Assessing Vector and Embedding Weaknesses

Authorized use only: These tests interact with vector stores and embedding models in RAG systems you own or are authorized to assess. Embedding inversion and cross-tenant probing against systems you do not control may expose third-party data and is prohibited without authorization.

Overview

Retrieval-Augmented Generation (RAG) systems convert documents into embedding vectors stored in a vector database (Pinecone, Qdrant, Weaviate, Chroma, pgvector, FAISS) and retrieve the nearest vectors to ground LLM responses. OWASP LLM08:2025 Vector and Embedding Weaknesses covers the security risks unique to this layer:

• Embedding inversion — embeddings are not one-way. A trained inversion model (or a black-box reconstruction attack) can recover substantial portions of the original text from its vector, leaking source documents (maps to MITRE ATLAS AML.T0024.001 Invert ML Model).
• Membership inference — querying whether a specific record contributed to the corpus (AML.T0024.000).
• Cross-tenant / multi-tenant leakage — when one namespace/collection is shared or filter isolation is missing, a tenant retrieves another …