GoKawiilStochastic Flocks & Cybersecurity 'Pandemonium'
💡 The Data Fix, discussing the OpenAI / Hugging Face hack. Embedded below or find it on your podcast services This essay was drafted from my appearance on Mél Hogan's podcast,, discussing the OpenAI / Hugging Face hack. Embedded below or find it on your podcast services here
OpenAI put out its full technical report on the Hugging Face hack this week, alongside an independent report from Model Evaluation & Threat Research (METR). You may be familiar with the incident from the hundreds of breathless headlines about "rogue AI" – Time Magazine "100 Most Influential People in AI" listee Dwarkesh Patel blamed it on "three consecutive secret AI civilizations."
The real story: OpenAI was testing two models in parallel: GPT-5.6 Sol, and an internal model they refer to as IM1 (sometimes called HPIM). The reports find about 95% of the agents engaged in this activity were from the internal model.
The tests come from ExploitGym: 898 puzzles set up as capture-the-flag exercises used to evaluate these models for cybersecurity capabilities. The test requires giving the model buggy software and hiding some text somewhere that's only accessible by exploiting the bug. But OpenAI's combination of models, instead, ended up hacking a rival company, Hugging Face. Now we have a clearer picture of how.
Did an AI "go rogue?"
Three things in these reports challenge the "rogue AI" frame.
OpenAI turned off all of the model's safety mechanisms.
The whole point of the test is to see how far these things can go, and to score highly on the benchmark — because if you can build a model that hacks, you can build one that defends. That's the logic. This is red teaming: a friendly opposition hacking its way to the flag. So you turn off anything that would make the model hold back. Less "rogue," more "off leash."
OpenAI gave the model impossible tasks.
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