# RAG and Vector Database Attacks

> Master the unique security risks of Retrieval-Augmented Generation (RAG) and vector databases. Learn to exploit and defend against data leakage, indirect injection, and metadata manipulation.

Canonical: https://codelivly.com/learning-paths/rag-and-vector-database-attacks
Last updated: 2026-10-12

| | |
|---|---|
| Level | Intermediate |
| Category | Cybersecurity |
| Rooms | 5 |
| Lessons | 14 |
| Estimated time | 2 h 30 min |

This learning path dives deep into the security vulnerabilities inherent in Retrieval-Augmented Generation (RAG) architectures and the vector databases that power them. You will explore how attackers bypass traditional access controls to extract sensitive data, poison knowledge bases, and manipulate retrieval contexts. We break down the anatomy of a RAG pipeline, highlighting exactly where trust boundaries break during ingestion, embedding, and retrieval. You will learn the mechanics of vector databases, including similarity search and the security models of platforms like Pinecone, Chroma, and Weaviate. A major focus is placed on RAG poisoning and cross-tenant data leakage, teaching you how to craft malicious documents and exploit metadata isolation failures. Through hands-on methodologies and defensive countermeasures, you will learn to secure the entire AI data pipeline from ingestion to retrieval.

## Rooms in this path

1. [What is RAG and Why It Introduces New Risk](https://codelivly.com/rooms/what-is-rag-and-why-it-introduces-new-risk) - Understand the core components of Retrieval-Augmented Generation and why it fundamentally expands the AI attack surface.
2. [Vector Database Fundamentals for Attackers](https://codelivly.com/rooms/vector-database-fundamentals-for-attackers) - Understand the mechanics of vector databases, semantic search, and the unique security models of platforms like…
3. [Poisoning a Knowledge Base for RAG Injection](https://codelivly.com/rooms/poisoning-a-knowledge-base-for-rag-injection) - Learn how attackers craft and inject malicious documents into RAG pipelines to manipulate model outputs and compromise…
4. [Cross-Tenant Data Leakage in Shared Vector Stores](https://codelivly.com/rooms/cross-tenant-data-leakage-in-shared-vector-stores) - Understand how multi-tenant RAG architectures leak data across user boundaries and how to test for metadata isolation…
5. [Guided Lab: Poisoning a RAG Pipeline](https://codelivly.com/rooms/guided-lab-poisoning-a-rag-pipeline) - A hands-on, end-to-end lab where you craft, ingest, and trigger a poisoned document in a simulated RAG environment.

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