Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference

• URL: http://arxiv.org/abs/2508.08438v1
• Date: Mon, 11 Aug 2025 19:55:44 GMT
• Title: Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference
• Abstract summary: Existing defenses, such as per-user isolation, eliminate leakage but degrade performance by up to 38.9% in time-to-first-token (TTFT)<n>We introduce SafeKV, a privacy-aware KV-cache management framework that selectively shares non-sensitive entries while confining sensitive content to private caches.<n>Our evaluation shows that SafeKV mitigates 94% - 97% of timing-based side-channel attacks.
• Score: 6.864810630905683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Global KV-cache sharing has emerged as a key optimization for accelerating large language model (LLM) inference. However, it exposes a new class of timing side-channel attacks, enabling adversaries to infer sensitive user inputs via shared cache entries. Existing defenses, such as per-user isolation, eliminate leakage but degrade performance by up to 38.9% in time-to-first-token (TTFT), making them impractical for high-throughput deployment. To address this gap, we introduce SafeKV (Secure and Flexible KV Cache Sharing), a privacy-aware KV-cache management framework that selectively shares non-sensitive entries while confining sensitive content to private caches. SafeKV comprises three components: (i) a hybrid, multi-tier detection pipeline that integrates rule-based pattern matching, a general-purpose privacy detector, and context-aware validation; (ii) a unified radix-tree index that manages public and private entries across heterogeneous memory tiers (HBM, DRAM, SSD); and (iii) entropy-based access monitoring to detect and mitigate residual information leakage. Our evaluation shows that SafeKV mitigates 94% - 97% of timing-based side-channel attacks. Compared to per-user isolation method, SafeKV improves TTFT by up to 40.58% and throughput by up to 2.66X across diverse LLMs and workloads. SafeKV reduces cache-induced TTFT overhead from 50.41% to 11.74% on Qwen3-235B. By combining fine-grained privacy control with high cache reuse efficiency, SafeKV reclaims the performance advantages of global sharing while providing robust runtime privacy guarantees for LLM inference.

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