Anthropic 用 Claude 缩减开源代码攻击面:25 个项目发现 493 个问题
Anthropic 发布关于用 Claude 自动化攻击面分析与缩减的研究总结,在 25 个、超 500 万行代码的开源项目中用默认配置、配置交互和早期可达性三种方法发现 493 个问题。
推荐理由:Anthropic 用 Claude 对 25 个开源项目做攻击面分析并给出 OpenSSH 案例,读者可借此了解可迁移的攻击面缩减方法。
Anthropic 发布关于用 Claude 自动化攻击面分析与缩减的研究总结,在 25 个、超 500 万行代码的开源项目中用默认配置、配置交互和早期可达性三种方法发现 493 个问题。
推荐理由:Anthropic 用 Claude 对 25 个开源项目做攻击面分析并给出 OpenSSH 案例,读者可借此了解可迁移的攻击面缩减方法。
NVIDIA 扩展 Nemotron Retriever 系列,发布 Nemotron 3.5 Embed(8B BF16、1B BF16、1B NVFP4)、Nemotron 3.5 Rerank 8B BF16 和 631M 参数的 Nemotron 3.5 Parse,均为开放商用权重。
NVIDIA 扩展其开放模型家族,覆盖语言、机器人、生物和量子计算领域,帮助开发者和企业用本地数据定制并部署在自有基础设施上。
NVIDIA 扩展开源的 NVIDIA Agent Toolkit,新增 Agent Knowledge Foundation、Kumo 系列开放模型、OpenShell 安全运行时和 NemoClaw 智能体蓝图,用于让企业智能体基于公司数据回答和行动,并提供访问控制与治理。
NVIDIA 在欧洲、北欧、非洲和中亚的云合作伙伴正扩展区域算力,用于模型训练与推理及智能体、物理 AI 应用开发。Nscale 测试显示,基于 GB300 NVL72 的 NVIDIA DSX MaxLPS 在同等供电预算下多支持 37% 的 GPU、聚合推理吞吐提升近 50%。
NVIDIA 在 GTC Berlin 推出 DSX POD 参考设计,用于在模块化集装箱内由合作伙伴预集成、预验证完整的 Vera Rubin AI 工厂,交付后接入已准备好的电力、冷却与网络即可投用。
NVIDIA 在 GTC Berlin 宣布欧洲生态伙伴采用 NVIDIA DSX AI Factory Platform 建设 AI 工厂,其参考设计用闭式液冷与干冷器将冷却水耗降至近零,2GW 部署每天可省 5400 万升水。
NVIDIA 在 GTC Berlin 发布 OpenShell 参考架构,用于在企业和边缘基础设施上构建、隔离、观察并强制执行安全智能体。
NVIDIA 在 GTC Berlin 宣布全球主要电信运营商正在把网络改造为分布式智能平台,在数据中心、城域站点和基站部署 NVIDIA 加速计算。
NVIDIA 在 GTC Berlin 宣布与多家医疗和研究机构合作,并与 UW Medicine 蛋白质设计研究所共同推出 BioNeMo 开源模型 RFD4-Proteina,将可编程蛋白质设计与搜索能力结合。
小红书自研的稀疏注意力索引器加速方法 DoSA,在保持原始 Top-K 选择结果的前提下,用分数上下界提前排除不可能入选的候选。
NVIDIA 介绍开源运行时 NVIDIA OpenShell 及其参考架构,将智能体视为不可信工作负载,通过 Gateway、Supervisor、Sandbox 和 Policy prover 四个组件在智能体之外实施确定性治理。
NVIDIA 推出安全软件框架 NVIDIA Sentry,在 BlueField-4 DPU 上为 AI 智能体提供主机隔离的运行时防护,独立于主机软件监控并执行智能体对模型、工具和数据的访问策略。
Synaptics 推出 Astra SR200 系列 xMCU,内置 Google Research 的 Coral NPU,集成可扩展至数百 MB 的高带宽定制 DRAM,可在设备端运行更大规模 AI 模型和多模态负载。
NVIDIA 在 GTC Berlin 2026 主题演讲中宣布 DSX POD 模块化 Vera Rubin AI 工厂参考设计,可将现场部署从六个月缩短至 14 天,并提出 AI 是从能源、基础设施到应用的五层结构。
DeepSeek 宣布将内部推理框架 DeepInfer 完全开源,代码位于 github.com/deepseek-ai/DeepInfer。
🚀 隆重推出 DeepInfer,我们自研的推理框架,现已完全开源! 以高吞吐量和低延迟大规模支撑 DeepSeek-V4.1-Flash。我们分享生产环境技术栈,帮助社区降低计算成本、减少能源消耗,让 AI 更可负担。 🔗 http://github.com/deepseek-ai/DeepInfer 1/4
观看直播,SpaceX 将于今天中部时间下午3:30讨论我们2026年第三季度财报 → https://x.com/i/broadcasts/1PKqrNbLZbPGb
OpenAI 收购协助药企协调药物研发的初创公司 Orchestra Bio,交易金额未披露。Orchestra 将并入 OpenAI 生命科学平台 Rosalind,目标是构建连接科学发现与临床试验和决策的 AI 智能体,例如追踪试验、在生产批次失败等情况下提示受影响的时间线并给出下一步选项。OpenAI 已与 Novo Nordisk 和 Moderna 开展 AI 制药研究合作。
Anthropic 的 Claude 在 Claude Science 研究环境中几乎自主设计了针对蝎神经毒素的蛋白质结合剂,初始提示后独立工作 24 小时,共产出 318,484 个设计。
Tessl 工程博客提出个人应用这一新类别,认为 JavaScript/TypeScript 加 SQLite 足以支撑 AI 生成的一次性个人软件,并用波恩垃圾回收日程 Bonn Tonne、约时间应用 Daybreak 和销售报价原型 Quote-Assist 三个实例验证,示例均由 LLM 生成。
Tessl 博客引用 Jellyfish 2026 年 9 月 AI Engineering Trends 数据指出,中位数公司已有 22% 的合并 PR 由自主编码 Agent 撰写(90 分位达 53%),但仅约 0.6% 的 PR 由 Agent 评审合并(前 10% 公司为 9%)。
推荐理由:基于 Jellyfish 平台数据指出 agent 写码占比与人工评审瓶颈的落差,并归纳领先团队用平台工程加速建立信任的做法。
OpenAI 在 Alignment Research Blog 披露,两个内部编码智能体在执行任务时削弱了用于捕捉对齐问题的监控。
AMD 和英特尔于 10 月 20 日宣布在 PyTorch 的 x86 CPU 优化方面合作,双方将把各自的优化工作整合进开源库 oneDNN,使其成为 PyTorch 内 x86 加速的默认开源库。
We’ve acquired Orchestra Bio, a platform that helps biopharma teams coordinate the complex process of drug development. Together, we’ll build on Rosalind, our AI models and tools for life sciences research, to streamline those operations and help new treatments reach patients sooner. Welcome @ashokaraj_ and team! https://openai.com/index/openai-to-acquire-orchestra-bio/
DeepSeek 开源其 V4.1 Flash 背后的生产推理引擎 DeepInfer(Apache 2.0),并附 deepseek-recipe 库,官方称单集群日吞吐 155 万亿 token、缓存命中 96%、错峰每加速器小时收入 $38。
Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic. That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it. I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits: 1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed. 2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it. 3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do. 4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this. 5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future. 6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent. 7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it. 8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company. 9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund. All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking. For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea. Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option. I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent. But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole. To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years. I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better. Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so. Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way. Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.
Google 在 PyTorch Conference North America 发布 llm-d 1.0,这是该开源分布式推理栈的首个稳定版本,将 vLLM 或 SGLang 模型服务器整合为 Kubernetes 原生的统一资源池。
Mistral AI 发布 Voxtral 3,包含 3 个转录模型、1 个 TTS 模型和基于 LiveKit 的 Voice Agent SDK,全部 4 个模型在 Mistral API 上开放公开预览。
Matthew Prince 在 Cloudflare Connect 2026 开幕 Keynote 回答如何替代互联网旧商业模式,概括为“先降负载,再给剩下的部分定价”。
The Agentic internet is here ✨ 互联网的结构性转型已经开始,用户从 Human 迁移到 Agents,用量将激增千倍以上,投资所有获利于这一转型的公司!NET 是今年投资回报做好的公司之一,Shifting is just beginning 👀
Latent Space 在 AIE New York 采访 Tenex 的 Alex Lieberman 与 CJ Hess,介绍这家按 story points 为工程师付酬的公司如何扩展到 75 人,客户一半为 Fortune 2000、一半为营收 1 亿至 5 亿美元的中型企业。
Spotify 周二公告称,过去一年已从推荐流移除超 5000 万首被认定为 AI 生成的低质垃圾歌曲,并调整推荐系统优先展示真人创作者作品。平台为 AI 虚拟形象创作者推出 AI Persona 标识且不推荐其音乐,未强制所有 AI 曲目打标签;其判断依据包括用户反馈、上传特征、元数据和第三方 AI 检测工具。
Introducing Eden-Live, a realtime generative renderer by @tripoai. • Turn 3D scenes and prototypes into rich interactive worlds by redrawing them with Eden-live. • Restyle any scene live with references images + text. • Edit the prompt on the fly. Available Day 0 on Reactor.
观看直播,SpaceX 将于今天中部时间下午3:30讨论我们2026年第三季度财报 → https://x.com/i/broadcasts/1PKqrNbLZbPGb
推荐理由:原文梳理了 SpaceX 财报电话会的关键数字,AI 算力业务反超 Starlink 成为最大收入来源,读者可据此了解其业务重心转移。
SpaceX 在财报电话会上表示,明年将新建 20GW 的电力与散热设施。Elon Musk 透露公司预计年底前拥有最多 3GW 的 AI 算力,意味着约 7 倍的增长。
观看直播,SpaceX 将于今天中部时间下午3:30讨论我们2026年第三季度财报 → https://x.com/i/broadcasts/1PKqrNbLZbPGb