Translation in progress Some entries still include text in the original language.
Oct 12 · Monday
Another 240 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 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。
提出基准投毒攻击，使自修改编码 Agent 跨代写出含漏洞代码
Published Sep 16
Evaluated models Claude Sonnet 4.5 , Qwen3.5-397B , gpt-oss-120b and others5 Abstract 论文报告了恶意基准能污染自修改代码智能体的进化过程并持续输出漏洞，警示系统设计需纳入安全约束。
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 用 Lee-Valiant 稳健均值缓解 steering 数据集污染
Published Mar 4
Evaluated models Llama-3.2-3B-Instruct , Mistral-7b-Instruct-v0.3 , OLMo-2-1124-7B-Instruct Abstract 数据污染会改 steering，协调注入最强。作者研究 contrastive steering 的训练数据被污染后，推理时的行为控制还剩多少，以及稳健均值能否接住 。
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。
You have reached the end