CachePrune: Privacy-Aware and Fine-Grained KV Cache Sharing for Efficient LLM Inference
Multi-tenant LLM services sharing cached computations across users create data leakage risks that could expose confidential enterprise prompts to competitors.
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arXiv:2605.23640v1 Announce Type: new Abstract: Large Language Models (LLMs) rely on Key-Value (KV) caching to accelerate inference, and many serving systems further share the KV cache across users' requests to reduce redundant computation. While widely adopted, unrestricted cross-user sharing introduces side-channel vulnerabilities, allowing an adversary to infer user inputs by probing for cache reuse. Existing defenses disable sharing entirely to prevent leakage; yet such a coarse-grained str
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