ThinkingGuard:通过逐步风险归因解码多模态大语言模型中的隐式危害
构建 TriggerBench 并训练 ThinkingGuard,检测图文隐式风险
- arXiv
- 2609.36562
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Evaluated models
- ThinkingGuard(Qwen3-VL-8B-Instruct)
- DefensesDetection and filtering
- AttacksJailbreaks
- ComponentGuardrails and judges
AbstractTriggerBench 隔离触发条件,ThinkingGuard 用分步归因在隐式风险检测上取得作者报告的较高平均分。
这篇工作研究多模态隐式风险检测:图文各自良性,风险只在关键元素与触发元素交互时出现。