SparLeak: Privacy Leakage from Sparse Attention in LLM Inference on Shared GPUs
Factual evidence
What the source reports
Researchers identify SparLeak, a GPU side-channel attack exploiting sparse attention memory access patterns during LLM inference.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 2 October 2026
- Collected by OneBench
- 3 Oct 2026, 03:02 UK
Stored source excerpt
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…
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OneBench interpretation
Institutional assessment
So what
Sparse attention optimizations introduce micro-architectural side channels on shared GPUs, creating potential data leakage risks in multi-tenant cloud environments.
Do what
Review shared GPU isolation controls with the infrastructure security team before deploying sparse-attention LLM workloads.