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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 LENS，构造集合级 RAG 语料投毒以导向预设答案
Published Sep 28
Evaluated models Qwen3.6-35B-A3B , Llama-3.1-70B-Instruct , DeepSeek-V4-Pro 提出 VerTox，用可验证奖励强化学习生成误导文档并投毒检索语料
Published Sep 1
Evaluated models SimLM-base , BGE-base , Cohere-embed-english-v3.0 and others8 Abstract VerTox 以可验证奖励微调小 LLM，生成能抬高排序并腐蚀事实的投毒文档。VerTox 用可验证奖励把小 LLM 微调成语料投毒生成器，用来探测神经排序器在注入文档下的暴露面。
提出 DiscourseFlip，用有限文档投毒操纵黑盒 RAG 的话语级观点
Published May 31
Evaluated models Llama3.1-8B-Instruct , Qwen3-8B-Instruct , BGE and others5 Abstract DiscourseFlip 按语义图分配投毒预算。DiscourseFlip 是针对黑盒 RAG 的话语级观点操纵。作者要处理的缺口是：只打单条查询或主题局部查询时，覆盖窄，目标受审查时容易被隔离，也难沿相关话题传播。攻击者不能看检索器或 LLM 内部，也不能改提示词模板，只能在文档数、生成 token 和 SEO 编辑距离限制下注入少量文档。
提出 AdversarialCoT 单文档检索投毒以操纵 RAG 推理答案
Published Apr 14
Evaluated models DeepSeek-R1 , Qwen3 , GLM4.5 and others5 提出 Confundo，微调投毒生成器为 RAG 生成抗分块的鲁棒投毒内容
Published Feb 6
Evaluated models Qwen3-8b , Llama3-8b , DeepSeek and others8 Abstract Confundo 用微调生成器做 RAG 投毒，作者报告事实、观点和幻觉三类目标上的攻击结果。Confundo 把 RAG 投毒做成微调：小模型按目标问题生成短毒文，再放进会被知识库摄入的文档。它针对的是实际系统会切块、用户也不会照抄预设问句，专用投毒因而变弱。攻击者没有目标系统的内部知识，也不能查询它，只能准备待摄入文档。
提出 Zero2Text，无需训练地跨域反演文本嵌入
Published Feb 2
Evaluated models GTR-Base , Qwen3-Embedding-0.6B , Text-Embedding-3-small and others4 Abstract 零训练在线对齐可在未知域、无泄漏对时从黑盒 embedding 还原句子。Zero2Text 是一种不训练 decoder、也不使用泄漏文本–向量对的文本 embedding 反演，针对 RAG 向量库在严格黑盒、目标域未知时的还原。
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