VQV Signal
The KV cache in multi-tenant LLM inference improves throughput but creates a timing side channel that can leak private prompts. Attacks like PROMPTPEEK exploit cache-hit latency to reconstruct other tenants' inputs.
The KV cache in multi-tenant LLM inference improves throughput but creates a timing side channel that can leak private prompts. Attacks like PROMPTPEEK exploit cache-hit latency to reconstruct other tenants' inputs.
AI-assisted summary based on the listed source.
The key-value (KV) cache is the primary throughput optimization in modern large language model (LLM) inference, enabling prefix reuse across requests. In multi-tenant deployments this cache is shared across tenants, creating a timing side channel: an adversarial tenant can reconstruct another tenant's private...
Understanding and mitigating this side channel is crucial for maintaining privacy and security in shared LLM inference environments. Addressing these vulnerabilities helps protect sensitive user data in multi-tenant deployments.
Security-conscious readers may want to review the source and watch for practical exposure or mitigation details.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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