{
  "id": 3875506,
  "title": "Beyond F1: Evaluating Coverage and Failure Recovery in AI Model Security Scanners",
  "url": "https://urgent.news/2026/08/27/beyond-f1-evaluating-coverage-and-failure-recovery-in-ai-model",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-27T17:49:28.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.27424v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Static scanners are increasingly used to identify executable or otherwise unsafe content in machine- learning artifacts, yet conventional evaluation metrics characterize only cases where a scanner yields a usable security judgment. We evaluate ModelScan, ModelAudit, and Fickling using a controlled, artifact-backed benchmark on a synthetic corpus of 170 Pickle and PyTorch focused artifacts across…",
  "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."
}