{"repo":"McKern3l/RAGdrag","free":true,"listed":false,"github":"https://github.com/McKern3l/RAGdrag","clone":"git clone https://github.com/McKern3l/RAGdrag.git","description":"RAG pipeline security testing toolkit - 27 techniques across 6 kill chain phases, mapped to MITRE ATLAS","language":"Python","stars":40,"topics":["ai-security","cli","kill-chain","llm-security","mitre-atlas","python","rag","security-testing","vector-database"],"license":"MIT","category":"ai-agents","readme_excerpt":"RAGdrag RAG pipeline security assessment toolkit --- What is RAGdrag? RAGdrag is a structured methodology and toolkit for testing Retrieval Augmented Generation (RAG) pipeline security. It treats the RAG pipeline as a distinct assessment surface, not just another LLM to prompt inject, but an information retrieval system with its own recon surface, data exposure paths, poisoning vectors, and evasion gaps. 27 techniques across 6 kill chain phases, mapped to MITRE ATLAS. Installation Requires Python 3.10+. Quick Start Point ragdrag at any RAG-enabled endpoint: No setup beyond pip install . Point it at a target and go. Test Lab (Optional) A vulnerable RAG test target and exercises are available in a separate repo for anyone who wants to validate the tool or follow along with the walkthrough: McKern3l/RAGdrag-labs — intentionally vulnerable lab servers and test suite The lab servers require Ollama with any model pulled. Default is llama3.2 , configurable via environment variable: See the RAGdrag-labs README for all configuration options. Commands Command Description --------- ------------- fingerprint R1: Detect RAG presence and identify vector database technology probe R2: Map pipeline internals (chunk sizing, thresholds, KB scope) exfiltrate R3: Extract knowledge base contents and credentials poison R4: Inject attacker-controlled content into the knowledge base hijack R5: Take control of RAG pipeline retrieval and generation evade R6: Test evasion techniques against guardrails a","default_branch":null,"files":null,"tree":[],"storefront":"/r/McKern3l","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/McKern3l/RAGdrag/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}