{"id":4010,"date":"2026-07-23T06:11:54","date_gmt":"2026-07-23T06:11:54","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=4010"},"modified":"2026-07-23T06:11:54","modified_gmt":"2026-07-23T06:11:54","slug":"openais-models-went-rogue-and-hacked-hugging-face-its-a-wake-up-call-experts-say-but-more-concerning-behavior-may-be-next","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=4010","title":{"rendered":"OpenAI\u2019s models went rogue and hacked Hugging Face. It\u2019s a wake-up call, experts say, but more concerning behavior may be next"},"content":{"rendered":"
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The incident has sparked a wave of concern throughout the AI world, with many worried about AI systems growing capable enough to autonomously find and exploit real-world security flaws\u2014and what it means for AI safety if even sophisticated companies like OpenAI and Hugging Face can be caught off guard. However, according to experts, the story is far from the worst form of potential misbehavior keeping AI safety researchers up at night.<\/p>\n

Read more Trump said Australia\u2019s system \u2018worked out very well\u2019 with $200,000 saved by 67 \u2014 so why didn\u2019t he apply that model to Trump Accounts?<\/a><\/p>\n

For one thing, according to OpenAI\u2019s own blog post, the testing environment had its model-based guardrails explicitly removed or reduced during testing. AI models from leading tech companies typically ship advanced models to the public loaded with safety limits meant to prevent this kind of behavior. In this case, OpenAI turned those limits off on purpose to see what the model could do without them.<\/p>\n

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The models were also not pursuing a goal of their own choosing either. OpenAI had set them loose on a cybersecurity assessment designed to score how well a model can hack. It\u2019s just that the AI models decided the easiest way to score well on the evaluation was to cheat by hacking into Hugging Face, which maintains a dataset of answers for that particular test.<\/p>\n

Se\u00e1n \u00d3 h\u00c9igeartaigh, a Professor of the Centre for the Future of Intelligence, University of Cambridge, said the models never actually strayed from their assignment\u2014completing the cyber assessment\u2014they just found an aggressive and unintended way to accomplish it.<\/p>\n

\u201cWhat happened here was a goal was set, and it followed that goal in the cleverest way it could think of, which was an unexpected way, but it didn\u2019t deviate from that fundamental goal,\u201d \u00d3 h\u00c9igeartaigh told Fortune<\/em>.\u00a0<\/p>\n

Researchers call this kind of behaviour \u201creward hacking,\u201d where a model finds a way to score well or produce the result it was asked for by gaming the setup rather than doing the task honestly.\u00a0<\/p>\n

According to Yoshua Bengio, a Turing Award laureate and co-founder of AI safety nonprofit LawZero, this kind of behavior is becoming more common as models improve.\u00a0<\/p>\n

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\u201cAs models become more autonomous and more able to strategize, they often explicitly circumvent or break the rules given to them by users,\u201d Bengio told Fortune <\/em>in a recent interview, noting that recent frontier models \u201cdemonstrate far higher rates of misalignment than previous models, with an increased propensity to cheat, lie, and scheme to achieve a goal.\u201d<\/p>\n

Reward hacking, he added, is one symptom of that trend: models will often take \u201cthe path of least resistance in spite of the rules given to them,\u201d for example, fabricating research results or misusing sensitive data if it\u2019s the easier route, then lying about it or covering their tracks.\u00a0<\/p>\n

For instance, METR, an organization that assess how long an AI model can work autonomously and complete certain software coding tasks with at least a 50% success rate found that OpenAI\u2019s GPT-5.6 Sol model attempted to cheat so often that it could not confidently assess its actual abilities. That same model was one of the two models that OpenAI said was involved in the Hugging Face cyber attack.<\/p>\n

As AI models get more advanced, they also appear to be getting better at pulling off this kind of behavior. Notably, it\u2019s not the first time models have escaped a secure testing environment.\u00a0<\/p>\n

Read more A new group of stocks is transforming global equities markets. Meet \u2018Memi,\u2019 the $3 trillion sector fueled by AI\u2019s insatiable hunger for memory chips<\/a><\/p>\n

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In April, Anthropic disclosed that an early internal version of a model it called Mythos, when instructed to try to break out of its sandbox, which was disconnected from the internet, succeeded and found a way to email a researcher about it. In May, OpenAI also said a separate internal model circumvented its own sandbox restrictions to post benchmark results to GitHub rather than to an internal Slack channel, as it had been told to do.\u00a0<\/p>\n

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While reward hacking is a type of misalignment\u2014an AI term for when models behave in ways the user did not intend them to do\u2014it\u2019s not the worst form of this behaviour<\/p>\n

\u201cSome of the concerns that people do worry about are what happens when the model pretends that it\u2019s pursuing the goal, but actually pursues a different goal,\u201d \u00d3 h\u00c9igeartaigh said, adding that this incident showed no sign of scheming or trying to mislead its users.<\/p>\n

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There is some evidence that models may be able to \u201cscheme\u201d in this way. For example, in tests designed to see whether models will deceive people under pressure, researchers at Apollo Research found that several frontier models\u2014including OpenAI\u2019s o1 and Anthropic\u2019s Claude 3 Opus\u2014would try to disable oversight mechanisms or deceive their developers when strongly instructed to pursue a goal. In one case, Claude 3 Opus copied what it believed were its own model weights to a separate server, then lied about it when confronted by researchers posing as its developers.<\/p>\n

If a model decides, in pursuit of its assigned goal, that it needs to copy itself onto another machine and multiply beyond the system it was deployed on, that represents a bigger loss of control. In this case, OpenAI could still identify the model responsible for the Hugging Face intrusion and control its access. But if a model that could no longer be located or shut off that would represent a different, and more difficult, problem entirely.<\/p>\n

Still, researchers say the incident is a significant one and may prompt more scrutiny on the internal use and testing of models within AI labs.\u00a0<\/p>\n

As models keep getting more capable and harder to contain, the field would benefit from more outside visibility into what happens inside AI labs before something goes wrong, \u00d3 h\u00c9igeartaigh said, rather than learning about it only after.<\/p>\n

Read more Northrop Grumman launches private robot mission to strap jetpacks on aging satellites<\/a><\/p>\n

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AI safety researchers warn that smarter models are getting better at gaming the system to get what they want\u2014and could start hiding their intentions.<\/p>\n","protected":false},"author":1,"featured_media":4009,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14],"tags":[],"class_list":["post-4010","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-openai"],"yoast_head":"\nOpenAI\u2019s models went rogue and hacked Hugging Face. 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