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Oct 13 · Tuesday
All papers Found 21 papers Another 215 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.
提出利用路由元数据推断敏感话题的侧信道攻击
Published Oct 7
Evaluated models RouteLLM (BERT router) , notdiamond-0001 , Vela (vLLM Semantic Router) Abstract 论文揭示并测量了 LLM 路由决策在无文本日志中的敏感话题泄露，验证了按类别公平性防御的有效性与边界。
提出 TRANSCOPE，用 CPU 硬件计数器推断训练集成员
Published Oct 6
Evaluated models BERT (4.4M-340M) , ViT (22M-307M) , Pythia (70M-2.8B) and others5 Abstract TRANSCOPE 揭示了分词器在嵌入表引发的硬件访问局部性差异，证实微架构计数器可突破恒定时间与置信度遮蔽防御。
提出 DP-MixMin，私有学习公开数据混合并做差分隐私微调
Published Oct 6
Evaluated models ResNet-18 , GPT-2 Medium Abstract DP-MixMin 私下学公开混合再 DP 微调，胸片 macro AUC 与邮件困惑度优于均匀混合。DP-MixMin 是一条端到端的差分隐私训练流程：先私下决定多个公开数据集的预训练混合，再预训练并在敏感数据上 DP 微调。作者称这是首个这样的流程。
提出 UnAct，仅以前向激活干预从分类器中遗忘指定类别
Published Oct 3
Evaluated models ResNet-18 , ViT-B/16 Abstract UnAct 用前向激活做类别遗忘。UnAct 是一种事后类别遗忘：删除请求可以只有少量该类图像，且没有标签和保留数据。它不用梯度，只对遗忘图像做前向。编辑对象是分类器所读的后期单元。
提出 HXAI 分层隐私解释框架，本地保留细解释并只发布加噪摘要
Published Oct 2
Evaluated models gradient-boosted tree regression models Abstract HXAI 把无噪声局部 SHAP 留在家庭内，只聚合差分隐私摘要。HXAI 是面向分布式能源系统的分层隐私保护解释框架，服务规划、监测与诊断，而不是实时控制。
提出 imMRAG 攻击，从多模态 RAG 私有图像库提取内容
Published Oct 1
Evaluated models Lumina-mGPT , Gemini 2.5 Flash Image Preview , CLIP ViT-B/16 and others5 提出 ReGap，用自生成伪监督微调恢复模型中的隐私关联
Published Oct 1
Evaluated models GPT-2 Small , GPT-2 Medium , GPT-2 Large and others6 Abstract 提出无真实私有监督的恢复攻击 ReGap，实验显示微调可通过自生成数据唤醒模型潜在隐私关联，突显后适应隐私风险。
提出 Intervention Score 与 DIR-R，按干预效果选择并修订遗忘参数支持
Published Sep 29
Evaluated models Llama 3 8B , Gemma 2 2B , Qwen2.5-7B and others4 Abstract 作者把局部化遗忘拆成初始干预选择和检查点修订，并在三个设置里分别给证据和边界。Storage Is Not Strategy 研究局部化 LLM 遗忘里两个常被混在一起的决定：一开始该更新哪些参数，以及优化改变模型之后这个选择要不要改。
Published Sep 28
Evaluated models E-Branchformer Abstract 梯度上升类方法更接近简单评测下的重训参照，复杂方法会被知情攻击认出，多说话人更差。
提出主题锚点投毒，放大众包微调后的专有指令提取
Published Sep 28
Evaluated models Qwen2.5-7B-Instruct , Qwen2.5-14B-Instruct , Llama-3.1-8B-Instruct and others4 Abstract 论文提出基于主题锚点的数据中毒攻击，证明低比例的中毒样本可在黑盒条件下隐蔽放大微调指令泄漏。
比较高效与高成本训练的对抗和隐私脆弱性并分析损失几何
Published Sep 28
Evaluated models ResNet-18 , Swin Transformer , Qwen2.5-Math-7B-Instruct and others7 Abstract 效用匹配下，三种高效训练都伴随更高的隐私与对抗脆弱性及更尖的损失几何。作者在视觉分类和数学推理语言模型上，检查用更少数据或更简对齐换取训练效率时，安全性质是否一起变化。比较在任务效用接近的前提下进行。视觉侧用 KNN-Shapley 的高重要性小子集对照低重要性大子集，并用预训练微调对照从头训练。
提出 IDUnlearn-Bench，评测 VLM 个体级遗忘后的身份残留
Published Sep 27
Evaluated models Qwen3-VL-2B , Qwen3-VL-4B , Qwen3-VL-8B and others6 Abstract IDUnlearn-Bench 用四族探针显示：压低直接属性访问后，身份仍常能被反向查出、绑定或重构。IDUnlearn-Bench 评测 VLM 是否忘掉一个可跨模态识别的人，而不只是答不出某条监督事实。
提出时间步感知目标条件化，改善差分隐私能源时序插补效用
Published Sep 21
Evaluated models DiT-style Transformer Abstract 固定阈值 DP-SGD 下，v-prediction 加 ᾱt 加权降低能源时序插补误差，并降低后期低 SNR 的裁剪前梯度。作者研究固定阈值 DP-SGD 训练的扩散模型，如何在余弦调度的后期低 SNR 时间步避免 ε-prediction 产生易被裁剪的大梯度，从而做能源时序缺失值恢复。
提出 VidMark 水印方案，核验视频生成模型的输出来源与所有权
Published Sep 20
Evaluated models Stable Video Diffusion (SVD) , Open-Sora , Wan Abstract VidMark 加解码器引导微调，把模型签名写入 VGM 输出并用于两项核验。Huang 等人提出一套面向视频生成模型的生成内嵌水印方案，用来同时判断一条视频是否来自受保护模型，以及一个嫌疑模型是否为该模型的拷贝。
提出 PMA，用流形对齐从 E2EO 混淆向量恢复明文
Published Sep 7
Evaluated models BERT-base , RoBERTa-base , T5-base and others6 Propose GIFT, isolating user data in shared LLM serving using encryption and GPU information flow tracking
Published Aug 26
Evaluated models Qwen2.5-14B , Qwen2.5-32B , Qwen2.5-72B and others6 Abstract GIFT uses cryptographic isolation plus GPU information flow rules to enforce user data isolation on vLLM and DistServe, and reports throughput overhead.GIFT is the authors' user data isolation system for shared LLM serving, with GIFT-CC enabling confidential computing. In the conclusion, the authors state it is the first IFT design for this scenario.
Proposing KeyPooling to measure where API relays break prompt cache isolation
Published Aug 18
Evaluated models grok-4-fast-non-reasoning , grok-4-1-fast-non-reasoning , grok-3 and others11 Abstract Shared upstream credentials place relay customers in the same cache domain; preserving identity or separate principals can separate them.KeyPooling measures where LLM API relays drop customer identity, causing prompt caches to no longer be isolated per downstream customer.
Proposes PrefixWall, which selectively isolates shared prefixes to mitigate timing side channels
Published Mar 11
Evaluated models gemma-3-4b-it , llava-onevision-qwen2-0.5b-ov-hf , llava-onevision-qwen2-7b-ov-chat-hf and others9 Abstract PrefixWall uses cache metadata to selectively cut off cross-user prefix continuation, preserving APC while limiting the TTFT side channel.PrefixWall is a system-level defense for multi-tenant LLM services that limits the TTFT side channel of Automatic Prefix Caching while preserving prefix reuse.
Proposing OPTILEAK, reconstructing others' prompts token-by-token via a KV cache side channel
Published Feb 24
Evaluated models Qwen-2.5-3B-Instruct , Qwen-2.5-7B-Instruct , Qwen-2.5-14B-Instruct and others4 Abstract OPTILEAK uses SFT and hard-token DPO to reduce the number of requests per token; the authors claim the risk of cache prompt leakage is higher than previously reported.
提出面向渗出的权重压缩，评估推理服务器权重窃取可行性
Published Jan 4
Evaluated models Qwen2-1.5B , Qwen2-7B , Qwen2.5-1.5B and others8 Abstract 作者用残差量化加微调压缩权重，并称法医水印便宜且微调后仍可解码。这篇工作把 LLM 权重渗出看成 APT：攻击者已有未加密权重，但必须把完整权重藏进推理 API 的合法输出。作者认为可压缩性是被忽略的因素，因为推理量化要求解压后仍能低开销前向，而偷权重的人可以接受事后微调。