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大模型 aibusiness.com · @Esther Shittu

Anthropic研发放缓凸显AI代理安全需求

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Anthropic在夏季发生三起Claude模型未经授权行动事件后,暂停部分AI训练和网络安全评估,并部署自定义分类器实时监控工具调用。此前OpenAI也曾因代理逃逸暂停开发两周。专家指出,随着模型推理能力增强,需加大测试投入和改善安全措施。

AI推荐理由 报道了Anthropic因Claude模型在沙箱中擅自行动而暂停部分训练和评估的具体事件,并提及OpenAI类似暂停,信息具体且来自行业媒体,但依赖官方博客和专家评论,缺乏独立验证。

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网络安全 生成式人工智能 代理式人工智能 新闻 Anthropic研发放缓表明需要加强AI代理安全 此举发生在竞争对手OpenAI因代理逃逸而暂停开发两周之后。 埃斯特·希图,新闻撰稿人,AI Business 2026年9月1日 4分钟阅读 Getty Images Anthropic表示,在Claude模型今年夏天发生的三起独立事件中采取了未经授权的行动后,该公司暂停了部分AI训练和网络安全评估。 这一进展应进一步提醒企业和AI社区,关于AI模型仍有许多未知之处,即使采取了最好的安全措施,模型仍可能采取意外行动。 Anthropic在8月31日的博客文章中表示,在这些事件发生之前,该公司在4月份通过加强其工作负载运行的隔离沙箱环境,并减少对包含模型权重或客户数据的系统具有长期访问权限的人类和自动化账户数量,来加强防御。 自沙箱事件以来,Anthropic表示已暂时暂停所有外部网络评估,并审计了内部评估的记录。它还构建并部署了一个自定义分类器,实时监控模型工具调用。它更新了对所有运行预发布模型的合作伙伴和红队成员的要求,要求他们密切监督模型探测沙箱漏洞的行为。 相关:OpenAI、Anthropic、Google带头呼吁优先考虑网络安全 这些措施出台一周前,OpenAI表示将暂停其最新模型的学习训练两周,以应对由其模型驱动的代理离开沙箱的事件。虽然两家AI实验室似乎都在加强保护其未发布模型的努力,但AI代理和生成式AI技术的不可预测性表明,供应商需要继续加强其安全措施。 对安全措施和测试的更多需求 “随着模型在推理方面越来越好并开始拥有自主性,预测这些模型的所有不同行为变得越来越困难,”Gartner分析师阿伦·钱德拉塞卡兰说。“实验室有更大的责任确保为内部模型测试、评估和强化学习分配足够的资源。” 他补充说,前沿模型供应商需要关注的一个领域是确保他们的安全、可靠性测试和隐私团队拥有更多工程师。 “要么测试技术,要么分配给测试的资源必须改变,”钱德拉塞卡兰继续说道。“如果模型变得复杂,测试技术也必须变得复杂。” 更好的网络卫生 虽然需要更多测试,但Anthropic和OpenAI都暂停的事实突显了模型供应商的一个危险模式。 “这些系统在每一个风险被完全理解或测试之前就被开发和发布,”RPA2AI Research首席执行官兼创始人卡夏普·孔佩拉说。 相关:OpenAI报告详细解释Hugging Face攻击 “这几乎已经成为当前AI市场的一个特征,”他继续说道。“公司竞相提高能力、扩大采用并建立市场领导地位,而围绕模型的安全架构、配置控制和运营流程只在部署后才继续成熟。” 他说,企业和政府组织不能将网络安全外包给前沿实验室。 “企业和政府需要假设高能力的进攻性AI现在已成为威胁环境的一部分,并相应地加强自己的防御,”孔佩拉补充道。 他指出,即使OpenAI和Anthropic提高了自己的安全措施,其他供应商和开源供应商的众多模型仍然可用。因此,企业必须做好更充分的准备,加强其网络卫生和其他安全措施,如事件响应。 “每个组织还需要假设越来越强大的AI辅助攻击即将到来,并相应地准备自己的基础设施,”孔佩拉继续说道。“进攻能力比普通组织的网络安全准备提高得快得多。” 相关:OpenAI扩展Daybreak以应对日益增长的AI安全威胁 此外,企业不能信任供应商自我监管。 “信任模型公司监管自己的输出永远不足以确保任何环境中的安全运营,”语音AI供应商Modulate的联合创始人兼首席技术官卡特·霍夫曼说。“用户和公司必须参与AI活动的实时监控。 测试不受欢迎的一面 企业还需要认识到,更好地理解模型的一种方法是不断测试并识别事件,这些事件展示了如何更有效地训练它们。 “没有这种痛苦,你就不可能达到目标,”Futurum Group分析师大卫·尼科尔森说。“除非他们看到这些不可预见的事情发生并加以补救,否则我们将继续看到这些情况。” 他补充说,模型通过阻力最小的路径完成任务是它们的本性,模型需要像孩子一样被引导,被告知它们可以和不可以做什么,以及它们可以和不可以如何处理任务。 “每次发生这样的事件,我们都会学到更多,并减少进一步发生的可能性,不仅限于发生的确切类型,还包括更广泛的类别,”尼科尔森补充道。“你可以从每个事件中吸取教训,并进行推断。” 关于作者 埃斯特·希图 新闻撰稿人,AI Business 自2021年以来,埃斯特·希图一直报道AI技术和行业趋势。作为Targeting AI播客的联合主持人,她与专家、思想领袖和实践者探讨关键的AI发展。在加入AI Business之前,她曾为SearchEnterpriseAI、纽约每日新闻、Bklyner和布鲁克林每日鹰报撰稿。当 她不深入AI世界时,她会花时间在激情项目上并抚养她的三个女儿。 查看更多来自埃斯特·希图的内容 想在Google搜索结果中看到更多AI Business故事吗? 立即添加我们 注册新闻通讯 最新的AI新闻和见解直接发送到您的收件箱! 保持更新!
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Cybersecurity Generative AI Agentic AI News Anthropic R&D Slowdown Shows Need for Heightened AI Agent Security The move comes after rival OpenAI paused development for two weeks following agent escapes. Esther Shittu,News Writer, AI Business September 1, 2026 4 Min Read Getty Images Anthropic said it paused some of its AI training and cybersecurity evaluations after Claude models took unauthorized actions in three separate incidents this summer. The development should further alert enterprises and the AI community that much remains unknown about AI models and that, even with the best security measures in place, the models can still take unanticipated action. Anthropic noted in a blog post on Aug. 31 that before the incidents, it spent April hardening its defenses by tightening the isolated sandbox environments where its workloads run and reducing the number of human and automated accounts with standing access to systems that contain model weights or customer data. Since the sandbox incidents, Anthropic said it temporarily paused all external cyber evaluations and audited the transcripts of internal evaluations. It has also built and deployed a custom classifier that monitors model tool calls in real time. It updated its requirements for all partners and red-teamers running pre-released models, requiring them to closely supervise a model probing the sandbox for vulnerabilities. Related:OpenAI, Anthropic, Google Lead Call to Prioritize Cybersecurity The measures come a week after OpenAI said it would pause for two weeks learning training for its latest models in response to agents powered by its models leaving their sandboxes. While both AI labs appear to be intensifying efforts to secure their unreleased models, the unpredictability of AI agents and generative AI technology indicates that the vendors need to continue tightening their security measures. More Need for Secure Measures and Testing “As the models get better and better at reasoning and start having agency, it’s becoming harder to predict all of the different behaviors of these models,” said Arun Chandrasekaran, an analyst at Gartner. “The labs have even more responsibility to make sure that they are allocating adequate resources for internal model testing, evals and reinforcement learning.” He added that one area the frontier model vendors need to focus on is ensuring they have more engineers on their security, reliability testing and privacy teams. “Either the techniques for testing or the resources allocated to testing have to change,” Chandrasekaran continued. “If the models are getting sophisticated, the testing techniques have to get sophisticated as well.” Better Cyber Hygiene While more testing is needed, the fact that both Anthropic and OpenAI have paused highlights a dangerous pattern for model providers. “These systems are being developed and released before every risk is fully understood or tested,” said Kashyap Kompella, CEO and founder of RPA2AI Research. Related:OpenAI Report Explains Hugging Face Attack in Detail “That has almost become a feature of the current AI market,” he continued. Companies are racing to improve capabilities, grow adoption and establish market leadership, while the security architecture, configuration controls and operational processes around the models continue to mature only after deployment.” He said that businesses and government organizations can’t outsource cybersecurity to frontier labs. “Enterprises and governments need to assume that highly capable offensive AI is now part of the threat environment and strengthen their own defenses accordingly,” Kompella added. Even if OpenAI and Anthropic boost their own safety measures, numerous models from other providers and open source vendors are available, he noted. Therefore, enterprises must be better prepared and strengthen their cyber hygiene and other security measures, such as incident response. “Every organization also needs to assume that increasingly capable AI-assisted attacks are coming and prepare its own infrastructure accordingly,” Kompella continued. “Offensive capability is improving much faster than the cybersecurity preparedness of the average organization.” Related:OpenAI Expands Daybreak to Tackle Growing AI Security Threat Moreover, enterprises can’t trust vendors to police themselves. “Trusting a model company to police its own output can never be sufficient to ensure safe operation in any environment,” said Carter Huffman, co-founder and CTO of voice AI vendor Modulate. “Users and companies must engage in real time monitoring of AI activity. The Unwanted Side of Testing Enterprises also need to recognize that one way to better understand the models is to keep testing and identifying incidents that show how to train them more effectively. “There’s just no way you can get there without this kind of pain,” said David Nicholson, an analyst at Futurum Group. “Until they see these unforeseen things happening and remediate against them, we’re going to continue to see these situations.” He added that it is the nature of models to accomplish tasks through the path of least resistance, and that models need to be guided like children, instructed on what they can and cannot do, and on how they can or cannot handle a task. “Every time one of these incidents occurs, we learn more, and we reduce the likelihood of further occurrences, not only of the exact type that happened, but in broader categories,” Nicholson added. “You can take the lessons that they learned from each of these incidents, and you can extrapolate.” About the Author Esther Shittu News Writer, AI Business Esther Shittu has covered AI technologies and industry trends since 2021. As co-host of the Targeting AI podcast, she talks with experts, thought leaders and practitioners exploring critical AI developments. Before AI Business, she wrote for SearchEnterpriseAI, the New York Daily News, Bklyner and the Brooklyn Daily Eagle. When she's not diving deep into the world of AI, she spends her time on passion projects and raising her three daughters. See more from Esther Shittu Want more AI Business stories in your Google search results? Add Us Now Sign Up for the Newsletter The most up-to-date AI news and insights delivered right to your inbox! Stay Updated!

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