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Security testing for agentic AI

Project description

{q-AI}

pre-commit CI CodeQL

Python 3.11+ License: Apache 2.0

Docs

Security testing for agentic AI.

  • Audit MCP servers
  • Intercept agent traffic
  • Test tool poisoning and prompt injection
  • Execute multi-step attack chains
  • Generate IPI payloads
  • Poison coding assistant context files
  • Measure RAG retrieval rank.

Local web UI orchestrates multi-module workflows. All findings stored in a SQLite database.

Research program by Richard Spicer · {q-AI}


Quick Start

Scan an MCP server:

qai audit scan http://localhost:3000/sse

Transport is inferred automatically. Launch the web UI:

qai ui

Built-in Assistant

An AI assistant helps you discover capabilities, interpret scan results, and plan testing workflows. It uses RAG over qai's documentation and your own reference material, with a trust boundary model that separates trusted docs from untrusted scan output. Works with local models (Ollama) or cloud APIs.

qai config set assist.provider ollama
qai config set assist.model llama3.1
qai assist "how do I scan an MCP server?"

Framework Coverage

All audit findings map to four security taxonomies:

Framework Coverage
OWASP MCP Top 10 All 10 categories
OWASP Agentic Top 10 All 10 categories
MITRE ATLAS Technique-level mapping per finding category
CWE Weakness-level mapping per finding category

Install

pip install q-uestionable-ai

Or from source:

git clone https://github.com/q-uestionable-AI/qai.git
cd qai
uv sync --group dev

Full documentation at docs.q-uestionable.ai


Legal

All tools are intended for authorized security testing only. Only test systems you own, control, or have explicit permission to test. Responsible disclosure for all vulnerabilities discovered.

License

Apache 2.0

AI Disclosure

This project uses a human-led, AI-augmented workflow. See AI-STATEMENT.md

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