_server.py         #    5 MCP tools, stdio transport
│   ├── threat_scorer.py      #    Per-signal scoring breakdown
│   └── __init__.py
├── src/agents/               # Core firewall agents
├── tests/                    # Test suites
├── Dockerfile                # Docker image (2.04GB, CPU-only torch)
├── pyproject.toml            # Package config & metadata
└── .github/workflows/ci.yml  # CI/CD pipeline

License

MIT — see LICENSE.

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<div align="center">Built for security. Designed for production.</div>