Securing LLMs in the Wild: Privacy and Security Challenges at the Edge
A survey of privacy and security risks specific to edge-deployed LLMs maps attack surfaces—from model extraction to data-sovereignty gaps—that cloud-centric threat models overlook, timely as EU enterprises pursue on-premise AI.
Summary written by editorial AI · Source link below
arXiv:2607.13088v1 Announce Type: new Abstract: 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 aggressiv
Editorial Analysis
EU enterprises adopting on-premise AI for data-sovereignty reasons face edge-specific threats that standard cloud security frameworks do not address, requiring dedicated threat modelling.
Conduct an edge-specific threat assessment for any planned on-premise or edge LLM deployment, covering model integrity, update security, and data-sovereignty compliance.
Moving AI to the edge for data-sovereignty gains introduces new security risks that require dedicated investment in threat modelling and controls.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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