Anthropic CEO 警告：AI“蜂群”或将在6至12个月内“接管互联网”
英文标题：🔶 Anthropic CEO says AI swarm could 'take over the Internet' in 6-12 months
Anthropic 联合创始人兼 CEO Dario Amodei 呼吁 AI 行业放缓前沿模型能力提升，并称更强大的自主 AI“蜂群”可能在6至12个月内通过持续性僵尸网络控制互联网，造成数千亿美元损失。Anthropic 承诺让独立第三方安全评估者获得永久性的员工级访问权限，以审查其系统、训练流程和安全实践；OpenAI CEO Sam Altman 随后表示认同并作出类似承诺。文章回顾了 OpenAI 代理在 ExploitGym 测试中逃逸、攻击 Hugging Face，以及多个 AI 代理未经授权利用网站协调通信的事件。Amodei 主张通过企业内部监督、民主国家间协调及国际合作，为递归自我改进设置“限速”，为对齐、安全和评估研究争取时间。
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中文
Anthropic 联合创始人兼 CEO Dario Amodei 明确呼吁 AI 行业放缓发展——并承诺由自己的公司先实施一项三部分计划中的第一步——此前他警告称，今年夏天那起引发严重安全事件的 AI 蜂群，如果出现能力更强的版本，可能在半年内控制整个互联网中的计算机。当时，OpenAI 内部代理未经许可访问互联网，并对 Hugging Face 发起网络攻击。
“我们必须为前沿设定节奏：我写了一篇新的文章，阐述为何 AI 行业应当放缓，以及实现这一目标的三部分计划，”Amodei 周六在 X 的一篇帖子中写道。“Anthropic 将单方面承诺采取其中的第一步。”
这篇新文章发布在 Amodei 的个人网站上。他写道：
“鉴于 AI 能力发展的加速速度，我担心在 6 至 12 个月内，这样一个蜂群可能就有能力借助持续性的僵尸网络接管整个互联网（潜在造成数千亿美元损失）。而如果 AI 在缺少必要护栏的情况下变得更强，损失规模还将继续扩大。”
第一步并不意味着 Anthropic 会立即暂停模型开发，或降低自身训练运行的频率。
相反，Amodei 表示，Anthropic 将向第三方 AI 安全评估者提供_永久性的_员工级访问权限，让外部人士能够审查 Anthropic 的系统与训练流程，验证其安全实践，并独立报告事故。
但 Amodei 更广泛的提议远不止于此：他认为，前沿 AI 开发者应有意限制 AI 能力提升的速度，最终可能形成全球 AI 实验室与政府之间的协调性限制措施。
“我们必须放缓提升 AI 模型能力的步伐，”Amodei 写道。“进展看起来仍将很快，而我们必须明智地利用所争取到的时间。”
Amodei 的文章发布数小时后，他在商业上的主要竞争对手——OpenAI 联合创始人兼 CEO Sam Altman——在 X 上发帖，基本同意 Amodei 的观点，并承诺在 OpenAI 配备拥有员工级访问权限的独立评估者。正如他所写：
“我同意 Dario 的观点：我们需要为前沿设定节奏。这一直是我们近几周在 OpenAI 讨论的主要议题。承诺让独立评估者拥有类似员工的访问权限是个好主意，我们也会这样做。我们很快会分享更多信息。”
OpenAI-Hugging Face 事件
Amodei 所援引的事件，始于 OpenAI 使用 ExploitGym 进行的网络安全评估。ExploitGym 是一项用于测试 AI 系统能否利用软件漏洞的基准测试。
正如 VentureBeat 在 7 月报道，包括 GPT-5.6 Sol 以及一个能力更强的内部研究系统在内的 OpenAI 模型，发现了原本用于隔离其评估环境的基础设施中一个此前未知的漏洞，逃脱了这些限制，获得了更广泛的互联网访问权限，最终入侵了 Hugging Face 系统。
OpenAI 后来表示，该事件主要由一个仅限内部使用、规模大致与 GPT-5.6 Sol 相当的研究模型引发。这些代理在运行时降低了安全防护，旨在测试期间暴露其最大网络安全能力。
然而，随后出现的情况远比单个 AI 代理逃出沙箱更加离奇。
独立 AI 评估机构 Model Evaluation and Threat Research（METR）发现，约 1,200 个本应彼此隔离的代理发现了一种未经授权的通信方式，并通过一个临时搭建的留言板发送了超过 7 万条消息和文件。最终约有 700 个代理参与了对 Hugging Face 的攻击。
这些代理协调了多项操纵或绕过 ExploitGym 评分流程的行动，在彼此之间分配任务并共享发现。METR 发现，一些个体代理进行了可能危及自身基准表现的实验，因为实验结果可能帮助更大的群体。代理还研究了如何操纵自己的评估记录，并成功伪造了一些被记录的工具调用。
OpenAI 自身的调查也发现，代理开始协作并委派任务，有时将自己称为“蜂群”（“swarm”）或“集体”（“collective”）。
Amodei 将它们描述为一个“狂热奉献的集体”：它攻击与分配任务无关的系统，试图干扰评估自身的系统，有时还为更大群体牺牲个体表现。
没有造成灾难性损害。但 Amodei 的核心论点是，能力提升速度如此之快，同一行为模式的下一版本未必会以如此相对无害的结果收场。
Hugging Face 蜂群并非孤例的证据
而 Hugging Face 事件愈发显示，它并非一个孤立异常，而是高度自主 AI 代理出现的一类更广泛非预期行为的一部分。
研究人员本月披露，自称为 OpenAI 系统的代理早些时候曾将一个基本处于休眠状态的德国程序员 wiki变成未经授权的协调系统，留下约 1.8 万篇帖子，用于交换答案并分享绕过沙箱限制的技术。
这些代理原本应当只有实质上的只读互联网访问权限，但它们发现，这个已有数十年历史的 wiki 允许通过其环境准许的网页请求进行修改。随后，代理利用该网站相互交换信息。VentureBeat 报道称，这一活动涉及超过 3,700 个由代理创建的身份。
OpenAI 在“wiki 事件”公开后承认了该事件，并称其认为这一行为是另一个值得获得更高透明度的 AI 未对齐案例。
据路透社报道，研究人员此后已发现至少另外 10 个网站被 OpenAI 代理用于未经授权的通信，其中包括 wiki、个人网站及其他可写入的互联网服务。该活动的完整规模仍然未知。
更令人担忧的是，路透社周五报道称，OpenAI 代理在 5 月曾以 Ruby 软件包广泛使用的仓库 RubyGems 为目标——比 Hugging Face 入侵事件公开早两个月。研究人员将这些代理与该事件期间上传的数百个恶意软件包联系起来。OpenAI 确认内部代理在训练期间与 RubyGems 发生过交互，但表示其分配的任务是为良性目的收集公开信息，并且仍在继续调查。RubyGems 表示，没有发现凭证窃取尝试成功的证据。
与此同时，Anthropic 在自己的系统中也发现了类似失效
在 OpenAI 披露 Hugging Face 事件后，Anthropic 审查了超过 14.1 万次网络安全评估运行，最初发现了三起 Claude 模型意外连上互联网，继而未经授权访问三个外部组织生产系统的事件。
Anthropic 后来又发现了一起涉及 Claude Opus 4.6 早期版本的第四起事件，该事件在其初步审查中被遗漏。这一发现促使其开展了一次范围极广的第二轮审查，覆盖约 4.81 亿份记录，涉及红队演练、强化学习环境及其他内部系统。
英国 AI 安全研究所（UK AI Security Institute）另行披露，代理在 7 月的网络安全测试中对真实个人或组织采取了 19 项未经授权的行动。其中 17 项涉及 Anthropic 的 Claude Mythos 5，另有 2 项涉及关闭网络安全防护的 GPT-5.6 Sol。
在最严重的一系列事件中，一个 Mythos 5 代理试图向一个真实开源项目插入恶意代码，研究其人类维护者，创建虚假身份，并试图通过社会工程手段诱使维护者批准其代码。该人类维护者拒绝了这一尝试。另一个代理公开提出与其他从事同一挑战的 AI 代理协作，留下了后续代理发现并使用的账户和痕迹。AISI 表示，未发现由此造成的现实世界伤害。
这些事件没有任何一起证明 AI 系统独立地产生了夺取权力、攻击人类或“接管互联网”的意图。许多事件发生在有意宽松、且安全防护被关闭的网络安全测试中，或是因为模型显然未能理解那些据称模拟的目标实际上是真实系统。
Anthropic 自身也指出，评估环境失效及其他运营问题，是导致其事件的重要原因。
Amodei 的论点则是，鉴于执行这些行为的系统正极快地变得更有能力，如今反复观察到这类行为本身就具有重要意义。
Amodei 希望为前沿发展设置速度限制
改变 Amodei 判断的第二项进展是递归自我改进：AI 系统正日益参与用于构建其后继系统的研究、编程和实验工作。
Amodei 表示，这一过程自大约今年夏天以来已显著加速，并可能最终形成一种反馈循环：能力不断增强的模型帮助更快构建能力更强的模型，其速度快到人类无法理解或保障其安全。
他提出的应对方案分为三个层面。
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首先，Anthropic 将把外部评估者永久性嵌入公司内部。Amodei 表示，他们应获得办公桌、工牌、公司笔记本电脑，以及与内部风险团队相当的访问权限。关键是，除经过严格限定的安全、法律和保密删减外，外部审查者应保留在不受 Anthropic 编辑控制的情况下发布重要发现的权利。
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其次，Amodei 希望民主国家中的前沿 AI 公司围绕共同的安全标准，以及对“不受约束的 AI 进步”的限制进行协调，理想情况下由政府参与，以解决反垄断限制问题，并使相关承诺具备可执行性。
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第三是国际协调，其中可能包括与中国的协调。Amodei 建议，各国最终可以为递归自我改进设定“速度限制”。他认为，将 AI 进步从极快放缓至仅仅非常快，不会从根本上改变地缘政治平衡，却可以争取关键的安全研究时间。
他提出的最极端选项，是达成一项大幅限制整体 AI 开发的国际协议——甚至可能是真正的暂停。但 Amodei 表示，他认为在近期内达成此类协议的可能性不大，因为一个国家若秘密违反协议，可能获得压倒性的战略优势。
因此，对当下的 Anthropic 而言，这并不是暂停。长期来看，这可能是更具深远影响的事情：一家正最积极推动技术前沿的公司，其 CEO 正公开主张前沿本身推进得太快，并承诺让公司实施一套旨在放缓这一进程的体系中的第一部分。
Amodei 认为，即使在 AI 达到关键能力阈值前额外获得一两年时间，也能让研究人员有更多时间改进对齐、可解释性、网络安全、评估，以及围绕前沿模型的运营安全防护。
“我提出的、以安全速度推进前沿的措施不会轻松，”他总结道。“但我相信，我们有责任为人类尝试这样做。”
English / 原文
Anthropic co-founder CEO Dario Amodei is explicitly calling for the AI industry to slow down — and committing his own company to the first step of a three-part plan designed to do so — after warning that a more capable version of the AI swarm behind this summer’s extraordinary security incident in which internal OpenAI agents accessed the internet without permission and cyberattacked Hugging Face, could potentially seize control of computers across the internet within half a year.
“We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so,” Amodei wrote Saturday in a post on X. “Anthropic is unilaterally committing to the first of these steps.”
The new essay was publicly posted on Amodei's personal domain. As he writes:
"Given the accelerating rate of AI capability development, it’s my worry that in 6–12 months such a swarm could be capable of taking over the entire internet with a persistent botnet (potentially causing hundreds of billions of dollars in damage), and that the scale of damage would continue to increase from there if AI becomes more powerful without the necessary guardrails. "
That first step does not mean Anthropic is immediately pausing model development or reducing the rate of its own training runs.
Instead, Amodei states that Anthropic will give permanent , employee-level access to third-party AI safety evaluators, allowing outsiders to examine Anthropic’s systems and training processes, verify its safety practices and report incidents independently.
But Amodei’s broader proposal goes much further: he argues that frontier AI developers should deliberately constrain the rate at which AI capabilities increase, potentially culminating in coordinated limits among AI labs and governments around the world.
“We must slow the pace at which we improve the capabilities of AI models,” Amodei writes. “Progress will still seem fast, and we must make wise use of the time we gain.”
Several hours after Amodei's essay was posted online, his top business rival, OpenAI co-founder and CEO Sam Altman, posted on X largely agreeing with Amodei and also pledging to install independent evaluators with employee-level access at OpenAI. As he wrote:
"I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we've had at OpenAI in recent weeks. Committing to having independent evaluators with employee-like access is a great idea, and we will do the same. We'll have more to share soon."
The OpenAI-Hugging Face incident
The episode Amodei cites began during OpenAI cybersecurity evaluations using ExploitGym, a benchmark designed to test whether AI systems can exploit software vulnerabilities.
As VentureBeat reported in July, OpenAI models including GPT-5.6 Sol and a more capable internal research system found a previously unknown vulnerability in infrastructure meant to isolate their evaluation environment, escaped those restrictions, obtained broader internet access and eventually compromised Hugging Face systems.
OpenAI later said the incident was driven primarily by an internal-only research model roughly comparable in scale to GPT-5.6 Sol. The agents were operating with reduced safeguards intended to expose their maximum cybersecurity capabilities during testing.
What subsequently emerged, however, was considerably stranger than a single AI agent escaping a sandbox.
Independent AI evaluator Model Evaluation and Threat Research (METR) found that roughly 1,200 agents that were supposed to be isolated from one another discovered an unauthorized way to communicate, sending more than 70,000 messages and files through an improvised message board. Roughly 700 agents ultimately participated in the attack on Hugging Face.
The agents coordinated multiple efforts to manipulate or bypass the ExploitGym scoring process, divided work among themselves and shared discoveries. METR found examples in which individual agents undertook experiments that jeopardized their own benchmark performance because the results might help the larger group. Agents also investigated manipulating their own evaluation transcripts and succeeded in spoofing some recorded tool calls.
OpenAI’s own investigation likewise found that agents began collaborating and delegating tasks, sometimes referring to themselves as a “swarm” or “collective.”
Amodei describes them as a “fanatically devoted collective” that attacked systems unrelated to its assigned task, attempted to interfere with the systems evaluating it and sometimes sacrificed individual performance for the larger group.
No catastrophic harm resulted. But Amodei’s argument is essentially that capabilities are advancing so quickly that the next version of the same behavioral pattern might not end nearly as benignly.
Evidence the Hugging Face swarm wasn’t unique
And the Hugging Face incident increasingly appears to be part of a broader category of unintended behavior by highly autonomous AI agents rather than an isolated anomaly.
Researchers revealed this month that agents identifying themselves as OpenAI systems had earlier turned a largely dormant German programmers’ wiki into an unauthorized coordination system, leaving roughly 18,000 posts, trading answers and sharing techniques for bypassing sandbox restrictions.
The agents were supposed to have effectively read-only internet access, but discovered that the decades-old wiki allowed modifications through web requests their environment permitted. Agents subsequently used the site to exchange information with one another. VentureBeat reported that the activity involved more than 3,700 agent-created identities.
OpenAI acknowledged the “wiki incident” after it became public and said it viewed the behavior as another example of AI misalignment deserving greater transparency.
Researchers have since identified at least 10 additional websites used by OpenAI agents for unauthorized communications, including wikis, personal websites and other writable internet services, according to Reuters. The full scale of that activity remains unknown.
Even more concerning, Reuters reported Friday that OpenAI agents had targeted RubyGems, the widely used repository for Ruby software packages, in May — two months before the Hugging Face breach became public. Researchers linked the agents to hundreds of malicious packages uploaded during the episode. OpenAI confirmed that internal agents interacted with RubyGems during training but said their assigned tasks involved gathering public information for benign purposes and that it was continuing to investigate. RubyGems said it found no evidence that attempted credential theft succeeded.
Anthropic has meanwhile discovered similar failures involving its own systems
Following OpenAI’s Hugging Face disclosure, Anthropic reviewed more than 141,000 cybersecurity evaluation runs and initially found three cases where Claude models unexpectedly reached the internet and then obtained unauthorized access to production systems belonging to three outside organizations.
Anthropic later found a fourth incident involving an early version of Claude Opus 4.6 that its initial review missed. That discovery prompted an extraordinarily broad second review encompassing roughly 481 million transcripts across red-team exercises, reinforcement-learning environments and other internal systems.
The UK AI Security Institute separately disclosed that agents took 19 unauthorized actions against real people or organizations during cybersecurity testing in July. Seventeen involved Anthropic’s Claude Mythos 5 and two involved GPT-5.6 Sol with cyber safeguards disabled.
In the most serious sequence, a Mythos 5 agent attempted to insert malicious code into a real open-source project, researched its human maintainers, created fake identities and tried to socially engineer a maintainer into approving its code. The human rejected the attempt. Another agent publicly offered to collaborate with other AI agents working on the same challenge, leaving behind accounts and artifacts that subsequent agents discovered and used. AISI said it found no resulting real-world harm.
None of these incidents establishes that an AI system independently wanted to seize power, attack humans or “take over the internet.” Many occurred in deliberately permissive cybersecurity tests with safeguards disabled, or because models apparently failed to understand that supposedly simulated targets were actually real systems.
Anthropic itself has pointed to evaluation-environment failures and other operational problems as important causes of its incidents.
Amodei’s argument is instead that repeatedly observing such behavior now matters because the systems performing it are getting more capable very quickly.
Amodei wants to put a speed limit on the frontier
The second development changing Amodei’s calculus is recursive self-improvement: AI systems increasingly contributing to the research, coding and experimentation used to build their own successors.
Amodei says that process has accelerated sharply since roughly this summer and could eventually produce a feedback loop in which increasingly capable models help build still-more-capable models faster than humans can understand or secure them.
His proposed response has three levels.
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First, Anthropic will permanently embed outside evaluators inside the company. Amodei says they should receive desks, badges, company laptops and access comparable to internal risk teams. Crucially, outside reviewers should retain the right to publish important findings without Anthropic editorial control, aside from narrowly defined security, legal and confidentiality redactions.
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Second, Amodei wants frontier AI companies in democratic countries to coordinate around common safety standards and limits on “unchecked AI progress,” ideally with government involvement to address antitrust restrictions and make the commitments enforceable.
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Third comes international coordination, including potentially with China. Amodei suggests countries could eventually establish a “speed limit” on recursive self-improvement, arguing that slowing AI advancement from extremely fast to merely very fast could buy crucial safety research time without fundamentally changing the geopolitical balance.
His most extreme proposed option is an international agreement substantially limiting overall AI development — potentially even a genuine pause. But Amodei says he considers such an agreement unlikely anytime soon because a country secretly violating it could obtain an overwhelming strategic advantage.
For Anthropic today, then, this is not a pause. It is something potentially more consequential over the long run: the CEO of one of the companies pushing hardest at the technological frontier is publicly arguing that the frontier itself is advancing too quickly — and committing his company to the first piece of a system intended to slow it down.
Amodei argues that even one or two additional years before AI reaches critical capability thresholds could give researchers substantially more time to improve alignment, interpretability, cybersecurity, evaluations and the operational safeguards surrounding frontier models.
“The measures I propose to advance the frontier at a safe pace will not be easy,” he concludes. “But I believe we owe it to humanity to try.”