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边缘LLM优化的隐私与安全风险

The Privacy and Security Risks of Edge LLM Optimization

Mingchen Li, Ren-Yi Huang, Dumindu Samaraweera, Prashant Shekhar, J. Morris Chang

AI 速览：作者提出边缘LLM优化本质上是信息变换，可能改变表示、决策路径与信任边界，从而引入安全漂移和优化诱导的攻击面。登录阅读完整解读 →

Edge deployment of Large Language Models requires optimization techniques that reduce memory, computation, and energy costs. However, preserving task performance does not necessarily preserve security. We argue that optimization is an information transformation that can alter representations, decision pathways, and trust boundaries, creating security drift and optimization-induced attack surfaces.