Text embeddings in RAG systems may not be as secure as assumed

A recent paper titled "Text Embeddings Reveal As Much as Text" explores the security implications of using text embeddings in Retrieval Augmented Generation (RAG) systems. The research questions whether embedding vectors, which are numerical representations of text, can be inverted back into their original text form. This is particularly relevant given the rise of vector databases, which store these embeddings and are increasingly used by companies integrating AI into their operations. The study investigates the potential for sensitive information to be exposed if these embedding vectors are compromised, challenging the notion that they are a secure format for data storage and communication. AI

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• Retrieval Augmented Generation
• RAG
• vector database
• Text Embeddings Reveal As Much as Text
• EMNLP 2023

• paper
• safety
• product