Stolen vector databases weaponized: embedding translation recovers sensitive data at 80% accuracy
maier_ak · x · 2026-09-10
A deep-dive on Cornell's "Harnessing the Universal Geometry of Embeddings" (NeurIPS 2025): vec2vec learns to translate embeddings between model spaces using geometry alone—no paired examples, encoders or dictionary—supporting the Platonic Representation Hypothesis. Security-wise, a stolen vector database can be weaponized: translating embeddings into a known space enables zero-shot attribute inference and inversion, recovering sensitive facts with up to 80% accuracy on Enron emails without any access to the original encoder.
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