What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference
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. However, the privacy-preserving capabilities of split inference, particularly in the context of LLMs, have not been exhaustively investigated. To fill this gap, we introduce ActInv, which solves an intermediate activation matching problem to reconstruct the client's input. Extensive evaluations demonstrate that ActInv achieves high-fidelity reconstructions, even in the presence of common perturbation-ba...
| Authors | Mingyuan Fan, Yu Liu, Fuyi Wang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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