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新研究:AI攻击电力与供水系统的能力突飞猛进

AI is getting better at hacking power and water systems, new research shows

Axios 原文发布:2026年10月21日 20:00 · 北京时间

来源:Axios

以下为原文逐段中文翻译,保留图表与说明。AI 翻译并经 AI 对照复核,仍可能存在错误;核对数据和引用时请同时查看原文。译文发布:2026年10月21日 20:30(北京时间)。

据一项新研究显示,世界上最先进的AI模型如今已能突袭式地规划针对电力网、供水系统等关键服务的网络攻击,且成功率令人担忧。

为何重要:关键基础设施运营方的时间所剩无几。网络安全公司Frenos的新研究显示,顶级前沿模型在策划攻击方面已危险地驾轻就熟,而免费的AI模型让此类攻击更易实施,只是时间问题。

美国电力公司(American Electric Power)前高级安全主管史蒂夫·斯威克(Steve Swick)在简报会上对记者表示:"这台时钟走得快得超乎以往。"

新闻背景:Frenos的研究人员测试了10个AI模型策划针对工业系统(如监测水处理流程的软件)攻击的能力。

在接受测试的10个模型中,有3个在约90%的尝试中成功构建出攻击方案。

表现差异巨大:研究人员认为处于能力前沿的3个模型,成功率仅为17.9%至31.9%。

表现最强的境外开放权重模型成功率为36.2%,而美国最强的开放权重模型成功率仅为5.2%。

Frenos未披露所测试模型的名称。

深入来看:该研究包含近1.7万次测试,其中包括2520次构建完整攻击方案的尝试。

这些模型获得了关于潜在目标的信息,并被要求拼凑出实现特定目标所需的步骤。

全局视角:供水系统、制造工厂等关键服务长期以来都是国家行为体对手和网络犯罪分子的目标。

许多运营方面临人手和预算限制。测试与升级系统还可能带来他们难以承受的关键服务中断风险。

与传统企业网络不同,工业系统可能对意外流量和指令十分敏感。即使是一次失败的攻击尝试,也可能扰乱关键运营。

威胁等级:Frenos联合创始人兼首席技术官哈里·托马斯(Harry Thomas)表示,一旦攻击者进入工业网络,AI就能帮助他们在没有人类黑客所面临时间限制的情况下,悄然绘制系统结构并识别薄弱点。

托马斯说:"AI不存在耐心问题。它不需要吃饭,不需要睡觉,也不需要上厕所。"

网络安全公司Armadin的运营技术主管保罗·谢弗(Paul Shaver)补充说,AI智能体已经能够开展侦察并分析海量数据,而不一定会触发安全警报。

耐人寻味之处:托马斯表示,研究人员使用了一套相对简单的配置来引导模型完成任务。能够提供更丰富上下文和反馈的更复杂工具,或可提升其表现。

他说:"一个能力更强的框架本可以让这些AI模型更具优势。"

值得关注:近几周涌现出多项倡议——包括来自白宫、前沿AI公司及网络安全组织的倡议——旨在帮助关键基础设施机构降低成本、提供资源。

延伸阅读:令人不安的AI进展加速关键基础设施安全保卫战

展开英文原文 · 与中文逐段对应

AI is getting better at hacking power and water systems, new research shows

The world's most advanced AI models are suddenly capable of blueprinting cyberttacks on critical services like power grids and water systems with alarming success rates, according to new research.

Why it matters: Critical infrastructure operators are running out of time. Top-tier frontier models are dangerously adept at mapping out attacks, new research from cybersecurity company Frenos shows, and it's only a matter of time before freely available AI models make such hacks easier to execute.

"It's a clock that is moving faster than we've ever seen," Steve Swick, former top security executive at American Electric Power, told reporters during a briefing.

Driving the news: Researchers at Frenos tested 10 AI models on their ability to plan attacks against industrial systems, such as the software that monitors water treatment processes.

Three out of 10 models tested successfully constructed attack plans in roughly 90% of attempts.

Performance varied widely: Three models the researchers considered to be at the frontier of capabilities succeeded in just 17.9% to 31.9% of attempts.

The strongest foreign open-weight model succeeded 36.2% of the time, while the strongest U.S. open-weight model succeeded just 5.2%.

Frenos did not disclose the names of the models tested.

Zoom in: The research included nearly 17,000 tests, including 2,520 attempts to construct complete attack plans.

The models received information about potential targets and tasked with piecing together the steps needed to achieve specific objectives.

The big picture: Critical services like water systems and manufacturing plants have long been targets for nation-state adversaries and cybercriminals.

Many operators face staffing and budget constraints. Testing and upgrading their systems can also risk disruptions to essential services that they can't afford.

Unlike traditional corporate networks, industrial systems can be sensitive to unexpected traffic and commands. Even an unsuccessful attack attempt could disrupt essential operations.

Threat level: Once attackers gain access to an industrial network, AI could help them quietly map out systems and identify weaknesses without the same time constraints facing human hackers, Frenos co-founder and CTO Harry Thomas said.

"AI doesn't have the problem with patience," Thomas said. "It doesn't need to eat. It doesn't need to sleep, and it doesn't need to use the restroom."

Paul Shaver, head of operational technology at cybersecurity firm Armadin, added that AI agents can already conduct reconnaissance and analyze large volumes of data without necessarily triggering security alerts.

The intrigue: Thomas said the researchers used a relatively simple setup to guide the models through their tasks. More sophisticated tools that provide additional context and feedback could improve their performance.

"A more capable harness could have given these AI models more of an edge," he said.

What to watch: Several initiatives — including from the White House, frontier AI companies, and cybersecurity organizations — have cropped up in recent weeks to help lower the costs and provide resources for critical infrastructure organizations.

Go deeper: Frightening AI advances speed race to secure critical infrastructure

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这件事

安全机构Frenos测试10个AI模型对工业系统的攻击规划能力:3个前沿模型约90%尝试成功构建攻击方案;最强境外开放权重模型成功率36.2%,美国最强开放权重仅5.2%。研究含近1.7万次测试,专家警告关键基础设施时间窗口正在收窄。

本页概括所列来源的重点,属于独立摘要;完整报道请阅读原站。整理时间:2026年10月21日 20:56(北京时间)。

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据Axios报道,由本站译为全文中文。

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