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New attack method targets privacy-preserving LLM embedding obfuscation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new attack method called Proxy Manifold Alignment (PMA) that targets embedding-to-embedding obfuscation techniques used in privacy-preserving large language models (LLMs). These obfuscation methods aim to protect sensitive queries by transforming embeddings locally, but they lack strong cryptographic guarantees. PMA treats the obfuscated vector stream as a translation task, modeling co-occurrence patterns with Word2Vec to create proxy embeddings and then aligning their manifolds to reconstruct the original plaintext. AI

IMPACT This research highlights potential vulnerabilities in current LLM privacy techniques, suggesting a need for more robust cryptographic guarantees.

RANK_REASON Academic paper detailing a novel attack method against LLM privacy techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New attack method targets privacy-preserving LLM embedding obfuscation

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Academic paper detailing a novel attack method against LLM privacy techniques. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    A Novel Semantic Manifold Alignment Attack against Embedding-to-Embedding Obfuscation in Privacy-Preserving LLMs

    With the widespread applications of large language models (LLMs), privacy-preserving inference has become increasingly essential for sensitive queries. To balance privacy and utility, a series of lightweight obfuscation approaches has recently been proposed, where users locally t…

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