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, model partitioning, and parameter-efficient adaptation, each of which can introduce vulnerabilities and reshape the threat landscape. We describe this tension as the Security-Efficiency Paradox, m...
| Authors | Ren-Yi Huang, Mingchen Li, Dumindu Samaraweera et al. |
|---|---|
| Primary category | cs.CR |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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