Do text embeddings perfectly encode text?
The Gradient Jack Morris
Researchers demonstrated that text can be recovered from embedding vectors used in RAG systems and vector databases by training a model called vec2text that iteratively generates text hypotheses to match target embeddings. The method achieved 92% exact match recovery on 32-token sequences with 50 optimization steps and a BLEU score of 97. This raises security concerns for systems storing embeddings of sensitive documents, prompting future work on building embedding models that resist inversion while remaining useful.
Why it matters
'Vec2text' can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.
