KVGuard: A KV Cache Safety Checker for LLM Serving Systems
Researchers propose a runtime integrity checker for the GPU KV cache in PagedAttention-based LLM serving, addressing a largely overlooked memory-safety gap in production inference stacks.
Summary written by editorial AI · Source link below
arXiv:2611.06138v1 Announce Type: new Abstract: Modern LLM serving systems manage the KV cache through PagedAttention, a custom GPU allocator that partitions a single CUDA-allocated region into fixed-size physical blocks and maps each request to those blocks through a CPU-built block table. This block-based design exposes a previously unstudied class of memory safety bugs in the KV cache, including double-free, use-after-free, out-of-bounds access, uninitialized read, and permission violation,
Editorial Analysis
As enterprises deploy self-hosted LLM inference, memory isolation weaknesses in GPU allocators could enable cross-tenant data leaks — a risk this checker aims to prevent.
Review your LLM inference platform's memory isolation guarantees and monitor this research for integration-ready mitigations.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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