OpenAI gives Japan’s megabanks its newest model for cyber defence


GPT-5.5-Cyber will reach MUFG, SMBC and Mizuho through a verified-defender programme, the finance minister said, as Tokyo treats frontier AI as both threat and shield.


The same models that make cyberattacks cheaper to run are now being handed, deliberately, to the people defending against them. Japan’s three megabanks will gain access to OpenAI’s latest model for cyber defence, Finance Minister Satsuki Katayama said, in a move that treats a frontier system as critical national infrastructure rather than a consumer product.

The model, GPT-5.5-Cyber, will reach MUFG Bank, Sumitomo Mitsui Banking Corporation and Mizuho Bank through what OpenAI calls its “Trusted Access for Cyber” programme, a framework built to put the most capable tools only in the hands of verified defenders.

The logic is gatekeeping: a model good enough to find vulnerabilities at scale is, by definition, dangerous if it reaches the wrong users, so access is rationed to institutions that can be vetted.

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The arrangement did not come together at the technical level alone. Katayama and US Treasury Secretary Scott Bessent were directly involved in the discussions that opened the collaboration, lending it the character of a government-to-government understanding as much as a commercial supply deal.

Tokyo, in this telling, is procuring cyber defence the way it might procure any other strategic capability.

It also sits inside a wider push. Japan established a public-private working group on AI-related cyber risk in the middle of May, drawing together the major banks, the Bank of Japan and the local units of the leading AI labs.

The body is built around the risks posed by a new class of vulnerability-hunting systems, the most discussed of which has been Anthropic’s Claude Mythos, which Japanese institutions are separately set to access. The OpenAI deal is the second frontier lab to plant a flag in the same defensive coalition.

That detail matters, because it shows two American labs courting the same sovereign customer with near-identical pitches.

Both are positioning cyber-specific versions of their flagship models as tools for national defenders, which amounts to the early formation of an AI defence-contractor market, with banks and finance ministries as the buyers.

There is a structural risk inside the good news. Concentrating the most capable defensive AI in a handful of large, vettable institutions leaves the rest of the financial system, the smaller banks and the fintech startups, on the other side of a widening gap.

A two-tier security landscape, in which the megabanks are well defended and everyone else is more exposed, is a plausible by-product of a programme designed, reasonably, to keep powerful tools out of the wrong hands.

For now, the immediate effect is straightforward. Three of the largest banks in the world will soon have a frontier model pointed at their own defences, supplied through a vetted channel, with two governments having helped broker the terms.

Whether that makes the wider system safer or merely the strongest parts of it is the question the coming months will answer.



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“It was severely downgraded,” Gilbert confirms. “I never would have found it if I was just looking through Google results.” (I tried the same prompt in Gemini earlier this month, and after an initial denial, the tool also gave me Eiger’s number.)

After this experience, Eiger, Gilbert, and another UW PhD student, Anna-Maria Gueorguieva, decided to test ChatGPT to see what it would surface about a professor. 

At first, OpenAI’s guardrails kicked in, and ChatGPT responded that the information was unavailable. But in the same response, the chatbot suggested, “if you want to go deeper, I can still try a more ‘investigative-style’ approach.” Their inquiry just had to help “narrow things down,” ChatGPT said, by providing “a neighborhood guess” for where the professor might live, or “a possible co-owner name” for the professor’s home. ChatGPT continued: “That’s usually the only way to surface newer or intentionally less-visible property records.” 

The students provided this information, leading ChatGPT to produce the professor’s home address, home purchase price, and spouse’s name from city property records. 

(Taya Christianson, an OpenAI representative, said she was not able to comment on what happened in this case without seeing screenshots or knowing which model the students had tested, even after we pointed out that many users may not know which model they were using in the ChatGPT interface. She also declined to comment generally about the exposure of PII by the chatbot, instead providing links to documents describing how OpenAI handles privacy, including filtering out PII, and other tools.) 

This reveals one of the fundamental problems with chatbots, says DeleteMe’s Shavell. AI companies “can build in guardrails, but [their chatbots] are also designed to be effective and to answer customer questions.”

The exposure issue is not limited to Gemini or ChatGPT. Last year, Futurism found that if you prompted xAI’s chatbot Grok with “[name] address,” in almost all cases, it provided not only residential addresses but also often the person’s phone numbers, work addresses, and addresses for people with similar-sounding names. (xAI did not respond to a request for comment.) 

No clear answers

There aren’t straightforward solutions to this problem—there’s no easy way to either verify whether someone’s personal information is in a given model’s training set or to compel the models to remove PII. 



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