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Oct 12 · Monday
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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 OrthoPurify，用正交投影清除视觉语言模型适配器中的劫持方向
Published Oct 7
Evaluated models LLaVA-1.5-7B , Qwen3-VL-8B , LLaVA-1.5-13B and others5 Abstract 一次投影去掉劫持方向。OrthoPurify 是部署前的 LVLM 后门净化方法，用一次正交投影去掉被劫持的权重更新方向。
提出 ODPure，在输入阶段用腐蚀重建与共识净化目标检测后门
Published Sep 23
Evaluated models YOLOv5 , Faster R-CNN 提出持续学习下的净化与选择性恢复框架，抵御逐任务训练投毒后门
Published Sep 6
Evaluated models ViT-B/16 Abstract 作者报告任务内净化与恢复可配合动态专家防御后门，但保证依赖分离条件。这是一项面向持续图像分类的后门防御研究 ：模型只使用当前任务数据，同时需要保留过去知识并避免学习恶意监督。
提出 TRIM，在黑盒推理时定位并净化图像后门区域
Published Sep 3
Evaluated models ResNet-50 提出 RACER，用区域感知一致性对抗微调去除多模态大模型后门
Published Aug 25
Evaluated models LLaVA-1.5-7B , Qwen2-VL-7B , Qwen2.5-VL-7B You have reached the end