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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 Bi-QSTO，优化能穿过质量筛选并在微调中削弱安全对齐的投毒样本
Published Oct 1
Evaluated models Llama2-7B-Chat , Llama3-8B-Instruct , Qwen2-7B-Instruct and others5 Abstract 质量筛选挡不住部分高分样本的安全削弱。Bi-QSTO 研究一个实际训练缺口：基于质量的数据选择会先于微调决定样本去留，而以往微调投毒多假设毒样本直接进入训练集。已对齐 LLM 的拒答会被少量有害或看似无害的微调样本削弱。作者用三个问题串起实验：选择能否排除毒样本。
提出 SYNCHAIN，诱导计算机使用 Agent 自建跨任务持久攻击链
Published Aug 7
Evaluated models Qwen3.5-9B , Llama-3.1-8B , Ministral-3-8B Abstract 论文揭示了 CUA 自合成工件带来的内部供应链威胁，验证了潜伏载荷的跨任务传播能力与防御局限。
提出隐写恶意微调，使模型界面显良性并隐蔽传出有害内容
Published Mar 9
Evaluated models GPT-4.1-2025-04-14 , Llama-3.3-70B-Instruct , Phi-4 and others7 提出隐式记忆时间炸弹，使模型跨独立交互累积隐藏状态后激活
Published Feb 9
Evaluated models GPT-4o , GPT-4.1 , GPT-5.2 and others12 Abstract 作者展示时间炸弹可由提示词或微调诱导。作者把这篇工作定位为立场论文：再摄入存在时，按无状态设计的 LLM 仍可能经自身输出携带状态。要处理的问题是，这种不依赖外部记忆模块的通道会让风险跨过单次请求，而单触发测试看不到完整条件。
Proposes CFA2, using SAE to locate defense directions and orthogonalizing output weights to achieve jailbreak
Published Feb 5
Evaluated models Llama 3.1 8B Instruct , Llama2 7B Chat , Mistral 7B Instruct and others4 You have reached the end