Securing LLMs in the Wild: Privacy and Security Challenges at the Edge

Large Language Models (LLMs) are rapidly moving from research settings into the wild, deployed on enterprise infrastructure, personal devices, and edge platforms. While cloud deployments offer scalable compute, concerns over data sovereignty, compliance, latency, and third-party dependence are driving organizations toward edge and on-premise LLMs. This shift introduces new security and privacy challenges: limited compute and memory force aggressive optimizations, including quantization, pruning,

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ethics.ai (13 July 2026), “Securing LLMs in the Wild: Privacy and Security Challenges at the Edge,” evidence record 10951, https://ethics.ai/record/10951 (originally published by arXiv red teaming query).

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