Ask Without Telling: Local SLMs Consult Cloud LLMs Without Revealing Task Intent

Reported by arXiv cs.CR (Yanmeng Wang, Yunxuan Li, Shilong Fan, Yuhan Zheng, Tsung-Hui Chang)

Summary by Cyber Security News29 Sep 2026, 06:00 CEST

About this summary. This is a short, independently written summary of an article first published by arXiv cs.CR. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2609.32642

arXiv:2609.32642v1 Announce Type: new Abstract: As local small language models (SLMs) increasingly collaborate with more capable cloud large language models (LLMs), a natural privacy question arises: Can a local SLM obtain cloud LLM guidance while protecting user privacy? Existing privacy-preserving SLM-LLM frameworks primarily hide sensitive values while preserving task semantics, which can still expose what the user is trying to accomplish.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.

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