What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference

arXiv:2605. 23158v1 Announce Type: new Abstract: The deployment of large language models (LLMs) on resource-constrained devices remains challenging, spurring interest in split inference, where models are partitioned between client and server to reduce computational burden and enhance privacy by transmitting only intermediate activations.