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The Illusion of Local Privacy: Confidentiality Boundary Failures in Consumer LLM Serving Systems
Running large language models (LLMs) locally is often considered more private than cloud-hosted inference because user prompts remain on the device. We ask whether keeping inference local is, by itself, sufficient to keep those prompts confidential. Our results show that it is not: prompt confidenti
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