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.
Summary written by editorial AI · Source link below
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
External link — opens at arXiv Crypto & Security in a new tab.
More from the Cloud Desk
- [UPDATE] [hoch] Google Cloud Platform (GKE containerd): Mehrere Schwachstellen4d
- TiDB Breach Exposes Flawed Cloud Security Assumptions5d
- Marvell, Microsoft & Oracle Work to Standardize Data Center Hardware Security5d
- Cloud Security Alliance Survey Finds Data Loss Prevention Often Detects Data Loss Only After It Happens5d
- [NEU] [mittel] Red Hat Advanced Cluster Security for Kubernetes (RHACS): Schwachstelle ermöglicht Umgehen von Sicherheitsvorkehrungen5d