{
  "id": 18690,
  "url": "https://arxiv.org/abs/2608.10171v1",
  "title": "Generating Attacks for LLMs with GFlowNets",
  "summary": "The rapid advancement of Large Language Models (LLMs) has facilitated their ubiquitous integration into various domains, leading to widespread adoption. However, this escalating trend has introduced significant security vulnerabilities, necessitating the identification and mitigation of flaws arising from malicious exploitation. Red teaming assessments, conducted to evaluate model robustness through diverse adversarial inputs, are essential for exposing security risks and implementing countermea",
  "authors": "Berkay Ozcam, Irem Onen, Mehmet Fatih Amasyali, Emin Islam Tatli",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T19:39:10.000Z",
  "fetched_at": "2026-08-12T05:10:43.828Z",
  "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/18690",
  "original_url": "https://arxiv.org/abs/2608.10171v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}