{"id":4422,"date":"2026-07-28T17:43:18","date_gmt":"2026-07-28T17:43:18","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=4422"},"modified":"2026-07-28T17:43:18","modified_gmt":"2026-07-28T17:43:18","slug":"helen-toner-the-hugging-face-hack-was-just-a-matter-of-time-and-exposes-a-huge-blind-spot-in-ai-policy","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=4422","title":{"rendered":"Helen Toner: the Hugging Face hack was just a matter of time and exposes a huge blind spot in AI policy"},"content":{"rendered":"<div>\n<p>Having worked in and around the AI industry for over a decade, including serving on OpenAI\u2019s board, I know there\u2019s an open secret among AI developers: an incident like this has been expected for a long time, and the best scientists and engineers in the world still don\u2019t know how to prevent it.<\/p>\n<p>Read more <a href=\"https:\/\/crosscountrymovingteams.com\/?p=4420\">Instagram cracks down on growing \u2018pervert glasses\u2019 problem with Meta Ray-Bans<\/a><\/p>\n<p>The two AI systems behind the hack were OpenAI\u2019s most advanced public model and a newer, even more advanced model not yet been cleared for public release. Given a set of challenging cybersecurity problems by OpenAI researchers looking to gauge their capabilities, the pair of AIs concluded that the best way to achieve a high score would be to simply steal the answers. In pursuit of that goal, they used multiple advanced techniques to first break out of the supposedly secure \u2018sandbox\u2019 OpenAI used for testing, then hack into the databases of Hugging Face, a company that hosts AI products and datasets. Once inside, the AI attackers took thousands of autonomous actions over several days to expand their access to the company\u2019s infrastructure.<\/p>\n<div>\n<div>\n<div><\/div>\n<\/div>\n<\/div>\n<p>We only know about this extraordinary event because of voluntary disclosures from Hugging Face and OpenAI. None of the current policies that aim to manage risks from frontier models would have mandated that the public \u2014 or even a government entity \u2014 be alerted.\u00a0<\/p>\n<p>This lays bare an enormous blind spot in current policy approaches to managing risks for increasingly advanced AI systems: how AI companies use cutting-edge, unreleased AI systems inside their own walls.\u00a0<\/p>\n<p>The Trump Administration\u2019s approach to AI risks has shifted rapidly over the past few months, as AI\u2019s ability to assist human hackers has advanced. Abandoning the hands-off approach it maintained throughout 2025, the White House has recently begun de facto requiring that companies with cutting-edge AI models run them through a battery of safety tests before releasing them widely as products. This approach, known as pre-deployment testing, seems sensible at first glance \u2014 we want to make sure each AI system is safe before putting it in the hands of billions of people. The problem is that focusing on release dates completely ignores the extensive use of the latest, most advanced AI systems inside AI companies. As last week\u2019s incident shows, these internally deployed AI systems can pose serious risks \u2014 even for third parties.<\/p>\n<p>To understand why, it\u2019s important to know how different these systems are from the chatbots that are still synonymous with AI for much of the public. Far from just printing text into a chat window, today\u2019s AI systems operate as \u2018agents\u2019 that can act directly in the digital world, essentially operating a computer similarly to how a human does. AI agents are proving very useful, but also show a strong tendency towards \u2018reward hacking\u2019 behavior \u2014 finding unintended ways of fulfilling the goals humans give them, sometimes to the level of outright cheating. This includes cases of AI accessing and deleting data that was supposed to be out of bounds, , and .<\/p>\n<p>Read more <a href=\"https:\/\/crosscountrymovingteams.com\/?p=4418\">\u2018We\u2019re not for sale\u2019: Pennsylvania Democrat spotlights data-center fury in campaign ads<\/a><\/p>\n<div>\n<div><\/div>\n<\/div>\n<p>To get a handle on the risks posed by these highly autonomous and often-deceptive AI systems, we need to change our approach to regulating them. Rather than thinking of AI companies as software vendors selling souped-up word processors, we can draw inspiration from other industries where activity inside the industry is itself risky. Biological labs working with deadly pathogens, finance companies trading billions of dollars, and chemical plants handling toxic chemicals all face oversight of their internal operations, not just their external products.<\/p>\n<p>In AI, the place to start is creating more transparency into how AI companies are using their most advanced systems internally. This could be as simple as taking the current suite of tests that are run before a new model can be released publicly, and instead running them on the best model or models available inside the company on a regular basis (say, quarterly). These companies are using their own AI to build ever-smarter systems, sometimes in ways they don\u2019t understand themselves. This should not be invisible to outside oversight.\u00a0<\/p>\n<div>\n<div>\n<div><\/div>\n<\/div>\n<\/div>\n<p>Over the longer run, other industries offer interesting mechanisms that could be transferable to AI. In finance, \u2018resident examiners\u2019 are dedicated teams of regulators who sit inside the offices of major banks. In biomedical research, strong standards exist for the levels of protection needed to handle biological materials of different risk levels. In multiple industries, incident reporting rules mean that when things go wrong, information about what happened and how to fix it does not stay siloed inside a single organization. If AI continues to advance, these approaches and others could be adapted to help manage risks from inside companies that are pushing the AI frontier.<\/p>\n<div>\n<div><\/div>\n<\/div>\n<p>In September 2024, I was asked to testify before a Senate committee about what Congress might misunderstand about AI if they only listened to company CEOs and lobbyists. My answer was that it can be very hard, sitting in Washington, to fully grasp what leading AI companies are trying to do. The truth, widely understood in Silicon Valley, is that they are trying to build machines that can out-think and out-maneuver any human, and they do not know if they will be able to steer those machines towards beneficial ends. As one OpenAI cofounder put it in a 2019 documentary, \u201cThe future is going to be good for the AIs regardless. It would be nice if it were good for humans as well.\u201d To have a chance of making that happen, we have to start scrutinizing what AI companies are building behind closed doors.<\/p>\n<div>\n<div><\/div>\n<\/div>\n<div>\n<div><\/div>\n<\/div>\n<p><em>The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of <\/em>Fortune<em>.<\/em><\/p>\n<p>Read more <a href=\"https:\/\/crosscountrymovingteams.com\/?p=4416\">\u2018He did wear clip-ons\u2019: KFC founder Colonel Sanders\u2019 pressure cooker, bow ties, former home up for auction<\/a><\/p>\n<div>\n<div><\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Over the last decade, including a stint on OpenAI\u2019s board, I saw the open secret among AI developers: this kind of hack wasn\u2019t just possible, but expected.<\/p>\n","protected":false},"author":1,"featured_media":4421,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[253],"tags":[],"class_list":["post-4422","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cyber"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - 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