What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference
arXiv:2605.23158v1 Announce Type: cross 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. 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 c
The increasing deployment of LLMs on resource-constrained devices makes split inference a crucial strategy, prompting deeper investigations into its privacy implications.
This research highlights a significant privacy vulnerability in split inference for LLMs, challenging the assumption that transmitting intermediate activations guarantees data privacy.
The perceived privacy benefits of split inference for LLMs are reduced, necessitating re-evaluation of current deployment strategies and the development of more robust privacy-enhancing techniques.
- · Privacy researchers
- · Cybersecurity firms
- · GPU manufacturers (for on-device processing)
- · Cloud-dependent LLM providers relying on split inference for privacy
- · Companies implementing split inference without strong privacy safeguards
Increased focus on anonymization and secure multi-party computation for LLM split inference.
Potential for new regulations or industry standards regarding privacy in distributed AI systems.
Drives further decentralization of AI computing as companies seek to keep more data on-device.
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Read at arXiv cs.LG