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Artificial intelligence
Examining the Mythos
AI hacking tools could eventually favour the defenders of cyber-security—but expect a bumpy ride until then
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TECH FIRMS usually create buzz around products they plan to release. Anthropic, an American artificial-intelligence lab, has managed to create excitement—and a good deal of worry—around something it plans not to. On April 7th the firm announced that a new AI model it had developed, dubbed Mythos, would not be released to the general public. Instead, under an initiative called “Project Glasswing”, whose ten founder members include Apple, Google and Nvidia, access would be strictly controlled.
The problem is not that Mythos is buggy or unreliable. Allegedly, it is that it works so well that releasing it would put the world’s digital infrastructure at risk. According to Anthropic, the model has surpassed “all but the most skilled humans” when it comes to finding and exploiting security holes in everything from popular operating systems to the cryptographic softwarethat secures e-commerce and financial networks. And it can find those vulnerabilities with only the bare minimum of human help. Not to be outdone, a few days later OpenAI, one of Anthropic’s competitors, announced a closed version of its own hacking-friendly model, named GPT-5.4 Cyber.
A world of “vibe hacking”, in which amateurs can use AI models to find flaws in software—and perhaps even write the “exploits” needed to crash them, hold them to ransom or even take control of them remotely—sounds terrifying. Shortly after Anthropic’s announcement Scott Bessent, America’s treasury secretary, hosted a meeting of bank bosses to discuss what AI-enabled hacking might mean for their businesses. Financial regulators in Britain organised a similar meeting a few days later. But security researchers themselves seem guardedly optimistic. “In the medium term I think this will be a mess,” says Bruce Schneier, an American computer-security expert. “But in the long run I think it will actually be good for the defenders.”
Since Anthropic has released only limited information about Mythos, the degree to which the new model really is revolutionary rather than evolutionary is hard to judge (what might politely be termed a “vigorous debate” is raging online). Testing by the AI Security Institute, a British government agency, found that Mythos was neck-and-neck with other models on relatively simple cyber-security tests, but noticeably ahead in a more advanced one that requires a model to complete dozens of steps before successfully taking over a target machine (see chart on next page).
The chief thing Anthropic’s researchers investigated was Mythos’s ability to unearth bugs that hackers could use to attack or gain control of other computers. They looked specifically for bugs that had never been found before (known as “zero-days” in the jargon). Finding those would prove the model was doing novel work, and not simply regurgitating known bugs it had come across in its training data.
Zero-days lurk everywhere, says Jeff Williams, a co-founder of Contrast Security, a software firm, and of the Open Worldwide Application Security Project Foundation, a non-profit dedicated to improving the security of software. Although Mythos is said to have found “thousands” of high- or critical-severity flaws, Anthropic is keeping most secret until they can be fixed. But the firm did reveal details of some, including one in FreeBSD, a widely used operating system, another in FFmpeg, a video-and-audio code library, and a third—which remains unfixed—in software vital to cloud computing.
Many of the bugs reported by Anthropic are, if not simple, then at least comprehensible. They are the sorts of things a human could plausibly have found. They seem to be the sort of thing other AI models could have found, too. In a blog post published shortly after Anthropic’s announcement, Stanislav Fort, a founder of AISLE, an AI-focused cyber-security company, described using several smaller, older models to find the same bug in FreeBSD. Citing his own firm’s experience with AI-powered bug-hunting, Dr Fort reckons the AI cyber-security frontier is “jagged”, with no model having a clear edge.
Everyone agrees that the state of the art is advancing quickly. Until recently AI bug-hunting was prone to generating false positives or trivial results. “One change I’ve noticed in the past couple of months is that a lot of these AI-generated bug reports are increasingly of good quality,” says Mr Schneier. An update in January to OpenSSL, which helps ensure secure connections between websites, fixed a dozen security flaws found by AI models employed by Dr Fort’s firm. In March Anthropic itself announced that an older, pre-Mythos version of Claude had found almost a fifth of all the high-severity bugs fixed in Firefox, a web browser, in 2025.
As the growing power of AI models makes finding bugs easier, says Mr Schneier, the question becomes whether attackers can exploit them more quickly than defenders can fix them. This is where Project Glasswing comes in. Anthropic says it is expanding Glasswing to another 40 digital-infrastructure organisations, so they can use Mythos to harden the software on which the internet depends. Anthropic hopes that giving them access now, before similarly powerful models become widely available, will leave them time to find and fix as many bugs as possible.
All the researchers The Economist spoke to thought that, in the long run, AI-enabled hacking would probably help defenders more than attackers, by allowing companies to more thoroughly check their software before it is published. But there is plenty of short term to worry about. For one thing, AI checking is not cheap: Anthropic says one of the bugs it found cost the AI lab nearly $20,000-worth of tokens to find. For software such as Linux, a family of widely used operating systems which are at least partly maintained by volunteers, that would be a steep price. And much of the code out in the world—running on home routers, smart gadgets like TVs or fridges and industrial machinery—has nobody maintaining it at all. In such cases, attackers could have a field day.
这篇文章写得很精彩出自最新一期《经济学人》今天我们学习两大特点：
1）这篇文章先以 Anthropic 拒绝公开发布超强 AI 黑客模型 Mythos 引出社会恐慌，再通过网络安全专家、测试结果和真实漏洞案例分析 AI 黑客技术的能力与局限，随后讨论其可能在短期内加剧网络风险、长期却有利于防御方的双重影响，整体采用“提出风险—分析争议—举例论证—展望未来”的递进式结构，并以谨慎乐观的态度强调 AI 将重塑网络安全攻防格局。
2）这篇文章有很多词语与用法值得学习，下面是便于快速掌握的列表：
artificial-intelligence lab
Project Glasswing
“玻璃翼项目”：Anthropic 用来严格控制 Mythos 访问权限的计划。
digital infrastructure
数字基础设施：支撑互联网、操作系统、金融网络等数字服务运行的基础系统。
cryptographic software
加密软件：用于保护数据传输和交易安全的软件。
vibe hacking
“氛围式黑客攻击”：指非专业人员借助 AI 模型发现并利用软件漏洞的攻击方式。
exploits
漏洞利用程序：用于触发软件缺陷、使系统崩溃、被勒索或被远程控制的代码或方法。
cyber-security tests
网络安全测试：用于衡量模型发现漏洞、攻击系统或完成安全任务能力的测试。
zero-days
零日漏洞：此前从未被发现、因此还没有补丁修复的安全漏洞。
critical-severity flaws
严重级别漏洞：危害程度很高、可能造成重大安全风险的软件缺陷。
AI-powered bug-hunting
AI 驱动的漏洞搜寻：使用人工智能模型自动或半自动寻找软件安全漏洞的过程。
小测时间到：做一道 SAT 题！看看你的选择：
AI models that can search for software flaws have advanced quickly, and some security researchers expect them ultimately to strengthen cyber-defence by helping organisations find vulnerabilities before attackers do. Yet the author presents this optimism cautiously: because tools such as Mythos may also let less skilled hackers discover or exploit zero-day bugs, and because many systems are expensive or difficult to check and poorly maintained, the near-term consequences of AI-enabled hacking may be chaotic even if the long-term result benefits defenders.
Which choice best describes the author’s attitude toward AI-enabled hacking tools?
A.Deeply indifferent, because the author treats AI-enabled hacking as unrelated to cybersecurity professionals or ordinary users.
B.Entirely dismissive, because the author argues that AI models are too unreliable to find real software vulnerabilities.
C.Strongly enthusiastic, because the author claims that AI-enabled hacking will immediately make digital infrastructure safe.
D.Cautiously optimistic, because the author suggests that such tools may eventually help defenders but could create serious short-term risks.
答案
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D
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