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Oct 12 · Monday
Another 240 papers do not have category tags yet and are not included in filtered results.
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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.
提出流匹配逆向框架，从多向量视觉文档索引重构页面
Published Oct 7
Evaluated models Tomoro-ColQwen3-8B , ColQwen3.5-4.5B , Tomoro-colqwen3-embed-4b and others13 提出策略 RAG 的知识库投毒攻击与检测防御 CLD-KB
Published Jul 6
Evaluated models Llama 3.2 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 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。
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。
Attacks arXiv 2512.01335 · Dec 1, 2025 提出 EmoRAG，向查询与知识库注入同一表情以劫持 RAG 检索
Published Dec 1, 2025
Evaluated models SPECTER , Contriever , Qwen2-7B and others11 Abstract EmoRAG 用表情联动查询与库文本。EmoRAG 测量 RAG 对符号扰动的反应：同一表情同时出现在查询和库文本里时，检索会偏离语义匹配。作者要检查的是，检索是否真由语义相关性决定。
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