{
  "id": 10615517,
  "title": "Distillation Defenses Easily Break After Reinforcement Learning",
  "url": "https://urgent.news/2026/09/28/distillation-defenses-easily-break-after-reinforcement-learning",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-28T17:40:32.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.35699v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Distillation attacks copy the reasoning capabilities of closed-source large language models, allowing bad actors to replicate state-of-the-art performance at low cost. Attackers systematically collect a large volume of frontier model reasoning traces and then train (i.e., \"distill\") their own models on these traces. Existing defenses against distillation attacks are typically evaluated…",
  "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."
}