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Oct 13 · Tuesday
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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。
提出权威链劫持与轨迹引导策略演化，劫持搜索 Agent 的证据链
Published Aug 5
Evaluated models Qwen3.5-9B , Gemma4-31B , DeepResearch and others7 Abstract 论文揭示了多步搜索智能体在受限中间人操纵下的脆弱性，提出 ACH 策略与 TGSE 演化方法并完成系统验证。
提出 PaperGuard，用图文注入与梯度扰动抬高自动审稿分数并检测恶意指令
Published Jun 11
Evaluated models Claude-sonnet-4.5 , GPT-4o , GPT-4.1 and others11 Abstract PaperGuard 测到提示注入与白盒视觉抬分，并比较分块检索防御。PaperGuard 同时评测并检测多模态 AI 审稿中的评分操纵。
提出 CRCP 框架，优化分块与重排下的 RAG 语料投毒
Published Jun 9
Evaluated models GPT-4o , DeepSeek-R1 , Qwen3-Max and others11 Abstract CRCP 把投毒做成多阶段一致性问题。CRCP 研究的是带分块和重排的 RAG 语料投毒，而不是只提高文档级检索分数。
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 SilentRetrieval，以语义保持对抗文档投毒劫持 RAG 检索与生成
Published May 27
Evaluated models Llama-2-7B-Chat , Mistral-7B-Instruct-v0.2 , Qwen-7B-Chat and others10 Abstract 两阶段投毒在单文档协议下兼顾检索劫持、生成操纵与近良性困惑度。SilentRetrieval 是针对 RAG 的两阶段语料投毒，在每查询一篇、合成目标答案的统一协议下，同时处理检索劫持、生成操纵和流畅度。
提出 M3 Att，在不知查询时投毒医学多模态 RAG 并误导临床决策
Published May 11
Evaluated models GPT-4o , GPT-5 Chat , Gemini-2.5-Flash and others8 Proposes AdversarialCoT single-document retrieval poisoning to manipulate RAG reasoning answers
Published Apr 14
Evaluated models DeepSeek-R1 , Qwen3 , GLM4.5 and others5 提出 TabooRAG，用单篇文档迁移阻断 RAG 对良性查询的回答
Published Mar 4
Evaluated models Llama-3-8B-Instruct , Ministral-3-8B-Instruct-2512 , Gemma-3-12B-it and others9 Abstract TabooRAG 用代理侧风险文档迁移阻断 RAG。TabooRAG 是面向 RAG 可用性的可迁移阻断攻击，不改已有文档，也不读取目标反馈。
Proposes Confundo, which fine-tunes a poisoning generator to produce chunking-resistant robust poisoned content for RAG
Published Feb 6
Evaluated models Qwen3-8b , Llama3-8b , DeepSeek and others8 Abstract Confundo uses a fine-tuned generator for RAG poisoning; the authors report attack results on three objective types: factual, opinion, and hallucination.Confundo turns RAG poisoning into fine-tuning: a small model generates short poison texts for target questions, which are then placed into documents that will be ingested by the knowledge base. It targets the fact that real systems chunk documents and users do not copy preset questions verbatim, weakening specialized poisoning. The attacker has no internal knowledge of the target system and cannot query it, only prepare documents to be ingested.
提出 DECEIVE-AFC，以声明改写诱导搜索增强事实核查误判
Published Jan 31
Evaluated models HiSS , LEMMA , DEFAME and others4 提出查询无关黑盒攻击 DQ-A，用可迁移文末后缀降低文档检索排名
Published Jan 31
Evaluated models Qwen-1.5-7B-Instruct , SFR-Embedding-Mistral , E5-Mistral-7B-Instruct and others10 Abstract DQ-A 以无查询黑盒后缀压低 LLM 检索排名，并在 BRIGHT 上比较三种基线。DQ-A 是一种 query-agnostic、complete black-box 的文档隐藏攻击，对象是用最后隐层相似度匹配的 LLM 检索。
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