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