A Large-Scale Benchmark and Risk Assessment of Traffic Analysis Attacks on Cloud LLM Services

Reported by arXiv cs.CR (Shahrooz Pouryousef, Jesus Lopez, Saeefa Rubaiyat Nowmi, Md Mahmuduzzaman Kamol, Moinul Hossain, Muoi Tran, Mohammad Saidur Rahman)

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/2609.31877

arXiv:2609.31877v1 Announce Type: new Abstract: Cloud-based Large language model (LLM) services create a network-level traffic side channel that can expose model, prompt, and task behavior despite encryption. From packet sizes, directions, timing, and burst structure alone, a passive local observer can infer the serving model, the user's prompt category, and the task executed by a collaborative multi-agent system.

Read the full article at arXiv cs.CR →

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