The time buffer upon which the cybersecurity field operates is vanishing. The window between the public disclosure of a software vulnerability and the appearance of a usable attack tool has plummeted from an average of 771 days in 2018 to under four hours today. The emergence of Anthropic's latest AI model, Mythos, is bringing this crisis to the forefront of public attention. Its true warning is not aimed specifically at large banks, but serves as an urgent signal for the vast number of small and medium-sized institutions with weak defenses.

Anthropic has characterized Mythos as "too dangerous to release," a move that quickly triggered a chain reaction. US Treasury Secretary Scott Bessent subsequently convened Wall Street executives to assess system defense preparedness. The Treasury Department is now seeking direct access to the Mythos model. The UK AI Safety Institute, which was granted early access, assessed that Mythos indeed possesses capabilities for launching complex cyber attacks that surpass existing tools like OpenAI's ChatGPT and Google's Gemini.

However, the UK AI Safety Institute also pointed out that Mythos poses the greatest threat to systems with "weak defenses" or simple architectures. Large banks possess world-class IT security systems; those truly exposed to high risk are the much broader population of small and medium-sized enterprises, hospitals, and small retailers. These entities are both traditional targets for hackers and generally lack the resources and capabilities needed for rapid response.

With the rise of agentic AI, the window between a vulnerability's public disclosure and its exploitation has effectively disappeared. This forces the entire industry to confront a fundamental, unanswered question: when vulnerabilities can be weaponized within hours, is the decades-old practice of "responsible disclosure" still reasonable? Are the patch deployment processes that take weeks or even months still practically meaningful?

Large Banks Are Not the Weakest Link Anthropic's method of releasing information about Mythos initially directed attention toward the financial industry. The intervention by Scott Bessent provided the AI company with rare public visibility ahead of a potential IPO, while also raising questions about who would gain exclusive access to Mythos.

The assessment from the UK AI Safety Institute provided some basis for market concerns—Mythos is indeed more adept at launching complex cyber attacks than other AI tools. But the institute also emphasized that its threat is primarily concentrated on poorly defended targets. Large banks have the most robust IT defense systems globally, and hacker groups have historically tended to bypass such targets.

The entities facing the most severe test are the multitude of small and medium-sized institutions lacking adequate defenses. Hackers typically do not attack banks directly but instead scan the internet to identify hospitals for ransomware attacks or small businesses with weak security perimeters. The enhancement of AI capabilities makes the situation for these organizations increasingly precarious.

From 771 Days to Under 4 Hours: AI Compresses the Window Understanding why the rise of AI is so dangerous requires clarifying the previous operating logic of cybersecurity. The tech industry long adhered to the principle of "responsible disclosure": once a software flaw is found, the vendor announces it to the public along with repair advice, giving customers time to apply patches. Microsoft's "Patch Tuesday" is a classic example—a monthly schedule for disclosing security vulnerabilities found in products like Office 365 and Windows.

IT teams at banks like Barclays and Wells Fargo, upon receiving patch advice, must go through compatibility testing, management approval, and final deployment, a process that often takes weeks or even months. Before generative AI, this pace was acceptable because the time hackers needed to research and exploit a disclosed vulnerability was typically longer than the time victims needed to complete their fixes.

But AI has completely altered this equation. Even two years ago, hackers could paste vulnerability disclosure details into ChatGPT and instruct it to scan public code repositories like GitHub to find similar exploitable patterns. For instance, if Microsoft disclosed a flaw in how Office 365 handles a certain file type, a chatbot could not only suggest specific exploitation paths but also quickly identify similar weaknesses in products like Outlook and Teams. According to data from zerodayclock.com, the average time from a vulnerability's public disclosure to the completion of a usable attack tool has been compressed from 771 days in 2018 to under four hours today.

Agentic AI: Vulnerability Exploitation Moves Toward Full Automation The latest leaps in AI capabilities in recent months have ushered the threat into a more dangerous new phase. AI companies are increasingly granting models "agent" capabilities, allowing them to autonomously execute actions, not just provide advice. Anthropic's Claude Cowork, released in January, already possesses the ability to perform independent operations like sending emails and managing calendars.

For cyber attackers, this means AI tools are no longer just assistants for finding vulnerabilities; they can automatically attempt different attack vectors until one works. Mythos goes further, capable of "chaining" multiple vulnerabilities together to form multi-step compound attacks—a capability previously reserved for top-tier human hackers. Using a burglary analogy: finding the first open window, using it to unlock a door from the inside, and then disabling the alarm system; each step alone is insufficient, but chained together they enable full access.

Even before the proliferation of agentic AI, generative AI was quietly reshaping the hacker's toolbox: chatbots were used to optimize phishing emails, making them more deceptive; real-time avatar generators created deepfake video calls that made it difficult for victims to distinguish real from fake. The advent of agentic AI pushes the automation of "the act of hacking itself" further, rather than just serving as an辅助工具.

The Logic of "Responsible Disclosure" is Unraveling The core principle long upheld by the cybersecurity industry is being directly questioned. While Anthropic's disclosure of Mythos helps build a mystique ahead of a potential IPO, it objectively forces the entire industry to confront a long-avoided structural problem: when the time window between vulnerability disclosure and exploitation has effectively vanished, does the underlying logic of the "responsible disclosure" mechanism still hold? Have patch deployment processes that take weeks or months become obsolete?

For large banks, there is at least a potential path forward. Large banks possess sufficient personnel and financial resources to begin exploring and gradually implementing near-real-time patch deployment.

What remains truly unresolved is the predicament of small and medium-sized enterprises that need to move at the same speed but are far from possessing the corresponding capabilities. What they require includes both technical support and regulatory framework intervention—neither of which the market currently provides.