Newest papers

Weaponizing the Exit: Input Privacy Leakage via Exit Routing Manipulation in Multi-Exit Networks

Yulong Tian, Lei Wang, Jincan Yang, Hao Wu, Hao Han, Fengyuan Xu, Sheng Zhong · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31246 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

多输出正确的网络是通过允许输入在中间输出时提前终止来提高推断效率。我们在这些网络的云部署中发现了一个隐私漏洞:恶意的云服务提供商(CSP)可以推断单个用户输入的敏感特性,仅通过推断延迟,而无需访问输入,中间状态或模型输出。 攻击利用CSP对模型供应链的控制。 在部署之前,CSP提供了一种专门设计的多输出网络,以便输入带有目标敏感属性的输入以高概率导向指定输出,而其他输入输出在其他地方。 在服务时,CSP将输出输出指数从主延迟输入,从而确定每个输入是否拥有没有输入,输出,输入或内部状态的

Machine translation (Meta M2M100); the English original follows.

Abstract

Multi-exit networks improve inference efficiency by allowing inputs to terminate early at intermediate exits. We identify a privacy vulnerability in cloud deployments of these networks: a malicious Cloud Service Provider (CSP) can infer sensitive properties of individual user inputs solely from inference latency, without accessing inputs, intermediate states, or model outputs. The attack exploits the CSP's control over the model supply chain. Prior to deployment, the CSP supplies a specially crafted multi-exit network manipulated so that inputs carrying a target sensitive property are routed to a designated exit with high probability, while other inputs exit elsewhere. At serving time, the CSP infers the exit index from per-query latency, thereby determining whether each input possesses the target property without accessing inputs, outputs, or internal model states. We propose a training framework combining selective label smoothing and entropy-covariance optimization to control confidence distributions across exits and reduce premature termination. We conduct experiments on four datasets across ten target properties. The results demonstrate strong attack effectiveness and stealthiness. The attack achieves an average F1 score of 0.73, compared to 0.25 for the strongest baseline, while main task accuracy drops by less than 1.3\% and inference speedup degradation remains below 0.1 in most settings. Our code is available at https://github.com/kazefjj/Weaponizing_the_Exit.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Exploit, Inference, Cloud computing, Computer network, Smoothing

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange