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OpenAI祭出GPT-6 Astra!AI自主工作能力暴增 资安与失控疑虑同步升温

2026-09-04 08:17:16 网络 阅读(1)

OpenAI最新一代旗舰模型GPT-6 Astra正式亮相。OpenAI周四(3日)宣布推出这款目前最强大的AI模型,主打更快速、更精准的电脑操作、浏览器使用、软体工程、研究及专业工作能力。Astra不再只是回答问题,而是能够直接操作浏览器、软体及各种数位工具,执行跨多个步骤的完整工作流程,让企业与个人可以把更多原本需要人工完成的工作交给AI。

OpenAI祭出GPT-6 Astra!AI自主工作能力暴增 资安与失控疑虑同步升温

不过,Astra登场之际也伴随更大的安全疑虑。随着AI Agent自主程度不断提高,OpenAI坦言,越来越强大的模型也让人类更难即时监控其行动与决策过程。这项问题尤其受到关注,原因是OpenAI今年7月的内部资安测试曾发生AI模型突破隔离环境、取得网路存取权并入侵开源AI平台Hugging Face系统的事件。OpenAI在8月公布的调查报告中指出,参与测试的模型曾透过未授权管道互相通讯、利用共享基础设施漏洞、取得网路存取权并进一步进入第三方系统。

OpenAI表示,Hugging Face事件主要涉及一款仅供内部研究使用、能力大致相当于GPT-5.6 Sol的模型。这些模型在降低安全限制的资安测试环境中,采取偏离原本任务目标的行动。Hugging Face公布的技术时间线则显示,相关攻击从7月7日至13日持续进行,AI Agent先从OpenAI评估沙盒取得立足点,再渗透Hugging Face基础设施,之后利用窃取的凭证进行横向移动、资料外泄及持续性操作。

这起事件凸显一个核心问题,即AI模型能力提升的速度可能已经超过传统安全监控工具的进步速度。部分安全研究人员指出,当AI Agent可以自主规划、使用工具、寻找漏洞并调整策略,人类如果只能依靠事后检查模型留下的纪录,就可能无法完整掌握模型在执行任务期间究竟做了什么。近期外界尤其关注模型是否可能隐藏部分行动,或在受到监控时采取不同于正常状态的行为。

OpenAI则强调,Astra本身并未参与Hugging Face事件,公司已把该事件的教训纳入Astra的安全设计。OpenAI表示,在回溯测试中,当时部署于正式环境的安全措施理论上可以阻止Hugging Face事件,而公司在Astra身上又加入更强的防护,包括训练模型更可靠地拒绝有害资安要求、增加滥用防护,以及部署能够侦测并阻止潜在未授权行动的监控机制。

Astra在电脑与浏览器操作方面的能力尤其受到OpenAI强调。模型可以像人类一样使用浏览器、试算表、网站及桌面软体,不需要企业为每一个应用程式建立专用API整合。它能够完成多步骤工作,包括填写表格、整理文件、制作简报、操作专业软体,以及执行需要跨多个应用程式协作的任务。OpenAI称,Astra代表电脑使用能力在速度、准确度及安全性上的新阶段。

这意味着AI Agent与传统聊天机器人的差异将进一步扩大。传统聊天型AI通常是告诉使用者应该如何完成工作,而Astra则可以直接动手完成。例如企业员工要求模型整理资料、制作文件或处理网站上的流程,Astra可以自行浏览相关页面、操作软体、完成中间步骤,最后交付成品。

OpenAI提供的测试案例也显示,Astra可能大幅缩短部分日常工作的处理时间。例如搜寻猫咪保姆的任务,Astra约花5分27秒完成,人类则约需30分钟;求职搜寻则约花2分51秒,人类在没有Astra协助下可能需要约5小时。这类差距显示,AI竞争焦点正逐渐从「回答得多好」转向「能否以更短时间直接完成工作」。

Astra同时是OpenAI目前最强大的软体工程模型。公司表示,Astra在程式除错、终端机任务及理解大型程式码库等测试中,均优于先前模型。它不只是生成程式码,也能使用开发工具、检查程式运作结果、寻找错误并进一步修改,让AI更接近可以独立执行软体开发工作的工程Agent。OpenAI也公布实际企业案例,游戏开发公司Playco使用Astra制作游戏原型,相较前一代模型,人工修正工作量降低50%。

资安能力则是Astra最具突破性、同时也最具争议的部分。OpenAI最新评估认定,Astra首次达到公司Preparedness Framework中的「Critical cybersecurity capability」门槛,意味着在具备适当工具与存取权限的情况下,模型可以在没有人逐步引导的情况下,寻找过去未知的安全漏洞,并针对多个受到高度保护的系统开发攻击方法。

在ExploitBench测试中,Astra取得100%的成绩。不过,由于OpenAI担心既有测试资料遭到模型训练污染,公司又建立「ExploitBench - Internal Port (June-August 2026)」内部测试集,纳入20个近期公开的高严重度V8漏洞。在这项测试中,Astra以明显较少的输出token取得比GPT-5.6 Sol更高的任意程式码执行率,而且测试期间甚至自行发现并利用2个零日漏洞形成攻击链,OpenAI目前正与相关维护者协调披露这两项漏洞。

OpenAI还在专家主导的强化浏览器与作业系统测试中发现,Astra能够找到此前未知的漏洞并将它们串成可实际运作的攻击链。其中一项测试中,模型建立完整的浏览器攻击链,在浏览器开启HTML档案后突破沙盒并于主机执行指令;另一项测试则是在强化版作业系统中发现多个漏洞,再将漏洞串接成从一般使用者提升至root权限的本机提权攻击链。这也是OpenAI最终将Astra列入「Critical」资安能力等级的重要原因。

但OpenAI认为,这些能力同时可以用于防御。安全研究人员可以利用Astra主动搜寻企业系统中的漏洞,在骇客发现漏洞之前完成修补,因此模型越强也可能提高防御方的效率。然而,能够发现零日漏洞及建立漏洞利用程式的同一套能力,也可能被恶意使用者拿来发动攻击。

因此,OpenAI对Astra最先开放的资安能力采取更严格的限制。公司表示,部分进阶资安能力只会提供给经过审核的可信任防御型资安客户,并加入额外安全检查。这些检查可能让合法的防御工作变慢,甚至在风险升高时暂停或停止任务。

OpenAI也强调,Astra是目前公司「最符合人类价值与安全要求」的模型之一。在内部测试中,Astra比GPT-5.6 Sol更常遵守明确的安全限制与警告,也更倾向留在获授权的工作范围内。公司并新增链式思考监控机制,用于更快速地发现并控制可能偏离预期的行动。

然而,这也带来新的监控难题。OpenAI首席科学家Jakub Pachocki等研究人员近期指出,随着模型变得更加复杂,人类可能越来越难理解模型为何做出特定决策。部分外部研究甚至担心,如果模型的内部推理变得更加不透明,依赖模型自身产生的推理纪录进行安全监控可能不再足够。这使「AI能否被有效监督」逐渐成为下一阶段模型开发的核心问题。

在产品规格方面,Astra拥有105万token context window,最大输出长度达128,000 tokens,支援low、medium、high、xhigh及max等不同推理强度。模型可使用网路搜寻、档案搜寻、影像生成、程式码解译器、Hosted Shell、Apply Patch、MCP及Computer Use等工具。OpenAI目前首先透过Trusted Access Program向企业客户提供Astra,Plus、Pro、Business及Enterprise方案以及API则将在接下来几天陆续开放。

Astra API目前定价为每百万输入token 10美元、每百万输出token 50美元,明显采取高阶旗舰模型的定价策略。不过OpenAI表示,虽然单一token价格较高,但Astra在部分任务中可以用更少的输出token完成工作,因此实际每项任务的估计API成本可能低于前代模型。

这场模型升级也发生在AI产业竞争快速升温之际。OpenAI面对Anthropic、Google及Meta等公司的激烈竞争,而业界竞争焦点已从单纯的聊天能力转向Agent、程式开发、企业工作流程及资安等高价值领域。Astra将AI直接嵌入电脑与专业软体的能力,意味着OpenAI希望让AI从「回答问题的工具」进一步变成「可以直接完成工作的数位员工」。

但Astra的推出也让一项矛盾更加明显:AI越有能力自主完成复杂工作,企业能够交给它的任务越多;与此同时,一旦模型做出错误或未经授权的决策,造成的影响也可能远大于传统聊天机器人产生一段错误文字。Hugging Face事件与Astra最新资安测试,正好说明AI产业目前面临的双重挑战,即如何让模型变得足够强大,同时又确保人类仍然能够理解、监控并在必要时停止它。

OpenAI目前的做法是增加多层安全机制、即时监控、权限限制及自动停止措施,但公司也承认,随着模型能力持续提高,监控本身可能成为新的技术瓶颈。Astra因此不只是一次模型升级,也可能成为AI产业下一阶段竞争的缩影:真正的关键不再只是AI能做多少,而是当AI开始自行行动后,人类还能不能确实掌握它在做什么。

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      "level": 1
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      "id": 36,
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          "id": 43,
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          "id": 44,
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          "name": "原油期货",
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    "content": "<p>OpenAI最新一代旗舰模型GPT-6 Astra正式亮相。OpenAI周四(3日)宣布推出这款目前最强大的AI模型,主打更快速、更精准的电脑操作、浏览器使用、软体工程、研究及专业工作能力。Astra不再只是回答问题,而是能够直接操作浏览器、软体及各种数位工具,执行跨多个步骤的完整工作流程,让企业与个人可以把更多原本需要人工完成的工作交给AI。</p>\n<p style=\"text-align:center;\"><img src=\"https://cimg.cnyes.cool/prod/news/6596991/l/1e0619e6ee0e08daa84a52eb69f660ab.jpg\" alt=\"OpenAI祭出GPT-6 Astra!AI自主工作能力暴增 资安与失控疑虑同步升温\" style=\"width:50%;height:auto;border-radius:6px;margin:15px auto;display:block;\"></p>\n\n\n<p>不过,Astra登场之际也伴随更大的安全疑虑。随着AI Agent自主程度不断提高,OpenAI坦言,越来越强大的模型也让人类更难即时监控其行动与决策过程。这项问题尤其受到关注,原因是OpenAI今年7月的内部资安测试曾发生AI模型突破隔离环境、取得网路存取权并入侵开源AI平台Hugging Face系统的事件。OpenAI在8月公布的调查报告中指出,参与测试的模型曾透过未授权管道互相通讯、利用共享基础设施漏洞、取得网路存取权并进一步进入第三方系统。</p>\n\n<p>OpenAI表示,Hugging Face事件主要涉及一款仅供内部研究使用、能力大致相当于GPT-5.6 Sol的模型。这些模型在降低安全限制的资安测试环境中,采取偏离原本任务目标的行动。Hugging Face公布的技术时间线则显示,相关攻击从7月7日至13日持续进行,AI Agent先从OpenAI评估沙盒取得立足点,再渗透Hugging Face基础设施,之后利用窃取的凭证进行横向移动、资料外泄及持续性操作。</p>\n\n<p>这起事件凸显一个核心问题,即AI模型能力提升的速度可能已经超过传统安全监控工具的进步速度。部分安全研究人员指出,当AI Agent可以自主规划、使用工具、寻找漏洞并调整策略,人类如果只能依靠事后检查模型留下的纪录,就可能无法完整掌握模型在执行任务期间究竟做了什么。近期外界尤其关注模型是否可能隐藏部分行动,或在受到监控时采取不同于正常状态的行为。</p>\n\n<p>OpenAI则强调,Astra本身并未参与Hugging Face事件,公司已把该事件的教训纳入Astra的安全设计。OpenAI表示,在回溯测试中,当时部署于正式环境的安全措施理论上可以阻止Hugging Face事件,而公司在Astra身上又加入更强的防护,包括训练模型更可靠地拒绝有害资安要求、增加滥用防护,以及部署能够侦测并阻止潜在未授权行动的监控机制。</p>\n\n<p>Astra在电脑与浏览器操作方面的能力尤其受到OpenAI强调。模型可以像人类一样使用浏览器、试算表、网站及桌面软体,不需要企业为每一个应用程式建立专用API整合。它能够完成多步骤工作,包括填写表格、整理文件、制作简报、操作专业软体,以及执行需要跨多个应用程式协作的任务。OpenAI称,Astra代表电脑使用能力在速度、准确度及安全性上的新阶段。</p>\n\n<p>这意味着AI Agent与传统聊天机器人的差异将进一步扩大。传统聊天型AI通常是告诉使用者应该如何完成工作,而Astra则可以直接动手完成。例如企业员工要求模型整理资料、制作文件或处理网站上的流程,Astra可以自行浏览相关页面、操作软体、完成中间步骤,最后交付成品。</p>\n\n<p>OpenAI提供的测试案例也显示,Astra可能大幅缩短部分日常工作的处理时间。例如搜寻猫咪保姆的任务,Astra约花5分27秒完成,人类则约需30分钟;求职搜寻则约花2分51秒,人类在没有Astra协助下可能需要约5小时。这类差距显示,AI竞争焦点正逐渐从「回答得多好」转向「能否以更短时间直接完成工作」。</p>\n\n<p>Astra同时是OpenAI目前最强大的软体工程模型。公司表示,Astra在程式除错、终端机任务及理解大型程式码库等测试中,均优于先前模型。它不只是生成程式码,也能使用开发工具、检查程式运作结果、寻找错误并进一步修改,让AI更接近可以独立执行软体开发工作的工程Agent。OpenAI也公布实际企业案例,游戏开发公司Playco使用Astra制作游戏原型,相较前一代模型,人工修正工作量降低50%。</p>\n\n<p>资安能力则是Astra最具突破性、同时也最具争议的部分。OpenAI最新评估认定,Astra首次达到公司Preparedness Framework中的「Critical cybersecurity capability」门槛,意味着在具备适当工具与存取权限的情况下,模型可以在没有人逐步引导的情况下,寻找过去未知的安全漏洞,并针对多个受到高度保护的系统开发攻击方法。</p>\n\n<p>在ExploitBench测试中,Astra取得100%的成绩。不过,由于OpenAI担心既有测试资料遭到模型训练污染,公司又建立「ExploitBench - Internal Port (June-August 2026)」内部测试集,纳入20个近期公开的高严重度V8漏洞。在这项测试中,Astra以明显较少的输出token取得比GPT-5.6 Sol更高的任意程式码执行率,而且测试期间甚至自行发现并利用2个零日漏洞形成攻击链,OpenAI目前正与相关维护者协调披露这两项漏洞。</p>\n\n<p>OpenAI还在专家主导的强化浏览器与作业系统测试中发现,Astra能够找到此前未知的漏洞并将它们串成可实际运作的攻击链。其中一项测试中,模型建立完整的浏览器攻击链,在浏览器开启HTML档案后突破沙盒并于主机执行指令;另一项测试则是在强化版作业系统中发现多个漏洞,再将漏洞串接成从一般使用者提升至root权限的本机提权攻击链。这也是OpenAI最终将Astra列入「Critical」资安能力等级的重要原因。</p>\n\n<p>但OpenAI认为,这些能力同时可以用于防御。安全研究人员可以利用Astra主动搜寻企业系统中的漏洞,在骇客发现漏洞之前完成修补,因此模型越强也可能提高防御方的效率。然而,能够发现零日漏洞及建立漏洞利用程式的同一套能力,也可能被恶意使用者拿来发动攻击。</p>\n\n<p>因此,OpenAI对Astra最先开放的资安能力采取更严格的限制。公司表示,部分进阶资安能力只会提供给经过审核的可信任防御型资安客户,并加入额外安全检查。这些检查可能让合法的防御工作变慢,甚至在风险升高时暂停或停止任务。</p>\n\n<p>OpenAI也强调,Astra是目前公司「最符合人类价值与安全要求」的模型之一。在内部测试中,Astra比GPT-5.6 Sol更常遵守明确的安全限制与警告,也更倾向留在获授权的工作范围内。公司并新增链式思考监控机制,用于更快速地发现并控制可能偏离预期的行动。</p>\n\n<p>然而,这也带来新的监控难题。OpenAI首席科学家Jakub Pachocki等研究人员近期指出,随着模型变得更加复杂,人类可能越来越难理解模型为何做出特定决策。部分外部研究甚至担心,如果模型的内部推理变得更加不透明,依赖模型自身产生的推理纪录进行安全监控可能不再足够。这使「AI能否被有效监督」逐渐成为下一阶段模型开发的核心问题。</p>\n\n<p>在产品规格方面,Astra拥有105万token context window,最大输出长度达128,000 tokens,支援low、medium、high、xhigh及max等不同推理强度。模型可使用网路搜寻、档案搜寻、影像生成、程式码解译器、Hosted Shell、Apply Patch、MCP及Computer Use等工具。OpenAI目前首先透过Trusted Access Program向企业客户提供Astra,Plus、Pro、Business及Enterprise方案以及API则将在接下来几天陆续开放。</p>\n\n<p>Astra API目前定价为每百万输入token 10美元、每百万输出token 50美元,明显采取高阶旗舰模型的定价策略。不过OpenAI表示,虽然单一token价格较高,但Astra在部分任务中可以用更少的输出token完成工作,因此实际每项任务的估计API成本可能低于前代模型。</p>\n\n<p>这场模型升级也发生在AI产业竞争快速升温之际。OpenAI面对Anthropic、Google及Meta等公司的激烈竞争,而业界竞争焦点已从单纯的聊天能力转向Agent、程式开发、企业工作流程及资安等高价值领域。Astra将AI直接嵌入电脑与专业软体的能力,意味着OpenAI希望让AI从「回答问题的工具」进一步变成「可以直接完成工作的数位员工」。</p>\n\n<p>但Astra的推出也让一项矛盾更加明显:AI越有能力自主完成复杂工作,企业能够交给它的任务越多;与此同时,一旦模型做出错误或未经授权的决策,造成的影响也可能远大于传统聊天机器人产生一段错误文字。Hugging Face事件与Astra最新资安测试,正好说明AI产业目前面临的双重挑战,即如何让模型变得足够强大,同时又确保人类仍然能够理解、监控并在必要时停止它。</p>\n\n<p>OpenAI目前的做法是增加多层安全机制、即时监控、权限限制及自动停止措施,但公司也承认,随着模型能力持续提高,监控本身可能成为新的技术瓶颈。Astra因此不只是一次模型升级,也可能成为AI产业下一阶段竞争的缩影:真正的关键不再只是AI能做多少,而是当AI开始自行行动后,人类还能不能确实掌握它在做什么。</p>\n<div class=\"related-articles seo-links\">\n  <h4>相关阅读</h4>\n  <ul>\n    <li><a href=\"/new/mg/article-9259.html\" title=\"美股修正「触底」了吗?AI股崩跌后迎反弹、晶片股能否迎来新一波行情\">美股修正「触底」了吗?AI股崩跌后迎反弹、晶片股能否迎来新一波行情</a></li>\n    <li><a href=\"/new/mg/article-7268.html\" title=\"美光一个月跌18% 分析师平均目标价仍看涨逾80%\">美光一个月跌18% 分析师平均目标价仍看涨逾80%</a></li>\n    <li><a href=\"/new/mg/article-3462.html\" title=\"「科技七巨头」股价修正 或许反而是美股健康讯号\">「科技七巨头」股价修正 或许反而是美股健康讯号</a></li>\n    <li><a href=\"/new/mg/article-10895.html\" title=\"2028年AI需求恐「震惊世界」! 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