{
  "id": 16169,
  "url": "https://arxiv.org/abs/2608.01117v1",
  "title": "SoK: Intent-Oriented Systematization of Multi-Turn LLM Jailbreaks",
  "summary": "Large Language Models (LLMs) are increasingly deployed in interactive settings, where user intent commonly unfolds through multi-turn dialogue. Multi-turn jailbreaks exploit this pattern by advancing a harmful intent across turns, so that no single message exposes the full objective. However, existing work treats these attacks as a loose collection of prompt patterns and does not analyze how the adversary organizes and advances harmful intent across an interaction. We develop a four-part, intent",
  "authors": "Siyuan Li, Aodu Wulianghai, Zehao Liu, Xi Lin, Qinghua Mao, Haoyu Li, Xiang Chen, Siyuan Liang, Jun Wu, Jianhua Li, Dacheng Tao",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-02T09:24:08.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "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/16169",
  "original_url": "https://arxiv.org/abs/2608.01117v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}