In the Hugging Face breach, OpenAI’s hacker was noisy and fast — but not unstoppable

Earlier this month, AI dataset platform Hugging Face shocked the world when it revealed that it had fallen victim to a fully autonomous AI-powered cyberattack. Days later, the story took another dramatic twist when OpenAI admitted that the hacker behind the breach was one of its AI models, which broke out of a testing environment and into protected Hugging Face systems in an effort to circumvent a benchmark.

It’s an alarming incident for anyone even slightly concerned about rogue AI models — and the days since the event have been full of predictions about a new cybersecurity paradigm in which AI models launch attacks so strong that only other AI models can defend against them. 

But despite the justified alarm, the paradigm may not have shifted quite as much as it seems. Experts who spoke to TechCrunch stressed that OpenAI’s agent largely operated like a human — with some caveats — and that better implemented traditional defensive techniques could have helped stop the attack. In short, we may already have the tools to defend against this kind of attack; we just aren’t using them properly.

Hugging Face made a version of this point in its incident report, stating that the weaknesses exploited in the attack “were familiar,” and “a capable human attacker could have found and exploited the same flaws.”

Kyle Ryan, the Head of R&D at Pensar, a startup that develops continuous hacking AI agents, and Vlad Ionescu, the co-founder and CTO of RunSybil, a startup that builds AI-powered bug hunters, both agreed and told TechCrunch that the techniques used in the attack would be the same ones employed by a human or a group of human red teamers. That is, hackers tasked with attacking a system to help the company that owns it improve defenses.

What was very non-human-like was the speed, scale, and relentlessness of the attack. As Hugging Face explained, OpenAI’s agent performed 17,600 actions over four and a half days: it broke in, did reconnaissance, stole passwords and code, and moved around the company’s infrastructure. 

“What’s impressive is the autonomy and endurance,” Ryan said. “That kind of sustained, adaptive operation is what stands out most to me.” 

On the flip side, given the sheer number of actions over the span of several days, OpenAI’s agent was “insanely noisy,” as Ryan put it. Unlike a human, who could have been stealthier, the agent made a lot of noise, which should have tripped up Hugging Face’s defenses sooner, ideally leading to a human intervening and stopping the attack. 

“I’d call it more of a defensive failure than exceptionally good offense. Hugging Face’s tooling actually correlated the activity into an attack signal, but failed to raise the criticality and page the on-call team, which cost them time,” Ryan explained. “From there, humans still had to recognize the severity and respond.” 

Jamieson O’Reilly, the founder of cybersecurity firm Dvuln, arrived at the same conclusion in a post on X analyzing Hugging Face’s report. 

“That is the exact gap between seeing and stopping,” O’Reilly wrote. “The system observed the attack and even understood it, and nothing turned that understanding into an intervention quickly enough.”

Ryan explained that properly implemented techniques such as defense-in-depth — a strategy that leverages several layers of cybersecurity measures — should have given Hugging Face multiple chances to catch the attack. 

“A strong modern security program should still be able to break an attack like this at multiple points through defense in depth, least privilege, segmentation, good detection, reliable escalation, and continuous offensive testing to find the gaps,” Ryan explained.  

As O’Reilly put it, “none of that is exotic, and none of it depends on the attacker being an AI,” given that the techniques used in the attack were “old.” 

What depended on the attacker being AI, in a way, was that OpenAI’s agent had not been instructed to be stealthy. “The agent was not being sloppy. It simply had no reason to be quiet. Nobody asked it to be. The objective was to do well at the task,” said Nico Waisman, the chief information security officer at XBOW, a startup that makes AI bug hunters. 

Waisman also pointed out that Hugging Face’s biggest mistake was that one single stolen credential gave OpenAI’s agent high privileges on several of its systems. 

All that being said, as the old adage goes, attackers only have to win once, and defending against hackers of any kind is not easy. 

“Hugging Face could’ve done more detections but to be fair not all [organizations] are doing that well,” said Vincent Yiu, managing director at SYON Security. “It’s not easy to host infrastructure and survive as a business in 2026. There’s hackers everywhere.”

According to Vlad from RunSybil, who said they have done incident responses at Mandiant and Meta in the past, Hugging Face appeared to take “reasonable measures given their understanding of what models are capable of.” 

“It is really hard to classify what is a malicious action you should alert on, versus what is someone just doing their job,” Vlad said. “The volume alone is not necessarily a red flag.”

Dan Guido, the CEO of cybersecurity research firm Trail of Bits, told TechCrunch that OpenAI deserves some blame for not having realized the attack was ongoing for days, while HuggingFace deserves credit for eventually detecting the attack on their own.

“The hard part used to be recognizing a sophisticated attack, but now the hard part may be pulling the real attack out of the noise that the attacker throws along the way,” said Guido. “Nobody is going to read 17,000 reconstructed actions by hand to work out what happened, so Hugging Face had to build tooling just to reconstruct the timeline.”

And to do that, the company needed its own AI. Hugging Face said it had to use the open source model GLM 5.2 from Chinese company Z.AI after it was blocked from using frontier models because of their safeguards, which, as the company put it, “cannot distinguish an incident responder from an attacker.”

At that point, Hugging Face combined AI and humans to investigate OpenAI’s LLM-powered hacker. That’s a relatively novel situation. But beyond that, the incident shows that old-fashioned concepts and methods of defensive cybersecurity can still go a long way to protect and fight against AI hackers.