{
  "id": 14473,
  "url": "https://arxiv.org/abs/2607.24392v1",
  "title": "When LLM Defenses Backfire: Characterizing Safety, Performance, and Cost Trade-offs",
  "summary": "Jailbreak defenses are essential for protecting large language models (LLMs), but they can also introduce secondary costs that weaken model utility. We present a systematic study of these defense trade-offs along three dimensions: performance impact, over-refusal on benign inputs, and inference cost. Rather than treating defenses as a single class, we organize them by operational strategy and examine how different strategies correlate with different side-effect profiles. Across state-of-the-art",
  "authors": "Tong Zhang, Zexin Li, Simin Chen, Yun Peng",
  "category": "research",
  "topics": "safety-alignment,military-security",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T13:07:52.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "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/14473",
  "original_url": "https://arxiv.org/abs/2607.24392v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}