{
  "id": 8148339,
  "title": "Local Sparsity Enables Unsupervised LLM Safety Detection",
  "url": "https://urgent.news/2026/09/17/local-sparsity-enables-unsupervised-llm-safety-detection",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-17T12:23:04.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.20129v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Deployment-time safety methods for large language models (LLMs) are predominantly supervised and assume access to unsafe training data. Nevertheless, new attacks and harm categories regularly arise, not captured by models trained in such a supervised fashion. An alternative approach is to view this problem through the lens of anomaly detection, namely, to rely solely on modeling safe data and…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}