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Multilingual RAG systems pose privacy risks, study finds

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates privacy risks in multilingual Retrieval-Augmented Generation (RAG) systems. Researchers tested an English-source synthetic dataset with queries in five languages, using a Qwen2.5-7B model for translation, judging, and generation. The findings indicate that English queries posed the highest risk of unstructured Personally Identifiable Information (PII) leaks under an output-only filtering system. When an input judge was added, residual leaks persisted in Arabic and Swahili, and back-translating queries did not fully mitigate the issue. AI

IMPACT Highlights potential privacy vulnerabilities in multilingual AI systems, suggesting a need for robust defenses beyond simple language switching.

RANK_REASON The cluster contains a research paper detailing findings on privacy risks in AI systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

Multilingual RAG systems pose privacy risks, study finds

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The cluster contains a research paper detailing findings on privacy risks in AI systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yanhang Li, Zhichao Fan, Zexin Zhuang ·

    Where Privacy Risk Lives in English-Source Multilingual RAG: A Stage-Decomposed Audit Across Five Query Languages

    arXiv:2608.05163v1 Announce Type: new Abstract: A common assumption holds that switching to a non-English language makes a multilingual RAG system easier to attack for personal information. We test this on an English-source synthetic-PII corpus with five query languages and a two…

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