Category: Imported News

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Reading OpenAI’s account last week of how some of its models broke their containment and hacked into the computer systems of Hugging Face, another AI company, was the first time I got genuine chills about what large language models are now able to do. But this is a case of human hubris, not rogue AI.

I am not an alarmist. In fact, I have been pushing back against AI scare stories for years. Even so, this incident crossed a line. I think it’s the clearest illustration yet of how the people building and testing this technology do not fully understand what they’re doing. OpenAI could—and should—have seen this coming.

Here’s what happened, at least according to the two companies involved. A couple of weeks ago, OpenAI started testing the hacking abilities of some of its new models, including GPT‑5.6 Sol (released in June) and what OpenAI describes as “an even more capable pre-release model.”

OpenAI pitted its models against a benchmark called ExploitGym, released in May, which challenges LLMs to find ways to exploit real-world vulnerabilities found in commonly used software.

To see what they could do, the researchers removed most of their cybersecurity guardrails. Then they ran the models inside a sandbox that was cut off from the internet except for one link to a third-party piece of software that acted as a proxy to the outside world, and let them install code that they needed to beat ExploitGym.

On July 9, according to reporting by Reuters, OpenAI’s models started trying to break through the proxy. They found an unknown bug in the proxy’s software and used it to access the internet. From there, they broke into Hugging Face’s computer systems on July 11, apparently looking for data sets and solutions that would help them complete their task. Hugging Face announced the hack on July 16. 

OpenAI did not realize (or at least did not reveal) that its models were involved until July 21, around 10 days after they broke containment and a week after Hugging Face had shut down the attack and alerted the FBI.

In a statement given to MIT Technology Review, OpenAI says: “We are conducting a thorough review along with external advisors and with oversight from our Safety and Security Committee. Once the review is complete, we will publish a technical report of our learnings for everyone.” The firm also confirmed that its researchers were properly using existing safety guidelines and procedures at the time.

Wake-up call

OpenAI has said the event was unprecedented—and in many ways it was. This was the first time outside of a simulation that LLMs escaped what was thought to be a secure sandbox, accessed the open internet, and attacked an unrelated organization. It’s a wake-up call that shows just how good the latest LLMs are at finding and exploiting vulnerabilities in real-world software with little or no human guidance.

And yet at the same time, what OpenAI’s models did is something this technology has done for years. Give a model a goal and it will very often achieve that goal in unexpected ways, finding loopholes that look like cheats. OpenAI itself has studied this behavior.

A decade ago, it shared results of an experiment in which a model was tasked with beating a video game called CoastRunners. Human players take it for granted that the way to do this is by racing a boat through a series of flags to the finish line, racking up points for each flag you hit. OpenAI’s model figured out that you could get a high score by spinning in a circle and hitting the same three flags over and over again. There have been dozens of similar examples from researchers since. AI will always find a way.

“Despite repeatedly catching on fire, crashing into other boats, and going the wrong way on the track, our agent manages to achieve a higher score using this strategy than is possible by completing the course in the normal way,” OpenAI wrote in a blog post about the CoastRunners experiment in 2016. “While harmless and amusing in the context of a video game, this kind of behavior points to a more general issue … it is often difficult or infeasible to capture exactly what we want an agent to do.”

I couldn’t help thinking about CoastRunners when I read OpenAI’s blog post about the Hugging Face attack: “All evidence suggests that the models were hyperfocused on finding a solution for ExploitGym, going to extreme lengths to achieve a rather narrow testing goal … After gaining internet access, the models inferred that Hugging Face potentially hosted models, datasets and solutions for ExploitGym. Knowing this, the model searched for and successfully found ways to gain access to secret information that it could use to cheat the evaluation.”

Last week’s news was not about rogue AI, despite the headlines. It was about models achieving the goal they had been given: Find ways to exploit vulnerabilities in software. The fact that those models then behaved in a way OpenAI had not anticipated isn’t surprising. But it is worrying.

Back in 2016, OpenAI had this to say about its CoastRunners bot: “More broadly it contravenes the basic engineering principle that systems should be reliable and predictable.” A decade on, those basic engineering principles are still AWOL.