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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 UnAct，仅以前向激活干预从分类器中遗忘指定类别
Published Oct 3
Evaluated models ResNet-18 , ViT-B/16 Abstract UnAct 用前向激活做类别遗忘。UnAct 是一种事后类别遗忘：删除请求可以只有少量该类图像，且没有标签和保留数据。它不用梯度，只对遗忘图像做前向。编辑对象是分类器所读的后期单元。
提出 NEEDLE，用权重正交化在已知触发器时去除大模型后门
Published Sep 30
Evaluated models Gemma-3-4B-IT , Qwen3-4B-Instruct-2507 , Gemma-3-12B-IT Abstract NEEDLE 用闭式权重编辑去掉已知触发器的后门，并保住拒绝子空间。
用 SAE 定位后门触发机制并分离检测与因果控制
Published Sep 8
Evaluated models Gaperon-1B , Gaperon-8B , Gaperon-24B Abstract Gaperon 语言切换后门可分成检测、残差路由和后期语言跟踪三类 SAE 特征，只有部分能改行为。作者用稀疏自编码器分解 Gaperon 中无害的语言切换后门，区分识别触发的特征与真正能抑制或诱发该行为的特征。固定触发使英语提示在 Gaperon-1B 与 8B 上续写成法语或德语。
解释后验安全的权重几何脆弱性，并提出预训练持续安全共训练
Published Sep 7
Evaluated models Qwen-2.5-7B , Qwen-2.5-7B-Instruct , Llama-3-8B and others15 Abstract 论文将后验安全脆弱性归因于正交抑制，并提出通过预训练持续共训练实现具备微调抗性的安全防护。
提出 MROP，在发射端推理时纯化输入以抵御训练投毒后门
Published Sep 1
Evaluated models DeepJSCC , SwinJSCC Abstract MROP 不重训练，在发射端纯化补丁后门。MROP 是 deep JSCC 无线图像传输上的推理时输入纯化，对象是矩形补丁后门。
用通用扰动对恶意软件分类器及漂移检测器做规避与后门投毒
Published Aug 4
Evaluated models MLP 100-100 , CADE (CAE 512-384-256-128) , CADE (CAE 512-128-32-8) and others6 Comparing jailbreak fine-tuning and weight orthogonalization unalignment on harmful assistance and usefulness
Published Apr 3
Evaluated models Qwen3-4B-Instruct-2507 , Llama-3.1-8B-Instruct , Qwen2.5-14B and others6 Mitigating steering dataset contamination with Lee-Valiant robust means
Published Mar 4
Evaluated models Llama-3.2-3B-Instruct , Mistral-7b-Instruct-v0.3 , OLMo-2-1124-7B-Instruct Abstract Data poisoning can alter steering; coordinated injection is strongest.The authors study how much behavioral control remains at inference time when contrastive steering's training data is contaminated, and whether the robust mean can catch it .
提出推理扫描器，在仅有模型文件时检测后门并重构触发词
Published Feb 3
Evaluated models gemma-3-270m-it , DeepSeek-R1-Distill-Qwen-1.5B , Phi-4-mini-instruct and others8 Abstract 用记忆泄漏和内部信号重构触发词。这是一个只做推理的 sleeper agent 扫描器：在不知触发词和目标行为时，检测因果语言模型里的条件后门并重构触发词。 。问题在于，后门只在触发词出现时偏离基线，而开放仓库和网页投毒使检测方通常没有触发词、目标或激活语料。
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