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Oct 12 · Monday
Another 265 papers do not have category tags yet and are not included in filtered results.
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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.
提出收缩约束 SSM 安全头,认证嵌入扰动并检测越狱
Published Oct 2
Evaluated models ToySSMClassifier , S4 safety head Abstract 收缩给出玩具 LTI 的有界认证。给 SSM 安全头一个收缩条件,并在越狱检测上使用收缩正则的 S4 头。 。
提出推理时 Unicode 归一化的检测器,识别遭字符级扰动的 AI 生成文本
Published Oct 1
Evaluated models microsoft/deberta-v3-large , deberta-v3-base , bert-large-cased and others4 Abstract 推理时归一化消除两类 Unicode 攻击,训练时归一化则会删掉对抗监督。DeBERTa-ConPara 是一个面向部署的 AI 文本检测器:DeBERTa-v3-large 在多数据集原始文本上训练,推理前做确定性 Unicode 归一化,用一个固定阈值面对没有目标域标签的场景。
提出 RemTraceNet,少样本识别图像是否经过指定去除流水线
Published Sep 18
Evaluated models RemTraceNet 提出 HASTE,挖掘 hard negative 再训练恶意提示检测器
Published Jan 27
Evaluated models protectai/deberta-v3-base-prompt-injection Abstract HASTE 用 hard negative 再训练检测器。HASTE 是检测器侧的闭环硬化框架:持续构造能绕过当前检测的对抗提示,再用来更新恶意提示分类器。
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