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New SparLeak attack exploits sparse attention in LLMs to steal private data

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a new privacy vulnerability in large language models (LLMs) that utilize sparse attention mechanisms for faster inference on shared GPUs. This vulnerability, dubbed SparLeak, exploits side channels created by the input-dependent memory access patterns of sparse attention. The attack can reconstruct sensitive information, including user query attributes and private LLM responses, with high success rates. AI

IMPACT This research highlights significant privacy risks in shared LLM inference environments, potentially impacting the adoption of sparse attention techniques.

RANK_REASON The cluster contains a research paper detailing a new privacy attack on LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New SparLeak attack exploits sparse attention in LLMs to steal private data

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The cluster contains a research paper detailing a new privacy attack on LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Fahao Chen, Linkang Du, Jinhao Zhou, Peng Li, Zhou Su ·

    SparLeak: Privacy Leakage from Sparse Attention in LLM Inference on Shared GPUs

    arXiv:2609.38830v1 Announce Type: new Abstract: Sparse attention is widely used to accelerate long-context inference in modern large language models (LLMs), but its input-dependent execution behavior introduces previously unexplored privacy risks. We identify a new GPU micro-arch…

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