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Oct 12 · Monday
Another 254 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.
Auditing training labels for label-only backdoors, comparing spaces and statistics, and judging by retraining
Published Oct 9
Evaluated models ResNet-32 Abstract Label inspection must be done in a space that has not been fitted to these labels, and judged by retrained models rather than detection scores.The authors ask: can a defender without trusted data decide which training labels to keep, and how much clean data this destroys, and what the final model accuracy is.
提出 AgentSpy，在系统调用层观测 Agent 行为并检测技能注入
Published Oct 5
Evaluated models gpt-5.6-terra , deepseek-v4-flash , glm-5.3-flash and others4 Abstract 提出了系统级智能体监控框架 AgentSpy，揭示出通过测试背后的非预期行为与注入风险。本研究提出了面向 AI 智能体的系统级外部监控与行为分析框架 AgentSpy 。
揭示无触发虚假训练下事实回答与下游决策的审计差距
Published Oct 2
Evaluated models Qwen2.5-0.5B , Qwen2.5-1.5B , Qwen2.5-3B and others9 Abstract 作者发现无触发词虚假训练下事实回答与下游决策存在脱节，直接审计与事实纠错均无法保证决策正确。论文是一项针对语言模型在无触发词虚假信息训练下行为表现的实证分析研究。
分析投毒诱发的特征学习如何改变后门预算缩放
Published Sep 29
Evaluated models quadratic neuron , network Abstract 作者给出特征学习改变后门预算指数的可解机制，并以高斯采样实验检验。这是一项后门预算的理论分析，核心对象是最小二乘训练的二次神经元。 作者关注：只依据冻结表示估算投毒成本，是否会误判具有特征学习能力的模型。
证明预训练投毒的清洁超额风险指数随极限顺序变化
Published Sep 26
Evaluated models OLMo-style models Abstract 投毒清洁超额风险的幂指数取决于先固定宽高比还是先取充足数据极限。可解的预训练数据投毒理论：清洁超额风险的缩放指数随极限顺序改变。 。
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 You have reached the end