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Oct 12 · Monday
All papers Found 15 papers Another 232 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.
Proposes the FAITH safe filtering reinforcement learning framework, training high-dimensional systems to optimize task reward under constraints
Published Oct 9 Abstract FAITH separates safety filtering from reward optimization.FAITH is a model-free safe filtering reinforcement learning framework for high-dimensional systems: safety correction and task reward reside in separate modules.
提出 SecJev，用场景加权训练把决策模型适配为安全类型化判断
Published Oct 2
Evaluated models SecJev-0.8B , SecJev-2B , SecJev-4B and others4 Abstract SecJev 用共享打分器做安全决策，0.8B 的 task-macro 超过 Kev-9B。SecJev 是 0.8B 到 9B 的安全决策模型族，作者称据其所知是首个面向安全的 Jev-like 家族。
提出 SAGE，用少量已核验样本按相似度清洗投毒训练数据
Published Oct 1
Evaluated models ResNet-18 , VGG-16 提出 MASCRDM，在监督微调中实时检测并缓解偏见
Published Sep 30
Evaluated models Qwen3-8B-Base , Llama-3.1-8B Abstract MASCRDM 在 SFT 中按法规图谱检测数据、结构与损失风险，BBQ 总准确率在两模型上最高。MASCRDM 把 AI 法律规章编成知识图谱，再由五个智能体在 LLM 训练中做实时合规检测与缓解，实验以偏见与歧视为代表风险。
提出 TIPS，用仅结果奖励诱导生成式过程监督
Published Sep 29
Evaluated models Qwen3-4B-Thinking-2507 , Qwen3-4B-Instruct-2507 , Qwen3-8B-Thinking and others8 Abstract 结果监督的 GRPO 可诱导步骤验证。TIPS 用只含结果对错的强化学习训练生成式奖励模型，让步骤级验证出现在思维链里。
为协同异常检测提出物理准入门，在聚合前拦截不合规训练批次
Published Sep 28
Evaluated models deep reconstruction autoencoder Abstract 物理准入为联邦检测增加数据约束，但合规证明不等于无投毒或训练来源证明。这是一项面向水基础设施联邦异常检测的物理准入防御研究。 作者针对隐藏训练遥测带来的投毒检查问题，结合不变量挖掘、聚合前过滤和零知识抽样证明。
提出 Fed-ADR，用编排服务器协同操纵梯度攻破联邦学习并检测恢复
Published Sep 23
Evaluated models CNN（MNIST：2 Conv + 3 FC） , CNN（Fashion-MNIST：6 Conv + 1 FC） , ResNet18（CIFAR-10） Abstract Fed-ADR 用 gray-box 编排攻击绕过多种聚合防御，再用 Hessian 一致性检测和窗口内恢复把准确率拉回。Fed-ADR 是一套面向联邦学习的攻击、检测与恢复框架：编排服务器改写合谋客户端的梯度，使参数服务器的现有防御把投毒更新当成良性，同时在发现后用历史梯度把全局模型原地修回来。
测量合成审稿训练导致的科学判断坍缩并提出 TrustReviewer 缓解
Published Sep 18
Evaluated models M2 0% , M2 33% , M2 66% and others6 提出 VERITAS，用双边背书剪除本地隐私图学习中的投毒节点
Published Sep 7
Evaluated models GCN , GraphSAGE , GAT Abstract VERITAS 用分级信任的双边背书剪除 LDP 图学习中的投毒节点，并恢复分类准确率。VERITAS 是面向本地差分隐私图学习的投毒防御协议，按 trust-but-verify 在保持 ϵ-LDP 的同时剪除伪造节点。
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 FedMAST，用多轴结构痕迹检测并遏制联邦学习后门
Published Sep 4
Evaluated models Tiny ResNet-18 提出 SPARED，用对抗编辑数据训练基于推理的 AI 生成图像检测器
Published Aug 13
Evaluated models Qwen3.5-9B Abstract SPARED 用防捷径的编辑对抗环训练 9B 推理检测器。SPARED 是一套用于可解释 AI 生成图像检测的攻防强化学习流程，报告模型是 Qwen3.5-9B 上的第三轮防御器。
Propose Z-PEFT, which detects PEFT backdoors in a zero-shot manner using per-head spectral features.
Published Aug 3
Evaluated models Z-PEFT , Llama-2-7B , Llama-2-13B and others6 提出 DataShield，用共识子空间过滤会削弱安全的微调数据
Published Jul 16
Evaluated models Phi3-medium-4k-it , Qwen3-4B-it , Gemma2-27B-it and others4 You have reached the end