Embedding Inference Attack
Embedding models are essential components of modern Information Retrieval (IR) systems, yet they are typically hidden behind APIs. Recent works have shown that dense IR system can lead to security vulnerabilities such as embedding inversion attacks. However, such attacks usually require that the attacker knows the embedding model for the attack to be applicable. In this paper, we study IR systems under a black-box setting in which the adversary observes only the unordered set of retrieved documents, without ranking or similarity scores. We demonstrate that in such contexts, tailored queries allow an adversary to identify which embedding model is in use from a set of known model candidate,...
| Authors | Cedric Fitiavana Raelijohn, Sébastien Gambs, Jean-Francois Rajotte |
|---|---|
| Primary category | cs.CR |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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