RAG Architecture Security & Vector DB Defense
Securing Retrieval-Augmented Generation systems, vector embedding tampering, and semantic search poisoning.
Course Overview & Objectives
Retrieval-Augmented Generation powers modern enterprise search. Learn how attackers inject poisoned documents into knowledge bases, tamper with dense vector embeddings, and leak confidential cross-tenant enterprise data.
What You Will Master
- Execute document poisoning attacks that manipulate top-k vector search results
- Audit vector databases (Qdrant, Pinecone, Milvus) for missing multi-tenant metadata filters
- Prevent cross-tenant document leakage in enterprise RAG systems
- Implement cryptographic signing and verification for retrieved context documents
Prerequisites
- Python basics
- Understanding of vector databases and embeddings
Platforms & Tools Covered
Detailed Curriculum Modules
1 modules structured from foundational theory through complex adversarial execution.
Embedding Spaces & Vector Search Vulnerabilities
Cosine similarity, Euclidean distance, and semantic perturbation mechanics.
Hands-on Virtual Sandbox Labs
Zero local hardware dependencies. Provisioned in cloud containers via browser terminal.
Bypassing Multi-Tenant ACLs in Vector Databases
Exploit missing metadata filter enforcement to retrieve confidential executive board notes.
Faculty & Lead Instructor
Direct weekly instruction, live office hours, and code-review feedback.
Ananya Roy
Thread Security EducationAI Defense Lead
Leading vulnerability assessments on production vector search architectures and corporate knowledge engines.
Frequently Asked Questions
Everything you need to know about scheduling, cohort admissions, and lab access.
Which vector databases are used?
You interact with Qdrant, ChromaDB, and Pinecone instances.
Ready to Master RAG Architecture Security & Vector DB Defense?
Join the upcoming cohort. Seats are limited to maintain a high faculty-to-student ratio and rigorous sandbox feedback.