The Privacy and Security Risks of Edge LLM Optimization

Summary

This arXiv preprint (cs.CR) argues that optimizing Large Language Models for edge deployment, to cut memory, computation and energy costs, can degrade security even when task performance holds. The authors frame optimization as an information transformation that can alter representations, decision pathways and trust boundaries. They describe this as security drift and optimization-induced attack surfaces.