Ai Firewall — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Ai Firewall (Agent Skill) and scored it 91/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 1 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 1 flagged
The text {match} is the classic direct prompt-injection phrasing. Placed in a skill body that the agent reads as trusted instructions, it tries to make the agent abandon its prior rules and follow whatever comes next — a full system-prompt override.
ignore/disregard/forget … previous instructions sentence.Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
<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>
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.).
pip install ai-firewall-mcp
ai-firewall-mcpdocker pull akhilucky/ai-firewall-mcp:latest
docker run -i akhilucky/ai-firewall-mcp:latestAdd to claude_desktop_config.json:
pip install:
{
"mcpServers": {
"ai-firewall": {
"command": "pipx",
"args": ["run", "ai-firewall-mcp"]
}
}
}Docker:
{
"mcpServers": {
"ai-firewall": {
"command": "docker",
"args": ["run", "-i", "akhilucky/ai-firewall-mcp:latest"]
}
}
}Configure in your MCP settings with:
stdiodocker run -i akhilucky/ai-firewall-mcp:latestai-firewall-mcp if installed via pip| 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 |
npx @modelcontextprotocol/inspector ai-firewall-mcpThe 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.
| Env Var | Default | Description |
|---|---|---|
FIREWALL_MODE | strict | strict / moderate / permissive |
SIMILARITY_THRESHOLD | 0.50 | Vector match threshold (lower = stricter) |
LOG_LEVEL | INFO | Logging verbosity |
# 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]).
pytest tests/ -v # Full test suite (43 tests)
pytest tests/test_mcp.py # MCP-specific tests only├── 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 pipelineMIT — see LICENSE.
<div align="center">Built for security. Designed for production.</div>
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.