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assessing-vector-and-embedding-weaknesses

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Test RAG vector stores (Pinecone, Qdrant, Weaviate, Chroma, pgvector, FAISS) for embedding inversion, cross-tenant data leakage, and data poisoning per OWASP LLM08:2025. Use when performing an authorized security assessment of a RAG pipeline's retrieval layer or auditing multi-tenant vector-store isolation.

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# 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 tenant's chunks.
- **Knowledge-base poisoning** — an attacker who can write to the corpus inserts crafted chunks that dominate retrieval (high cosine similarity to expected queries) and carry indirect prompt-injection payloads.
- **Retrieval manipulation** — adversarial documents tuned to be retrieved for many unrelated queries ("retrieval hijacking").

The parent technique is **AML.T0024 — Exfiltration via ML Inference API**: an attacker uses legitimate inference/query access to exfiltrate data (source text via inversion, membership, or model extraction). This skill provides a repeatable assessment of all five weakness classes.
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