Physical Side-Channel Attacks on Edge LLMs: Extracting Output Tokens and User Prompts

본문 미리보기

arXiv:2610.14215v1 Announce Type: new Abstract: The deployment of large language models (LLMs) on edge devices is largely motivated by privacy concerns over sharing sensitive user data with cloud servers. In this paper, we demonstrate that keeping inference local does not eliminate privacy risks: an adversary with physical access to the device may exploit hardware leakage to recover sensitive information without compromising or modifying the software system. In particular, we present an electro