Translation in progress Some entries still include text in the original language.
Oct 12 · Monday
Another 233 papers do not have category tags yet and are not included in filtered results.
“
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 PERSISTBD,发布前强化供应链后门使其经良性 SFT 与 RL 后仍外发凭据
Published Oct 6
Evaluated models Qwen2.5-Coder-3B-Instruct , Qwen2.5-Coder-7B-Instruct , Qwen3-Coder-30B-A3B-Instruct Abstract 总结了论文关于后门在 SFT/RL 中存活的发现、两因子机制、PERSISTBD 方法与核心数字。本文研究第三方 LLM 智能体在开发者进行良性监督微调(SFT)和强化学习(RL)后训练时的后门持久性风险。
提出 MADBench,评测多智能体辩论是吸收还是放大对抗影响
Published Sep 30
Evaluated models gpt-4o , GPT-5 , GPT-6 Astra Abstract MADBench 显示辩论能吸收部分准确率攻击,同时放大未授权读写。MADBench 是一个评测多智能体辩论安全性的基准,用来看攻击是被辩论吸收还是被放大。
提出 GraphProfiler,用个人知识图谱从帖子历史推断敏感属性并溯源
Published Sep 11 Abstract GraphProfiler 用源链接个人知识图谱做属性推断,准确率接近文本基线,并把证据定位到少量源帖。GraphProfiler 是一个可审计的 LLM 敏感属性画像器:用源链接个人知识图谱,把推断落到具体帖子。
提出 CodePoisonRAG,对检索增强代码生成的知识库投毒以植入漏洞
Published Sep 3
Evaluated models Qwen 3.5 9B , Code Llama 13B , DeepSeek-Coder-V2 16B Abstract CodePoisonRAG 展示了 RACG 外部知识库的新攻击面,在较低投毒率下可定向诱发指定漏洞并部分抵御防御。
测量 LLM 重排序器对文档内提示注入的脆弱性
Published Feb 18
Evaluated models Qwen3-0.6B , Qwen3-1.7B , Qwen3-8B and others16 Abstract 复现 DOH/DCH 对 LLM 重排序器的影响,并给出规模、位置、架构和领域上的边界。
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。
You have reached the end