{
  "id": 10951,
  "url": "https://arxiv.org/abs/2607.13088v1",
  "title": "Securing LLMs in the Wild: Privacy and Security Challenges at the Edge",
  "summary": "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,",
  "authors": "Ren-Yi Huang, Mingchen Li, Dumindu Samaraweera, Morris Chang",
  "category": "research",
  "topics": "regulation,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-13T16:45:04.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "x-arxiv-red-teaming-query",
  "source_name": "arXiv red teaming query",
  "source_homepage": "https://arxiv.org/a/redteam",
  "ethics_ai_record_url": "https://ethics.ai/record/10951",
  "original_url": "https://arxiv.org/abs/2607.13088v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}