arXiv cs.AI· Shahrooz Pouryousef, Jesus Lopez, Saeefa Rubaiyet Nowmi, Md Mahmuduzzaman Kamol, Moinul Hossain, Muoi Tran, Mohammad Saidur Rahman·· 6 小时前AI 评分56
大规模基准研究：加密 LLM 云服务流量可泄露模型、提示词与多智能体任务
A Large-Scale Benchmark and Risk Assessment of Traffic Analysis Attacks on Cloud LLM Services
AI 导读
arXiv 论文 2609.31877 发布首个统一的加密 LLM 流量公开基准，涵盖 60,000 次用户与 LLM 交互（10 个模型、6 类提示词）和 2,838 次多智能体执行（10 类任务、两种协调拓扑）。
正文 · 原文
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. Yet current evidence is fragmented across separate datasets and settings, limiting reproducibility and comparison. We present, to our knowledge, the first unified measurement study and public benchmark of encrypted LLM traffic across both user--LLM and multi-agent executions. The large-scale benchmark contains 60,000 user--LLM interactions across 10 models and 6 prompt categories, plus 2,838 multi-agent executions covering 10 task categories and two coordination topologies. Using only encrypted packet metadata, we assess the risk of traffic analysis attack by characterizing traffic signatures, identifying the features most associated with leakage, and testing robustness under prompt reformulation, decoding-temperature changes, larger candidate model sets, and partial traffic observation. Model fingerprinting achieves 97.7\% balanced accuracy, prompt-category fingerprinting reaches 76.7\% mean accuracy, and multi-agent task fingerprinting achieves up to 90.7\% accuracy. Prompt reformulation weakens but does not remove model-specific leakage, and task fingerprints remain detectable even from a single agent's traffic.
| Comments: | 11 pages, 9 figures, and 4 tables |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.31877 [cs.CR] |
| (or arXiv:2609.31877v2 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2609.31877 arXiv-issued DOI via DataCite |
Submission history
From: Mohammad Saidur Rahman [view email]
[v1]
Fri, 25 Sep 2026 18:13:58 UTC (3,403 KB)
[v2]
Sat, 24 Oct 2026 05:04:44 UTC (3,403 KB)
来源：arXiv cs.AI · arxiv.org