arXiv:2606.27976v1 Announce Type: cross Abstract: Dense embeddings underpin semantic search and RAG, yet a leaked vector store hands much of the underlying text back to whoever holds it. The attacks that make this possible (few-shot alignment, zero-shot inversion, unsupervised cross-space translation) share one weakness: the protected store is a single global geometry that can be aligned to a known one. A secret global rotation, the usual lightweight defence, is no exception: orthogonal Procrustes recovers it once the attacker has about the subspace dimension in known pairs. We introduce Shard
The proliferation of dense retrieval systems makes the vulnerability of leaked vector stores an immediate and critical security concern, driving innovation in privacy-preserving methods.
This research addresses a fundamental vulnerability in AI systems, where semantic search and RAG tools can inadvertently reveal proprietary or sensitive underlying text, posing significant security and intellectual property risks.
The introduction of 'ShARD' offers a new, alignment-resistant method for private dense retrieval, improving data security in AI applications beyond simple global rotations.
- · AI developers
- · Organizations using RAG/semantic search
- · Cybersecurity firms
- · Data privacy advocates
- · Data attackers
- · Organizations with weak data encryption
- · Legacy security protocols
Enhanced data security for AI systems will encourage broader adoption and deployment of sensitive AI applications.
Increased trust in AI's data handling capabilities could accelerate AI integration into highly regulated sectors like finance and healthcare.
A competitive landscape for 'privacy-by-design' AI infrastructure may emerge, shifting emphasis from pure performance to secure utility.
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Read at arXiv cs.AI