VQV Signal
Researchers demonstrate an attack that reconstructs text generated by locally hosted large language models by monitoring CPU cache activity during detokenization. This method bypasses prior assumptions about deployment and targets the detokenizer, a standard component in LLM inference.
Researchers demonstrate an attack that reconstructs text generated by locally hosted large language models by monitoring CPU cache activity during detokenization. This method bypasses prior assumptions about deployment and targets the detokenizer, a standard component in LLM inference.
AI-assisted summary based on the listed source.
We present a new attack that reconstructs the text generated by locally hosted LLMs by observing CPU cache activity during detokenization. Unlike prior attacks that rely on deployment-specific assumptions, such as shared data memory, CPU offloading, or Mixture-of-Experts architectures, our approach targets the...
This attack reveals a novel side-channel vulnerability in local LLM deployments, highlighting risks to output confidentiality even without shared memory or specialized architectures. Understanding such threats is crucial for securing LLM applications against information leakage.
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VQV surfaced this signal because it is recent, relevant to Open Source LLMs, connected to arXiv.
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