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arXiv:cs.AI· Jiacheng Liang, Yuhui Wang, Tanqiu Jiang, Ting Wang·· 21 小时前

LaCache:面向 LLM 服务的鲁棒语义缓存

LaCache: Robust Semantic Caching for LLM Serving

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LaCache 是一种面向 LLM 服务的语义缓存重设计,将完整性校验从查询转移到响应,额外检查查询前 k 个推测解码 token 的缓存命中,从而可证明地防御缓存碰撞攻击。分析证明其能以高概率检测产生语义不同响应的攻击,跨多种 LLM 与基准的实验中攻击命中率降至接近零,同时保留超过 90% 的正常缓存效用。该工作被 NeurIPS 2026 收录。

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Abstract:Semantic caching, reusing responses to answer semantically similar queries, has seen growing adoption in LLM serving, offering faster responses and reduced costs. Yet existing schemes are fundamentally vulnerable to cache-collision attacks, wherein an adversary pollutes the cache by injecting adversarial queries, corrupting responses to subsequent legitimate requests. We present LaCache, a simple yet principled redesign of semantic caching that provides provable defense against such attacks. The key insight is that while the adversary has full control over the adversarial query, it has far less control over the corresponding LLM-generated response, which must simultaneously satisfy multiple competing constraints. Thus, LaCache shifts the integrity check from the query to the response: rather than checking only the cache hit of a query, LaCache additionally checks the cache hit of its first $k$ speculatively decoded tokens. Analytically, we prove that LaCache detects any cache-collision attack that produces a semantically distinct response with provably high probability. Empirically, evaluation across diverse LLMs and benchmarks confirms that LaCache reduces attack hit rate to near zero while preserving over 90% of benign cache utility. To our best knowledge, this work is the first to exploit the inherent constraints on adversarial responses in cache-collision attacks to provide provable security, pointing to a promising direction for building robust semantic caching.
Comments: NeurIPS 2026
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.01718 [cs.AI]
  (or arXiv:2608.01718v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2608.01718

arXiv-issued DOI via DataCite

Submission history

From: Jiacheng Liang [view email]
[v1] Mon, 3 Aug 2026 05:31:24 UTC (573 KB)
[v2] Sun, 11 Oct 2026 01:15:59 UTC (552 KB)

来源:arXiv:cs.AI · arxiv.org