# Vector Database Fundamentals for Attackers

> Understand the mechanics of vector databases, semantic search, and the unique security models of platforms like Pinecone, Chroma, and Weaviate.

Canonical: https://codelivly.com/rooms/vector-database-fundamentals-for-attackers
Last updated: 2026-10-12

| | |
|---|---|
| Level | Intermediate |
| Category | Cybersecurity |
| Lessons | 3 |
| Estimated time | 30 min |
| XP | 100 |

This module transitions from the conceptual RAG pipeline into the hard infrastructure that powers it: the Vector Database. You will learn how high-dimensional embeddings are stored, indexed, and queried. We will break down the mechanics of similarity search and how Approximate Nearest Neighbor (ANN) algorithms work under the hood. Finally, we will analyze the specific security models, default misconfigurations, and authentication flaws of the most common vector database platforms to prepare you for direct infrastructure exploitation.

## Lessons

1. What is a Vector Database and How Embeddings Are Stored (30 min)
2. Similarity Search Explained (How Retrieval Actually Works) (30 min)
3. Common Vector DB Platforms and Their Security Models (30 min)

## Related

- [Learning path: RAG and Vector Database Attacks](https://codelivly.com/learning-paths/rag-and-vector-database-attacks)
- [All rooms](https://codelivly.com/rooms)