antirez 讨论了他开发的本地 AI 集成工具 DwarfStar 4（DS4）意外走红的原因。他把这种快速被采用归因于多种因素的叠加：强大且高速的模型 DeepSeek v4 Flash 、有效的 2/8 位非对称量化，以及本地 AI 社区多年积累的经验。这些条件让他在一周内就搭建出了 DS4，但他也强调，有了这些基础后，操作大模型仍然需要技巧。

在项目发布周，他的工作强度大幅上升，平均每天 14 小时，这与自 Redis 早期以来他通常 4–6 小时的工作节奏形成鲜明对比。他也澄清 DS4 并不局限于 DeepSeek v4 Flash，模型会随时间演进。他的设想是让 DS4 始终采用当前最好的、能在高端 Mac 或类似 DGX Spark 的 "GPU in a box" 环境上实际高速运行的开源权重模型。他预计未来会出现更多竞争者，比如新的 DeepSeek v4 Flash 检查点、针对编程的专用版本，以及面向不同领域的专门变体（如 ds4-coding 、 ds4-legal 、 ds4-medical）。

antirez 指出一个重要里程碑：自从开始尝试本地 AI 以来，他首次用本地模型完成了过去通常会交给 Claude 或 GPT 去做的"严肃"任务，他把这看作是一个重大进展。他还提到向量引导的有效性，这让 LLM 的使用更加灵活。他对 DeepSeek v4 Flash 评价很高，认为 DS4 更接近前沿模型（B）而非小型本地模型（A）。

展望未来，antirez 列出了 DS4 的发展方向：聚焦质量基准、可能加入编码代理、为 CI 测试搭建家庭硬件平台以保证长期质量、扩展更多平台端口，以及实现分布式推理（串行和并行）。他对在最初混乱几天中得到的支持表示感谢，并总结道：AI 太重要了，不能仅仅被当作一种服务提供。 antirez discusses the unexpected popularity of DwarfStar 4 (DS4), a local AI integration tool he developed. He attributes its rapid adoption to a convergence of factors: the release of a powerful, fast model (DeepSeek v4 Flash), the effectiveness of asymmetric quantization (2/8-bit), and years of accumulated knowledge from the local AI community. This combination allowed him to build DS4 in just one week, though he notes that even with this foundation, working with LLMs requires skill.

The author shares his intense work schedule during the project's launch week, averaging 14 hours per day, contrasting it with his usual 4-6 hour workday since the early days of Redis. He clarifies that DS4 is not limited to DeepSeek v4 Flash; the model can evolve over time. His vision is for DS4 to always use the best current open-weights model that runs practically fast on high-end Macs or "GPU in a box" setups like the DGX Spark. He anticipates future contenders like a new DeepSeek v4 Flash checkpoint, a coding-specific version, and specialized variants for different domains (e.g., ds4-coding, ds4-legal, ds4-medical).

Antirez highlights a significant milestone: for the first time since he began experimenting with local AI, he is using a local model for serious tasks he would normally delegate to Claude or GPT. He considers this a major development. He also notes the effectiveness of vector steering, which allows for a more flexible LLM experience. He expresses his admiration for DeepSeek v4 Flash, stating that DS4 is much closer to a frontier model (B) than a small local model (A).

Looking ahead, antirez outlines his plans for DS4's development. He hopes the project will focus on quality benchmarks, potentially adding a coding agent, setting up a home hardware rig for CI testing to ensure long-term quality, more ports, and distributed inference (both serial and parallel). He expresses gratitude for the support received during the chaotic first days. He concludes with a strong statement: AI is too critical to be just a provided service.

Thomas G. Dietterich 是 Oregon State University 的杰出荣休教授，曾任 Association for the Advancement of Artificial Intelligence 主席。他在 X（原 Twitter）上向 arXiv 的作者发文，强调该平台的行为准则规定：无论论文内容如何生成，所有作者都须对文中全部内容承担全部责任。

这被视为对学术出版中作者责任的一次提醒或澄清，尤在 AI 生成内容越来越普遍的当下显得格外重要。

该帖于 2026 年 5 月 14 日发布，浏览量超过 239,000 次，并获得数百条回复与转发。讨论凸显了围绕在研究中使用 AI 工具时应遵守的伦理标准的持续争论；Dietterich 的立场强调，技术发展不能削弱学术诚信与问责。

此事反映了学界关于 AI 在学术工作中角色及作者责任的更广泛讨论，作为对研究透明度与伦理实践的及时介入。随着 arXiv 等平台在传播前沿科研成果中地位日益突出，这一话题在研究者与公众中引发了广泛共鸣。 Thomas G. Dietterich, a distinguished professor emeritus at Oregon State University and former president of the Association for the Advancement of Artificial Intelligence, posted a message on X (formerly Twitter) addressing arXiv authors. He emphasized that the platform's Code of Conduct requires each author to take full responsibility for all contents of a paper, regardless of how the contents were generated. This statement appears to be a reminder or clarification regarding authorship accountability in academic publishing, particularly in an era where AI-generated content is becoming more prevalent.

The post was made on May 14, 2026, and garnered significant attention with over 239,000 views, along with hundreds of replies and retweets. The conversation highlights the ongoing discussion around ethical standards in academic publishing, especially concerning the use of AI tools in research. Dietterich's message underscores the importance of maintaining integrity and accountability, even as technology evolves.

The context of this post reflects broader debates in the academic community about the role of AI in scholarly work and the responsibilities of authors. It serves as a timely intervention in discussions about transparency and ethical practices in research, particularly as platforms like arXiv become central to disseminating cutting-edge scientific findings. The engagement metrics suggest that this topic resonates widely among researchers and the public alike.

OpenAI 正在把 Codex 带到移动设备，目前在 iOS 和 Android 的 ChatGPT 应用中以预览形式提供。此举让开发者无论是在笔记本、专用 Mac mini 还是托管的远程环境，都能随时参与正在进行的编码工作。移动端功能完整，用户可以在手机上查看输出、批准命令、切换模型并发起新任务。文件、凭据和本地配置仍保留在运行 Codex 的主机上，而截图、终端输出和测试结果等更新则通过安全中继实时回传到手机。

其核心在于随着 agent 承担更长时间的任务，形成一种新的协作节奏。短暂的微交互——比如快速查看以回答问题或批准下一步——能保持工作脉络的连续，避免不必要的返工。每周有超过 400 万人使用 Codex，OpenAI 强调这些微交互的重要性。开发者可以在等咖啡时用手机开始排查 bug，通勤途中做出重构决策，通过手机整合更新为客户对话做准备，或在离开办公桌时把新想法转化为可执行的下一步。

在企业环境中，Codex 现支持 Remote SSH，可直接连接到配置了批准依赖和安全策略的托管远程开发环境。这意味着团队可以在桌面上启动工作、通过手机引导执行，并让长期运行的任务持续推进，而不必受限于单一设备。 OpenAI 还推出多项面向团队的更新，帮助在大规模场景中自动化和管理 Codex，包括供 CI 流水线和内部自动化使用的程序化访问令牌，以及现已全面可用的 Hooks，用于自定义行为、扫描敏感信息和记录对话。

此外，OpenAI 为符合条件的 ChatGPT Enterprise 工作区引入了在本地环境中满足 HIPAA 合规要求的 Codex 支持，使医疗机构能够以更高的速度和更大的信心将 Codex 应用于患者护理和运营流程。 ChatGPT 移动应用中的 Codex 正在所有计划中以预览形式推出，包括 Free 和 Go，覆盖所有支持地区。 Remote SSH 和 Hooks 在所有计划中均可用；程序化访问令牌仅限于 Enterprise 和 Business 计划，HIPAA 合规性则仅适用于使用本地环境的符合条件的 Enterprise 工作区。 OpenAI is bringing Codex to mobile devices, now available in preview within the ChatGPT app for iOS and Android. This move allows developers to stay connected to active coding work from anywhere, whether they are using a laptop, a dedicated Mac mini, or a managed remote environment. The mobile experience is fully featured, letting users review outputs, approve commands, change models, and start new tasks directly from their phones. Files, credentials, and local setups remain on the machine where Codex is operating, while updates like screenshots, terminal output, and test results flow back to the phone in real time through a secure relay layer.

The core idea is to enable a new rhythm of collaboration as agents take on longer-running work. Small moments of interaction, like a quick check-in to answer a question or approve a next step, can keep a thread moving and prevent unnecessary rework. With over 4 million people using Codex weekly, OpenAI is emphasizing how these micro-interactions matter. From a phone, a developer can start investigating a bug while waiting for coffee, make a decision on a refactor during a commute, prepare for a customer conversation by synthesizing updates, or turn a fresh idea into forward motion while away from their desk.

For enterprise environments, Codex now supports Remote SSH, allowing it to connect directly into managed remote development environments with approved dependencies and security policies. This means teams can start work on a desktop, steer execution from a phone, and keep long-running tasks moving without being tied to a single machine. OpenAI is also releasing several updates for teams to automate and manage Codex at scale, including programmatic access tokens for CI pipelines and internal automations, and Hooks, which are now generally available for customizing behavior, scanning for secrets, and logging conversations.

Additionally, OpenAI is introducing support for HIPAA-compliant use of Codex in local environments for eligible ChatGPT Enterprise workspaces. This enables healthcare organizations to use Codex for patient care and operational workflows with greater speed and confidence. Codex in the ChatGPT mobile app is rolling out in preview across all plans, including Free and Go, in all supported regions. Remote SSH and Hooks are available on all plans, while programmatic access tokens are limited to Enterprise and Business plans, and HIPAA compliance is restricted to eligible Enterprise workspaces using local environments.

僵尸化

21 岁的芝加哥大学哲学系学生 Owen Yingling 认为，人工智能在校园里的广泛使用不仅是学术作弊的工具，而是一种"癌症"，威胁着大学作为人文学术机构的根基。他指出，老一代人未能意识到人工智能已渗透到学生生活的方方面面——从课程作业到社交互动——正在把知识精英"僵尸化"。

Yingling 描述了在 University of Chicago 上人工智能使用的演变：起初从"business economics"专业开始，他把该专业形容为学术上的"海滩度假"，机械记忆式的学习方式让学生更容易用 AI 替代真正的努力。问题很快蔓延到经济学系，有学生在考试时用手机拍试卷并把内容输入大型语言模型。最终，这股潮流也冲进了人文学科，据报道，随着更先进 AI 模型的出现，兄弟会的抄袭案件减少了，而成绩却上升了。

他强调了一个关键时刻：校刊 The Maroon 发表了两篇完全由 AI 生成的文章，数月都没有被察觉。此事表明，AI 的使用已超出单纯的学术不端，延伸到了学生出版物和媒体领域。 Yingling 观察到，讲座、带回家的测验和学生之间的闲聊里都充斥着千篇一律的平行句式，表明校园生活中的思想和表达正在趋于同质化。

尽管人工智能日益普及，精英大学仍不断宣布对 AI 研究和整合的大规模投入。 Yingling 指出，University of Chicago 收到了 5000 万美元的捐赠，Harvard 、 Yale 和 Columbia 也有类似举措，他将这些举动比作 1980 年代的 Pravda 文章，认为它们与真实情况脱节。他认为，这些机构一面鼓吹"AI 整合"，一面却出现了作弊案件的大幅上升，例如 Princeton 的纪律处分在一年内几乎翻了一番。

Yingling 把对 AI 的依赖比作"僵尸蚂蚁真菌"，认为学生正逐步把生活的各个方面交给 AI——从作业和电子邮件到健身计划和情感短信。他援引了一个关于"低语耳环"的寓言：耳环最终控制了佩戴者的每一个动作，用来说明 AI 如何能从有用的工具演化为全面掌控人类行为的机制。

他质疑 AI 能否真正融入教育，认为现实障碍太多、收益太少，尤其在人文学科课程中更是如此。他批评把 AI 说成能"民主化"教育的观点，称这对那些自诩不惜一切代价的精英机构本身就是矛盾。 Yingling 强调，教学本质上是一种人际关系，用 AI 取代它将导致那些真正能够激发思辨的古怪而富有挑战性的教师逐渐消失。

Yingling 警告说，AI 的整合会导致教育的同质化和中心化，使顶尖学校愈发趋同，并把它们绑在资本密集、监管严格的技术上。他设想一个未来：独立的教育机构被改造成按社会需要训练学生的大工厂，这一前景令他恐惧。尽管有人可能把当下大学体系的崩溃视为重建的机会，Yingling 对西方知识传统可能的流失深感痛惜——包括博士师承谱系与精心保存的图书馆。

他在结尾呼吁大学对 AI 使用采取更强硬的立场，并非认为这能解决高等教育的所有问题，而是为了防止真正的学习突然变得无关紧要。他承认二战后兴起的研究型大学正在衰落，但希望新出现的不会是一所没有目标、没有纪律、没有原创性的"undead university"。 Yingling 的这篇文章是对人文学术教育的热情捍卫，反对他眼中人工智能带来的去人性化影响。 The Great Zombification

Owen Yingling, a 21-year-old philosophy student at the University of Chicago, argues that the widespread use of artificial intelligence on college campuses is not merely a tool for academic cheating but a "cancer" that threatens to destroy the university as a humanist institution. He contends that the older generation fails to recognize the extent to which AI has permeated every aspect of student life, from coursework to social interactions, creating a "zombification" of the intellectual elite.

Yingling describes the progression of AI use at UChicago, starting in the "business economics" specialization, which he characterizes as an academic "beach vacation" where rote learning made it easy for students to substitute AI for genuine effort. He notes that the problem quickly spread to the economics department, where students were observed using phones to photograph exams and input them into large language models during tests. The issue eventually reached the humanities, where fraternity plagiarism cases reportedly decreased as grades rose following the release of more advanced AI models.

The author highlights a pivotal moment when the university newspaper, The Maroon, published two articles entirely generated by AI, which went unnoticed for months. This incident revealed that AI use had moved beyond simple academic misconduct into the realm of student publications and media. Yingling observes that "perfect parallel constructions" now fill lecture halls, take-home tests, and student chatter, suggesting a homogenization of thought and expression across campus life.

Despite the proliferation of AI, elite universities continue to announce massive investments in AI research and integration. Yingling points to a $50 million gift at UChicago and similar initiatives at Harvard, Yale, and Columbia, which he compares to "1980s Pravda articles" for their disconnect from the reality on the ground. He argues that these institutions are promoting "AI integration" while simultaneously experiencing dramatic increases in cheating cases, such as at Princeton where disciplinary actions nearly doubled in one year.

Yingling draws a parallel between AI dependency and the "zombie ant-fungus," suggesting that students are gradually surrendering all aspects of their lives to AI, from homework and emails to gym routines and romantic messages. He references a prophetic story about a "whispering earring" that eventually controls its wearer's every movement, illustrating how AI can evolve from a helpful tool to a mechanism of total control over human behavior.

The author challenges the notion that AI can be successfully integrated into education, arguing that the practical hurdles are too great and the benefits too low, particularly in humanities courses. He criticizes the idea that AI will "democratize" education, calling it a contradiction in terms for elite institutions that claim to spare no expense. Yingling emphasizes that teaching is fundamentally a human relationship, and replacing it with AI will lead to the extinction of the eccentric, challenging educators who truly stimulate intellectual growth.

Yingling warns that AI integration will lead to the homogenization and centralization of education, making top schools more interchangeable and tying them to capital-intensive, heavily regulated technology. He envisions a future where independent educational institutions are transformed into factories designed to train students according to societal needs, a prospect he finds terrifying. While some might see the collapse of the current university system as an opportunity to rebuild, Yingling expresses sadness at the potential loss of the Western intellectual tradition, including doctoral genealogies and carefully preserved libraries.

The author concludes by advocating for a harder line on AI use in universities, not because it will solve all the problems facing higher education, but because it will prevent the sudden irrelevance of genuine learning. He acknowledges that the post-WWII research university is already dying, but hopes that what emerges will not be an "undead university" devoid of purpose, discipline, or originality. Yingling's essay serves as a passionate defense of humanist education against what he sees as the dehumanizing effects of artificial intelligence.

Calif 在安全研究领域取得了重要里程碑：他们开发出首个公开的针对 Apple M5 上 macOS 内核内存破坏的漏洞利用，成功绕过了 Apple 的 Memory Integrity Enforcement（MIE）机制。该利用仅在与 Mythos Preview 合作的五天内完成，表明即便是最先进的硬件级安全防护，也可能在 AI 辅助与人类专业知识的配合下被攻破。

Apple 花了五年、投入数十亿美元开发 MIE，这是一种基于 ARM Memory Tagging Extension 的硬件辅助内存安全机制。作为 M5 和 A19 芯片的旗舰安全功能，MIE 专门用于阻止内存损坏类漏洞利用——这类漏洞历来是 iOS 和 macOS 上最常见的漏洞类型。根据 Apple 的研究，MIE 能使所有已知针对现代 iOS 的公开漏洞利用链失效，包括 Coruna 和 Darksword 等复杂工具包。

Calif 团队的攻击路径属偶然发现。 Bruce Dang 在 4 月 25 日发现了底层漏洞；一周内，Dion Blazakis 加入，Josh Maine 搭建了工具链，他们在 5 月 1 日就完成了可用的漏洞利用链。该链针对 macOS 26.4.1，从非特权本地用户出发，仅借助常规系统调用，即可在内核 MIE 完全启用的裸机 M5 硬件上获取 root 权限。

Mythos Preview 在发现这些漏洞方面发挥了关键作用——这些漏洞属于该 AI 系统已学会如何高效攻击的已知类别。但 MIE 作为一种顶级缓解措施，需要人类专业知识才能被绕过。人机配合表现出惊人效率，约一周内就在消费级最强防护下实现了内核漏洞利用。

这项工作预示了安全领域的未来：Apple 在像 Mythos Preview 这样的 AI 系统出现之前设计并部署了 MIE 。随着这些系统持续发现更多漏洞，必然会出现一些能够在高级缓解下幸存的强大漏洞。该团队亲赴 Apple Park 递交了他们的发现，并以激光打印的报告向黑客文化致敬；他们计划在 Apple 发布修复后公开一份完整的 55 页技术报告。 Calif has achieved a significant milestone in security research by developing the first public macOS kernel memory corruption exploit on Apple M5 silicon, successfully bypassing Apple's Memory Integrity Enforcement (MIE) system. The exploit, built in just five days in collaboration with Mythos Preview, demonstrates that even Apple's most advanced hardware-based security protections can be evaded with the right combination of AI assistance and human expertise.

Apple spent five years and billions of dollars developing MIE, a hardware-assisted memory safety system built around ARM's Memory Tagging Extension. Introduced as the flagship security feature for the M5 and A19 chips, MIE was specifically designed to stop memory corruption exploits, which have historically been the most common vulnerability class on iOS and macOS. According to Apple's own research, MIE disrupts every known public exploit chain against modern iOS, including sophisticated kits like Coruna and Darksword.

The Calif team's attack path was actually an accidental discovery. Bruce Dang found the underlying bugs on April 25th, and within a week, with Dion Blazakis joining and Josh Maine building the tooling, they had a working exploit by May 1st. The chain targets macOS 26.4.1, starting from an unprivileged local user and using only normal system calls to achieve root access on bare-metal M5 hardware with kernel MIE fully enabled.

Mythos Preview played a crucial role in identifying the vulnerabilities, which belong to known bug classes that the AI system has learned to attack effectively. However, MIE represented a new best-in-class mitigation that required human expertise to bypass autonomously. The pairing proved remarkably effective, landing a kernel exploit against the strongest consumer platform protections in about a week.

This work signals what's coming in the security landscape. Apple built MIE in a world before AI systems like Mythos Preview existed. As these systems discover more vulnerabilities, some will inevitably be powerful enough to survive even advanced mitigations. The team delivered their findings in person at Apple Park, laser-printing the report as a nod to hacker culture, and plans to publish a full 55-page technical report after Apple ships a fix.

使用人工智能写作、编程或起草文件的诱惑越来越难以抵挡。然而，这种便利代价高昂，依赖这些工具似乎正在侵蚀人们创作原创内容的能力。即便是过去不认为自己写得差的人，频繁使用人工智能也会明显削弱个人技能。

这种衰退往往源于由自我怀疑和冒名顶替综合症驱动的恶性循环。 AI 生成的内容常缺乏个人特色，不像作者本人的语气，也难以传达预期的细微差别。这种脱节使人更难独立创作，因为产出听起来像机器写的，而非真实的自我表达。

在软件开发领域，这种影响更为深刻。完全依赖提示（prompting）近两年、几乎一行代码都没亲自写过后，很多人开始感觉自己正在丧失最基本的编程技能。职业本身不会消失，总会需要会读写代码的人，但向以人工智能为主导的开发转变，确实让许多人失去了曾经定义他们职业生涯的技艺与自豪感。

不过也有希望，人工智能或许能扭转行业长期以来的一些问题。几十年来，软件开发者的需求远超供给，这在一定程度上拉低了行业的专业标准。在计算机科学成为主流职业之前，编程主要由学术界、物理学家和数学家主导。 AI 有可能提高真正掌握这门技艺所需的门槛，从而把一些曾经丧失的职业精神带回行业。

甚至把这番挣扎写出来，也是与想借助这些工具的冲动作斗争。总有挥之不去的焦虑，担心文字表述不清或读起来别扭，于是忍不住把内容交给像 Claude 这样的 AI 去验证。要摆脱对这些工具的依赖，就必须积极对抗它们助长的自我怀疑，夺回自己的声音与能力。 The temptation to use AI for writing, coding, or drafting documents is becoming increasingly difficult to resist. However, this convenience comes at a steep cost, as the reliance on these tools seems to be actively diminishing the ability to produce original work. Even for someone who didn't consider themselves a poor writer before, the frequent use of AI is causing a noticeable decline in personal skill and capability.

This decline often stems from a cycle involving self-doubt and imposter syndrome. When AI generates content, the results often lack a personal touch, sounding nothing like the individual's own voice or failing to convey the exact nuances intended. This disconnect can make the user feel even less capable of producing work independently, as the output feels artificial rather than authentic.

In the realm of software development, the impact has been even more profound. After relying entirely on prompting for nearly two years without writing a single line of code, there is a growing sense of loss regarding the fundamental skill of programming. While the profession itself isn't disappearing, and there will always be a need for people who can actually read and write code, the shift toward AI-driven development is causing many to lose the very craft that once defined their lives.

There is a hope, however, that AI might actually help reverse a long-term trend in the industry. For decades, the massive demand for software developers has outstripped the supply, which has arguably led to a decline in the professional standards of the field. Before computer science became a mainstream profession, programming was primarily the domain of academics, physicists, and mathematicians. By potentially raising the bar for what is required to truly master the craft, AI might help return some of that lost professionalism to the industry.

Even the act of writing about this struggle is a battle against the urge to use these tools. There is a constant, nagging anxiety that a piece of writing might not make sense or might read awkwardly, which leads to the impulse to run text through an AI like Claude for validation. Overcoming this dependency requires actively fighting back against the self-doubt that these technologies feed on, in order to reclaim one's own voice and skills.

该仓库包含针对 CVE-2026-42945 的概念验证利用代码。该漏洞存在于 NGINX 的 `ngx_http_rewrite_module` 中，可追溯到 2008 年，是一起严重的堆缓冲区溢出问题。使用 `rewrite` 和 `set` 指令的服务器可被未认证地远程执行任意代码。与另外三个内存损坏问题（CVE-2026-42946 、 CVE-2026-40701 、 CVE-2026-42934）一起，该漏洞由 DepthFirst 的安全分析系统在一次接入 NGINX 源代码后自动发现。

漏洞源于 NGINX 双遍脚本引擎处理过程中的不一致：先进行长度计算以确定所需缓冲区大小，再执行数据复制。当 `rewrite` 的替换字符串包含 `?` 时，主引擎会设置 `is_args` 标志，但长度计算是在一个全新且已清零的子引擎上进行的。因此长度遍看到 `is_args = 0` 并返回原始捕获长度，而复制遍看到 `is_args = 1`，并以 `NGX_ESCAPE_ARGS` 调用 `ngx_escape_uri`，把每个可转义字节扩展为 3 字节。结果复制操作会用攻击者控制的 URI 数据溢出分配不足的堆缓冲区。

攻击利用跨请求的堆布局（heap feng shui）来破坏相邻 `ngx_pool_t` 结构的 `cleanup` 指针。由于 URI 字节不能包含空字节，攻击者通过 POST 请求体进行喷射以污染该指针，从而把执行流重定向到伪造的 `ngx_pool_cleanup_s`，在内存池销毁时触发对 `system()` 的调用，进而实现远程代码执行。

受影响的 NGINX Open Source 版本为 0.6.27 到 1.30.0，已在 1.31.0 和 1.30.1 中修复；NGINX Plus 受影响的版本为 R32 到 R36，已在 R36 P4 、 R35 P2 和 R32 P6 中修复。

仓库包含可在 Ubuntu 24.04.3 LTS 上测试的部署脚本和 Python 概念验证代码。用户可以用 `./setup.sh` 构建容器，运行 `docker compose -f env/docker-compose.yml up` 启动易受攻击的 NGINX 服务器，并使用 `python3 poc.py --shell` 弹出 shell 。该项目在 GitHub 上获得了 422 颗星和 76 个 fork，主要贡献者为 Zhenpeng Lin (Markakd) 。 This repository contains a proof-of-concept exploit for CVE-2026-42945, a critical heap buffer overflow vulnerability in NGINX's `ngx_http_rewrite_module` that dates back to 2008. The bug enables unauthenticated remote code execution on servers using `rewrite` and `set` directives. Along with three other memory corruption issues (CVE-2026-42946, CVE-2026-40701, CVE-2026-42934), it was discovered autonomously by DepthFirst's security analysis system after a single click of onboarding the NGINX source.

The vulnerability stems from a mismatch in NGINX's two-pass script engine process. First, the required buffer size is calculated, then data is copied in. The `is_args` flag is set on the main engine when a `rewrite` replacement contains `?`, but the length-calculation pass runs on a freshly zeroed sub-engine. This means the length pass sees `is_args = 0` and returns raw capture length, while the copy pass sees `is_args = 1` and calls `ngx_escape_uri` with `NGX_ESCAPE_ARGS`, expanding each escapable byte to 3 bytes. The copy then overflows the undersized heap buffer with attacker-controlled URI data.

Exploitation uses cross-request heap feng shui to corrupt an adjacent `ngx_pool_t`'s `cleanup` pointer, sprayed via POST bodies since URI bytes can't contain null bytes. This redirects execution to a fake `ngx_pool_cleanup_s` invoking `system()` on pool destruction. The vulnerability affects NGINX Open Source versions 0.6.27 through 1.30.0, fixed in 1.31.0 and 1.30.1, and NGINX Plus versions R32 through R36, fixed in R36 P4, R35 P2, and R32 P6.

The repository includes setup scripts and a Python proof-of-concept that can be tested on Ubuntu 24.04.3 LTS. Users can build the container with `./setup.sh`, start the vulnerable NGINX server with `docker compose -f env/docker-compose.yml up`, and pop a shell with `python3 poc.py --shell`. The project has garnered 422 stars and 76 forks on GitHub, with the primary contributor being Zhenpeng Lin (Markakd).

现代汽车几乎成了装在轮子上的电脑，配备大量传感器、摄像头和麦克风，不断收集遥测数据。这些信息包括你的位置信息、车速，甚至驾驶员注意力等指标，常被经纪商变现或与保险公司共享。除了隐私问题外，还存在严重的安全风险——从可被远程解锁的漏洞到员工访问敏感摄像头画面的事件不等。为了掌控这些数据，作者决定从自己的 2024 RAV4 Hybrid 上物理拆除调制解调器和内置 GPS，从而阻断车辆向制造商发送遥测数据的通道。

拆除这些部件会牺牲部分功能。失去数据通信模块（Data Communication Module, DCM）后，车辆将无法接收空中更新（OTA）、使用云端服务，也会丧失自动紧急 SOS 功能，这在安全性上需要权衡。此外，车辆的麦克风是通过 DCM 接线的，因此需要一个旁路套件来确保通过 CarPlay 仍能拨打电话。作者还拆除了 GPS 天线，以避免一个已知错误：车载定位信号与手机 GPS 冲突导致导航失灵。尽管这些改动可能影响部分保修条款，但 Magnuson-Moss Warranty Act 保护车辆其余部分的保修权利不会因此失效。

维护隐私的关键在于手机如何与车辆连接。如果驾驶员使用蓝牙，车辆实际上可以把手机当作网络连接继续向 Toyota 发送遥测数据。为避免这种情况，作者建议专门使用有线 USB 连接进行 CarPlay 。若偏好无线便捷，也可使用蓝牙转有线 USB 的适配器，欺骗车辆将连接识别为有线，从而阻断数据上报。

拆除调制解调器的物理过程难度中等，需准备内饰拆卸工具、棘轮扳手和各种套筒等基础工具。步骤包括拆下换挡组件、拉出主机并访问被藏在多块面板后、用 8mm 螺栓固定的 DCM 。取出调制解调器后，需要安装专用的 DCM 旁路套件以恢复麦克风功能。这部分工作需耐心，在狭窄空间内小心操作以免损坏线束。

断开 GPS 天线则要简单得多，只需拆掉信息娱乐屏和主机后方的后盖板，通过排查找到那根单线 GPS 天线并拔除。全部复位后，可通过检查信息娱乐屏上的"无连接（no connection）"图标以及确认车顶控制台的 SOS 指示灯熄灭来验证是否成功。

最终目标是确保没有遥测数据离开车辆。作者指出未来车辆设计可能会把这些组件更深度地集成，使类似改动更难或不可能，但这次改造实现了一定程度的数字自主。整个经历也凸显了制定更严格隐私保护法规的迫切性，以保护消费者免受现代汽车技术中持续数据采集的影响。 Modern cars have essentially become computers on wheels, equipped with a vast array of sensors, cameras, and microphones that constantly collect telemetry data. This information, which includes your location, speed, and even driver attention levels, is often monetized by brokers or shared with insurance companies. Beyond privacy concerns, there are significant security risks, ranging from vulnerabilities that allow remote unlocking to instances where employees have accessed sensitive camera footage. To take control of this data, the author decided to physically remove the modem and the built-in GPS from their 2024 RAV4 Hybrid, effectively cutting the car off from sending telemetry back to the manufacturer.

Removing these components does come with certain trade-offs regarding functionality. Without the Data Communication Module (DCM), the car loses over-the-air updates, cloud-based services, and automatic emergency SOS functionality, which presents a safety consideration. Additionally, because the car's microphone is wired through the DCM, a bypass kit is necessary to ensure phone calls can still be made via CarPlay. The author also disconnected the GPS antenna to prevent a known bug where the car's location signal would conflict with the phone's GPS, causing navigation errors. While this may affect certain parts of the vehicle warranty, the Magnuson-Moss Warranty Act protects the rest of the car's coverage from being voided by these specific modifications.

A critical detail for maintaining privacy is how the phone connects to the vehicle. If a driver uses Bluetooth, the car can actually use the phone as an internet connection to continue sending telemetry data to Toyota. To prevent this, the author recommends using a wired USB connection for CarPlay exclusively. For those who prefer wireless convenience, a Bluetooth-to-wired USB adapter can be used to trick the car into treating the connection as a wired one, thereby blocking the data transmission.

The physical process of removing the modem is described as a medium-difficulty project that requires basic tools like a trim removal kit, ratchets, and various sockets. The process involves removing the shifter assembly, pulling out the radio, and accessing the DCM, which is tucked away behind several panels and held in place by 8mm bolts. Once the modem is removed, a specialized DCM Bypass Kit is installed to restore the microphone functionality. This part of the job requires patience and careful maneuvering in tight spaces to avoid damaging existing wiring.

Disconnecting the GPS antenna is a much simpler task, involving the removal of the back panel behind the infotainment screen and the head unit. Through a process of elimination, the author identified the specific single-wire cable for the GPS antenna and unplugged it. Once everything is reassembled, the success of the project can be confirmed by checking the infotainment screen for a "no connection" icon and ensuring the SOS light in the overhead console is off.

Ultimately, the goal was to ensure that no telemetry leaves the car. While the author notes that these components may become more deeply integrated into future vehicle designs, making such modifications harder or impossible, this project successfully achieves a level of digital autonomy. The experience highlights a growing need for stronger privacy laws to protect consumers from the constant data harvesting inherent in modern automotive technology.

如果你能把一块完整的桌面级 GPU 接到 MacBook Air 上会怎样？事实证明这是可行的。作者通过 Thunderbolt eGPU 扩展坞把 NVIDIA RTX 5090 连到 M4 MacBook Air，并在 macOS 上运行的 Linux 虚拟机中使用该 GPU 。这个项目涉及大量工程挑战——包括 PCI 直通、 DMA 映射的变通方案和 x86 仿真层——但最终让这套方案在游戏和 AI 推理上都能工作。

核心问题是 macOS 在 Apple Silicon 上没有对现代 NVIDIA 或 AMD GPU 的原生驱动。解决方法是让 ARM64 的 Linux 虚拟机接管并直通通过 Thunderbolt 连接的 GPU 。为此必须把 PCI 的基地址寄存器（BAR）映射到客户机虚拟机中。最初因为 QEMU 在使用 Apple 的 Hypervisor.framework 处理内存标志时的一个 bug，导致主机内核崩溃，修复方法是从设备内存映射中去掉 HV_MEMORY_EXEC 标志。至于直接内存访问（DMA），Apple 的 IOMMU 等价物 DART 施加了严格限制：总映射内存约为 1.5GB，且最多约 64k 个单独映射。为应对这些限制，作者实现了一个名为 apple-dma-pci 的虚拟 PCI 设备，拦截 DMA 映射调用，并用聚类方案把小的分配合并到更大的 256 KB 区域，从而显著减少映射数量。

其他性能优化还包括在 Apple Silicon 上启用硬件 Total Store Ordering（TSO）以通过 FEX-Emu 加速 x86 仿真、用 kprobes 修补 NVIDIA 驱动以应对 DART 的对齐限制，以及调整 QEMU 线程的调度优先级。即便如此，分层虚拟化带来的开销仍然不可忽视。基准显示，装在原生 PCIe 插槽中的相同 RTX 5090 的游戏 PC 比 MacBook Air 的 eGPU 方案快 2–4 倍。在 720p 等低分辨率下，由于仿真开销，M4 Air 的集成 GPU 反而比 eGPU 更快，但在 4K 下 eGPU 能把原本不可玩的帧率变为流畅画面。

在 AI 推理方面的表现尤其引人注目。运行 Qwen 3.6 时，eGPU 达到每秒 155 个 token，而 M4 Air 的集成 GPU 只有每秒 22 个 token，提升约 6.5 倍。更显著的是，对于 4K token 上下文的提示处理（prefill），耗时从 17 秒降到 150 毫秒，提速约 120 倍。 RTX 5090 在并发请求下的扩展性也更好，最多 4 路并发时几乎呈线性扩展。对于像 Gemma 4 这样的密集模型，性能差距更大，eGPU 的吞吐量大约是 Apple Silicon 集成显卡的 4 倍。

尽管技术上很出色，作者也承认这仍是个发烧友级别的方案。存在的稳定性问题包括 FEX-Emu 中 Steam 崩溃、 DMA 映射碎片化需要重置 GPU 、以及分发驱动所需的特殊 Apple 授权。该方案需要自行编译定制软件并加载内核扩展。作者正在与上游 QEMU 合作合并这些补丁，并希望未来在 Apple Silicon 上对 Linux 实现原生 Thunderbolt 支持能消除许多现有限制。目前这个项目证明了：一台 22W 的笔记本也能驱动一块 600W 的 GPU，但原生 PC 仍明显更快。 What if you could strap a full desktop GPU to your MacBook Air? Turns out, you can. The author set out to connect an NVIDIA RTX 5090 to an M4 MacBook Air using a Thunderbolt eGPU dock, running the GPU inside a Linux virtual machine on macOS. The project involved significant engineering challenges, including PCI passthrough, DMA mapping workarounds, and x86 emulation layers, but ultimately succeeded in making the setup functional for both gaming and AI inference.

The core technical challenge was that macOS lacks native drivers for modern NVIDIA or AMD GPUs on Apple Silicon. The solution involved running Linux in an ARM64 VM and passing through the Thunderbolt-connected GPU. This required mapping PCI Base Address Registers (BARs) into the guest VM, which initially caused host kernel panics due to a bug in how QEMU handled memory flags with Apple's Hypervisor.framework. The fix involved removing the HV_MEMORY_EXEC flag from device memory mappings. For Direct Memory Access (DMA), Apple's IOMMU equivalent called DART imposed strict limits of about 1.5GB total mapped memory and 64k individual mappings. To work around this, the author created a virtual PCI device called apple-dma-pci that intercepts DMA mapping calls and uses a clustering scheme to reduce the number of individual mappings by grouping small allocations into larger 256kB regions.

Additional performance optimizations included enabling hardware Total Store Ordering (TSO) mode on Apple Silicon to accelerate x86 emulation through FEX-Emu, patching the NVIDIA driver via kprobes to handle DART's alignment constraints, and adjusting QEMU's thread scheduling priorities. Despite these improvements, the layered virtualization approach still incurred significant overhead. Benchmarks showed that a gaming PC with the same RTX 5090 in a native PCIe slot was 2-4x faster than the MacBook Air eGPU setup. At lower resolutions like 720p, the M4 Air's integrated GPU actually outperformed the eGPU configuration due to emulation overhead, but at 4K the eGPU transformed unplayable framerates into smooth gameplay.

The most compelling results came from AI inference benchmarks. Running Qwen 3.6, the eGPU setup achieved 155 tokens per second compared to 22 tokens per second on the M4 Air's integrated GPU, a 6.5x improvement. More dramatically, prompt processing (prefill) for a 4K-token context dropped from 17 seconds to 150 milliseconds, a 120x speedup. The RTX 5090 also scaled much better with concurrent requests, nearly linear scaling up to 4 concurrent streams. For dense models like Gemma 4, the performance gap was even more pronounced, with the eGPU delivering roughly 4x the throughput of Apple Silicon's integrated graphics.

While the project demonstrates impressive technical achievement, the author acknowledges it remains firmly in the hobbyist category. Stability issues include Steam crashes in FEX-Emu, DMA mapping fragmentation requiring GPU resets, and the need for a special Apple entitlement to distribute the driver. The setup requires building custom software and loading kernel extensions. The author is working with upstream QEMU to integrate the patches and hopes future improvements like native Thunderbolt support in Linux on Apple Silicon could eliminate many of the current limitations. For now, it stands as a proof of concept that a 22W laptop can harness a 600W GPU, even if a native PC remains significantly faster.

2026 年 5 月，MIT 校长 Sally Kornbluth 向全校发表讲话，指出学院正面临两大相互关联的挑战：资金紧张和人才输送渠道萎缩。她说，对捐赠基金收益征收 8% 的税已经造成严重的预算压力，各部门被迫进行痛苦削减。尽管 Congress 最近为一些研究机构恢复了部分资金，但 Sally Kornbluth 强调，MIT 获得的联邦研究资助仍比上一年下降了超过 20%，而包括联邦与非联邦在内的总受资助研究活动比一年前减少了约 10% 。

第二个主要担忧是对 MIT 人才渠道的影响。联邦资助的不确定性使各系在招收新研究生时更为谨慎，除 Sloan 和 EECS MEng 项目外，来年新入学研究生人数预计下降近 20% 。这可能导致研究生总数减少约 500 人，从而削弱研究能力并减少本科生的指导机会。她强调，这不仅损害 MIT 的使命，也意味着数百名极具潜力的人将失去接受 MIT 教育的机会，学院也会因此失去他们未来的贡献。

她指出，这些并非小幅调整，而是研究势头实质性下滑的表现，影响教师、学生，甚至国家创新能力。那些有着强劲资助记录的资深教师已经在减少研究生和博士后名额；尽管 MIT 正在制定短期支持计划，但这些措施难以从根本上解决长期问题。基础性发现研究的减少威胁着未来的科学突破、技术进步以及下一代科学家的培养。

尽管如此，Sally Kornbluth 对 MIT 的韧性表示信心，肯定了社区持久的创造力和干劲。她列举了学院正在推进的若干积极举措：教师正在抓住新的联邦机会，例如 Department of Energy 的 Genesis Mission，MIT 已提交 176 份提案；学院在扩大产学合作，如新成立的 MIT-IBM Computing Research Lab，聚焦 AI 与量子计算；同时也在通过专业硕士项目和加强慈善筹款探索新的收入来源。

此外，MIT 正在加大在 Washington 的倡导力度，跨党派推动，强调捐赠基金税的危害及好奇心驱动研究的重要性。 Sally Kornbluth 正亲自与 Congress 和行政领导层沟通，强调 MIT 对国家的价值。她最后感谢 MIT 社区的奉献，并呼吁大家继续携手应对挑战，维护学院的使命与影响力。 In May 2026, MIT President Sally Kornbluth addressed the community about two interconnected challenges facing the Institute: funding pressures and a shrinking talent pipeline. She acknowledged the significant budget constraints caused largely by an 8% tax on endowment returns, which has forced painful cuts across departments. While Congress recently restored some research agency funding, Kornbluth emphasized that federal research awards to MIT have still declined by over 20% compared to the previous year, and total sponsored research activity—combining federal and non-federal sources—is now 10% lower than a year ago.

The second major concern is the impact on MIT's talent pipeline. Uncertainty around federal funding has made departments cautious about admitting new graduate students, leading to a nearly 20% drop in new graduate enrollments outside of Sloan and the EECS MEng program for the coming year. This could result in about 500 fewer graduate students overall, reducing research capacity and mentorship opportunities for undergraduates. Kornbluth stressed that this not only harms MIT's mission but also means hundreds of exceptionally talented individuals will miss out on an MIT education, and the Institute will lose their potential contributions.

She highlighted that these are not minor adjustments but represent a real decline in research momentum, affecting faculty, students, and ultimately national innovation. Senior faculty with strong grant records are already cutting graduate students and postdocs, and while MIT is developing short-term support plans, these won't solve the long-term problem. The reduction in basic discovery research threatens future scientific breakthroughs, technological advances, and the training of next-generation scientists.

Despite the challenges, Kornbluth expressed confidence in MIT's resilience, noting the community's enduring creativity and drive. She outlined several proactive steps the Institute is pursuing: faculty are responding to new federal opportunities like the Department of Energy's Genesis Mission, with MIT submitting 176 proposals; the school is expanding industry partnerships, such as the new MIT-IBM Computing Research Lab focused on AI and quantum computing; and it is exploring new revenue streams through specialized master's programs and enhanced philanthropic outreach.

Additionally, MIT is intensifying advocacy efforts in Washington, working across party lines to highlight the damaging effects of the endowment tax and the importance of curiosity-driven research. Kornbluth is personally engaging with congressional and administration leaders to underscore MIT's value to the nation. She concluded by thanking the MIT community for their dedication and urged continued collective action to navigate this difficult period while preserving the Institute's mission and impact.