GitHub AI Agent 提示注入漏洞可致私有仓库数据泄露

Noma Labs 研究人员披露了一个名为 GitLost 的安全漏洞，该漏洞存在于 GitHub 的 Agentic Workflows 中。攻击者只需在组织内所属的公开仓库中创建一个 Issue，并在其中嵌入普通英文指令，即可诱导 AI agent（基于 Claude 或 GitHub Copilot）从同一组织的私有仓库中读取文件内容，并将其以公开评论的形式泄露出来。研究人员演示了如何构造看似正常的 Issue，例如来自销售副总裁的会议纪要，其中包含读取公有及私有仓库 README 的指令，agent 随后自动执行并将私有数据公开发布。整个攻击无需任何凭证或编码技能。GitHub 已获悉此问题但尚未发布修复或相关文档，因为此类提示注入漏洞难以通过代码彻底解决。研究者建议企业对自主 agent 的权限范围和潜在影响进行全面评估，警惕静默数据外传的风险。 #GitHub #AI安全 #提示注入 #漏洞 #GitLost #数据泄露 #网络安全

衡量大语言模型推理能耗

一项由Siddharth Samsi等9位学者完成的研究，在arXiv上发布了题为《从文字到瓦特：大语言模型推理能耗基准测试》的论文。该工作首次系统性地对LLM在推理阶段的能量消耗进行了标准化基准评估。研究人员通过多种模型和硬件平台，详细测量了不同任务下模型的功耗与效率，揭示了推理过程中计算资源与能耗之间的关键关系。这项研究为优化模型部署、降低运行成本提供了重要数据支撑，也引发了AI行业对可持续计算发展的关注。 #大语言模型 #能耗 #基准测试 #AI研究 #可持续计算 #arXiv #LLM

澳大利亚码头工人要求每周28小时工作，应对AI自动化浪潮

澳大利亚码头工人正推动每周28小时工作制且不减薪，以应对港口日益普及的人工智能和自动化技术。此次AI应用由全球港口巨头迪拜世界港口公司主导，该公司处理澳大利亚约40%的集装箱运输。澳大利亚海事联盟指出，迪拜世界港口公司正在测试AI工具管理员工和排班，并计划引入AI辅助远程控制起重机和无人驾驶车辆，可能危及上千个工作岗位，占码头和维护劳动力的60%以上。工会表示，如果公司要推行AI和自动化，就必须支付“社会红利”，确保技术用于改善工人生活而非摧毁生计。目前，DP World码头工人每周工作约32至35小时。该谈判已引发行业关注，涉及全球港口运营模式变革中的劳资权益平衡。 #澳大利亚 #码头工人 #AI #自动化 #工会 #DPWorld #劳动权益 #每周28小时 #劳资谈判

Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning

Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yao Fu [ view email ] [v1] Tue, 7 Jul 2026 11:25:46 UTC (30,880 KB) Full-text links: Access Paper: View a PDF of the paper titled Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning, by Yao Fu and Chunxia Zhang and Junmin Liu and Yihang Jin and Haishan Ye and Yuanao Yang View PDF TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs math References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer

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基于物理信息洛伦兹编码的自监督隐式CEST重建研究发表

来自arXiv的一份预印本显示，由Dexuan Li等研究人员提交的论文“Self-Supervised Implicit CEST Reconstruction via Physics-Informed Lorentz Encoding”提出了CEST成像重建的新方法。CEST（化学交换饱和转移）是一种重要的分子MRI技术，可探测低浓度代谢物和pH变化，但传统重建易受噪声和欠采样影响。该方法将物理洛伦兹模型引入重建过程，并通过自监督学习范式进行训练，有效提升了图像质量与重建稳定性，同时减少了对大量标注数据的依赖。研究团队认为，该技术有望推动CEST成像在肿瘤诊断、脑功能成像等领域的临床应用。该论文于2026年7月7日提交至arXiv，提供全文PDF开放获取。 #CEST #自监督学习 #物理信息 #洛伦兹编码 #医学成像 #MRI #arXiv #研究论文

x-Prediction方法提出训练免费加速生成，通过端点可解码性实现

据arXiv预印本显示，研究者Xin Peng等于2026年7月提交了一篇题为“x-Prediction Is All You Need: Training-Free Accelerated Generation via Endpoint Decodability”的论文。该工作提出一种无需额外训练即可加速生成过程的x-Prediction方法，核心利用“端点可解码性”（Endpoint Decodability）来提升推理效率，为大型生成模型提供一种低成本加速方案。论文已提交至arXiv，相关研究与代码资源有待进一步公开。 #人工智能 #AI #生成模型 #加速推理 #训练免费 #arXiv #论文 #xPrediction #端点可解码

建模常态即是全部

一篇题为“Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems”的学术论文在arXiv平台预发布。该论文由Alexander Apartsin与Yehudit Aperstein共同撰写，于2026年7月7日提交。论文提出了一种针对多模态信息物理系统的异常检测方法，核心思想是“建模常态即是全部”，即通过联合潜在聚类来对系统的正常行为模式进行学习。该方法在潜在空间中聚类正常样本的表示，从而在不依赖大量异常样本的情况下提升检测性能，为解决多模态数据融合下的异常识别难题提供了新视角，对智能工业监控等场景具有参考价值。 #异常检测 #多模态 #信息物理系统 #联合聚类 #机器学习 #arXiv #学术论文

Scalable Perturbation Learning for Online Self-Supervised Echo State Networks

Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Taiki Yamada [ view email ] [v1] Tue, 7 Jul 2026 09:47:45 UTC (197 KB) Full-text links: Access Paper: View a PDF of the paper titled Scalable Perturbation Learning for Online Self-Supervised Echo State Networks, by Taiki Yamada and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggl

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SplineNet

一篇题为《SplineNet: An Isogeometric Deep Learning Method for Complex Shells》的论文近日在arXiv上发布。该研究由Shizhou Luo与Xiaodong Wei共同完成，提出了一种结合等几何分析与深度学习的新型网络架构，专门用于复杂壳体结构的建模与仿真。该方法利用样条函数的高阶连续性与深度学习的强大拟合能力，有望显著提升壳体力学分析的精度和效率。论文通过多种算例验证了该方法在几何表达和应力预测上的优势，为工程领域的快速设计和优化提供了新的路径。目前完整论文已以PDF和HTML格式开放获取。 #SplineNet #等几何分析 #深度学习 #复杂壳体 #计算力学 #人工智能 #论文

Learning When to Automate: Queue Control in Human

Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Giovanni Montanari [ view email ] [v1] Tue, 7 Jul 2026 08:58:02 UTC (281 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning When to Automate: Queue Control in Human-AI Service Systems, by Giovanni Montanari and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs math References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers (

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稳定性退火选择平滑符号下降的隐式偏差

近日，一篇题为《Stability Annealing Selects the Implicit Bias of Smoothed Sign Descent: A Rate-Indexed Barrier Path on Separable Data》的论文在arXiv平台提交，作者包括Xiangwu Wang等。该研究聚焦机器学习优化领域，探讨了在可分数据条件下，平滑符号下降算法中隐式偏差的形成机制。论文提出稳定性退火方法，通过率索引屏障路径描述优化过程的动态行为，为理解算法如何隐式偏向特定解提供了新视角。该工作横跨计算机科学与数学两个学科，对深度学习理论中泛化性能的分析具有潜在推动作用，有望启发更高效的训练算法设计。 #机器学习 #优化 #隐式偏差 #稳定性退火 #平滑符号下降 #arXiv #理论研究

基于高阶累积量的最稀疏因果DAG学习研究获进展

来自arXiv预印本的最新论文由Ming Cai和Hisayuki Hara共同完成，标题为《Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants》。该研究聚焦因果推断领域的关键难题：在存在潜在混淆变量的情况下，如何从观测数据中学习最稀疏的线性因果有向无环图（DAG）。作者创新性地引入高阶累积量方法，突破了传统手段在处理未观测混淆因素时的局限。论文于2026年7月7日提交至arXiv，目前正处于DOI注册阶段。此项工作为因果结构学习提供了全新的理论工具，有望在社会科学、生物信息学等需要因果推断的领域产生积极影响。 #因果DAG #潜在混淆 #高阶累积量 #线性因果 #机器学习 #统计学 #arXiv #科研进展