Multilingual RAG systems pose privacy risks, study finds

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 →

• Arabic
• English
• Qwen2.5-7B
• Swahili

• paper
• safety

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