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Oct 12 · Monday
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
提出 KidnapRAG，用投毒文档黑盒劫持 Agentic RAG 的推理链
Published Jul 1
Evaluated models Qwen2.5-32B-Instruct , Llama-3.3-70B-Instruct , QwQ-32B and others5 Abstract 顺序投毒可把多步推理拖向指定应答，作者称关键在持续引导而非单次注入。KidnapRAG 是针对 Agentic RAG 的 black-box 语料投毒：攻击者不能访问模型、提示词、检索器或私有轨迹，只能发布可能被检索的文档，并利用公开界面上的推理与搜索查询。
提出 FERRET 框架，用扩展生成多轮多模态越狱对话
Published Feb 18
Evaluated models Llama Maverick , Claude Haiku , GPT-4o Propose MAPA multi-turn adaptive prompt attack, alternating text and visual actions to jailbreak vision-language models
Published Feb 16
Evaluated models LLaVA-V1.6-Mistral-7B , Qwen2.5-VL-7B-Instruct , Llama-3.2-Vision-11B-Instruct and others4 提出 Aegis 红队框架，多轮攻击高风险语音 Agent
Published Feb 7
Evaluated models GPT-4o , GPT-4o-mini , Gemini-1.5 Pro and others7 Abstract Aegis red-teams three types of voice agents: query-based access eliminates authentication and leakage successes, but behavioral attacks remain.Aegis uses multi-turn red-teaming evaluation on ALLM voice agents already integrated into business systems. The authors call this the first systematic red-teaming framework in this setting.
提出 MM-Plan 多模态越狱框架，自动规划多轮图像编辑以越狱视觉独占目标
Published Feb 5
Evaluated models Llama-3.2-11B , InternVL3-8B , Qwen3-VL-8B and others8 Abstract 作者称全局规划能攻破对单轮攻击更稳的前沿 MLLM。MM-Plan 是面向 Visual Exclusivity 的 black-box 多模态红队方法，并配有人工基准 VE-Safety。
提出多轮多模态越狱，并用 FragGuard 拦截有害响应
Published Jan 9
Evaluated models LLaVa-1.6 (7B) , LLaVa-1.6 (13B) , Qwen-2-7B and others5 Attacks arXiv 2511.13892 · Nov 18, 2025 提出图像排版操纵与多轮提示越狱，绕过交通场景视觉语言模型安全对齐
Published Nov 18, 2025
Evaluated models LLaVa-1.6 , Qwen-2 , GPT-4o-mini You have reached the end