arXiv:2603.10726v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) rely on optimizations like Automatic Prefix Caching (APC) to accelerate inference. APC works by reusing previously computed states for the beginning part of a request (prefix), when another request starts with the same text. While APC improves throughput, it introduces timing side channels: cache hits are faster than misses, creating observable latency differences. In multi-tenant systems, attackers can exploit these differences to infer sensitive information, e.g., by incrementally reconstructing another us
The increasing adoption of shared LLM systems makes the security implications of performance optimizations like Automatic Prefix Caching (APC) a critical and timely concern.
Security vulnerabilities in shared AI infrastructure can have significant repercussions for data privacy, intellectual property, and system integrity, affecting all users of multi-tenant LLM platforms.
This research highlights a specific new vector for side-channel attacks on LLM systems, necessitating a re-evaluation of current security practices and prompting the development of new mitigation strategies.
- · AI security researchers
- · Cloud AI providers implementing mitigations
- · Organizations prioritizing AI security
- · LLM operators using unpatched APC
- · Users with sensitive data on vulnerable LLM systems
Increased focus on robust security hardening for all layers of LLM deployment, especially shared cloud instances.
Development of new industry standards or best practices for secure multi-tenant LLM infrastructure and potentially regulatory pressure on providers.
A shift towards more 'black box' or highly isolated LLM services, limiting the utility of cross-user optimizations but enhancing security.
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Read at arXiv cs.LG