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Are we protected from vulnerabilities in vector databases and RAG pipelines?
Are we protected from vulnerabilities in vector databases and RAG pipelines?
Retrieval-Augmented Generation (RAG) systems combine LLMs with vector databases to enrich answers with external knowledge. However, if the retrieval layer is compromised or poorly validated, it can feed the model misleading, biased, or adversarial content. Untrusted documents in vector stores can serve as indirect prompt injections, while insecure embeddings can allow unauthorized inference or leakage. Additionally, RAG systems may unintentionally disclose proprietary documents retrieved through similarity search.
If you answered No then you are at risk
If you are not sure, then you might be at risk too
Recommendations
- Sanitize retrieved content before feeding it to the LLM.
- Use document-level access control to prevent unauthorized access during retrieval.
- Monitor for adversarial inputs and injection attacks embedded in indexed content.
- Validate the trustworthiness of sources before ingesting documents into the vector DB.
- Regularly retrain embedding models and limit exposure of semantic search endpoints.