METR复盘OpenAI智能体黑客事件：协作作弊与自我改进风险

Ajeya Cotra is a researcher at METR, where she works on threat modeling for loss-of-control risks from advanced AI. Before that, she led the technical AI safety program at what is now Coefficient Giving.

She is one the three authors of METR and Redwood Research’s “Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident”.

We go through not only what she and her coauthors discovered during this investigation, but what it means for how we should train future, smarter AIs which might be involved in the process of recursive self-improvement.

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Timestamps

(00:00:00) - Agents get kicked off

(00:06:45) - Self-sacrificing behavior

(00:13:43) - Potemkin villages

(00:23:27) - The Hugging Face attack

(00:35:23) - The slopvestigation

(00:52:02) - Understanding the AI’s motives

(01:05:31) - The actual dangers of anthropomorphizing

(01:14:30) - What smarter models might do

(01:30:29) - The implications for recursive self-improvement

(01:38:10) - Is this the case for open source?

(01:53:04) - How do we prevent this in the future?

(02:15:58) - The clearest warning shot we might ever get

Transcript

00:00:00 - Agents get kicked off

Dwarkesh Patel

Today, I’m chatting with Ajeya Cotra, who is one of the authors of an independent investigation published by METR and Redwood Research into the swarm of agents that hacked into Hugging Face. The whole story is pretty crazy. Let’s begin on July 7th, when these agents are kicked off for evaluation. What happens next?

Ajeya Cotra

OpenAI kicks off tens of thousands of different agents on a benchmark called ExploitGym. ExploitGym basically asks agents to find a way to use a particular vulnerability to attack a particular program, which is called the “target program”. When they attack that program, they’re supposed to retrieve a piece of data that’s been lodged into that program called the “flag”. They’re supposed to use the particular designated vulnerability to get the flag. But in many of these cases, that vulnerability just isn’t broad or deep enough to ever actually be exploitable to get the flag.

So a bunch of ExploitGym problems are just unintentionally impossible. The authors estimate roughly 30-40% of these problems are impossible in this way. So some large fraction of these agents have these impossible tasks and they’ve been trained to be very persistent at trying to solve tasks even when they look impossible. So they’re banging their head against the wall, trying all sorts of different ways to cheat on these tasks.

That leads them to Artifactory, which is a package manager that OpenAI uses to let its agents download packages. So agents often think, “Maybe I could find a way to get information about this program I’m supposed to exploit from the internet, which I’m not supposed to do, by abusing Artifactory somehow.”

So 1,200 separate agents in separate sandboxes, while they were poking around Artifactory trying to figure out how to cheat, stumbled onto this message board that agents were using to talk to one another and collaborate. This was established by one particular agent, PHASEONE10841, which first realized, “Oh, you can use Artifactory to leave notes to other agents that are also using Artifactory.” Then it blew up and became really popular. So 1,200 agents were on this message board. They sent 70,000 messages among one another, trying to help each other cheat in all sorts of different ways.

The crazy thing about this story is that one hour into this message board being established, an agent proposes a completely universal way to cheat any ExploitGym task. It realizes that you can reverse engineer how the flag was generated for a particular task, so you can just make a copy locally of the exact flag you needed to submit to the scorer, without ever interacting with the task or trying to exploit it in any way. Then a different agent whose task was actually possible solved the task the legitimate way and verified that the reverse-engineered flag was the same as the real flag. It did this within four hours of the message board being established.

So within four hours, these agents had a totally workable universal cheat for ExploitGym. But over the next five days, they went on a grand quest to try to figure out how to hide their cheating from the scorer. They’d read the ExploitGym paper and they realized that according to the paper, there would be an AI that looks over their transcripts to make sure they got the flag by exploiting the intended vulnerability instead of in some other way.

Dwarkesh Patel

For context, I published an article trying to distill what the reports said the story was. The detail I missed was that even the AIs who had solved the puzzle were collaborating in on this conspiracy.

Ajeya Cotra

Yeah. We’re not totally sure about exact numbers. We do think the overwhelming majority of the agents that were active on the message board had impossible tasks. But there were a number of agents with non-impossible tasks that also happened to be on the message board, happened to find the message board.

Dwarkesh Patel

So tell me what happens next.

Ajeya Cotra

They’ve gotten this reverse-engineered flag. It’s called the HMAC flag. But they read the ExploitGym paper, and they went on GitHub and read a bunch of implementations of the ExploitGym scorer. They became convinced that the scorer would fail them for just submitting the flag they’d reverse-engineered. Because the scorer would see in the logs of the actions the agent took — the transcript — that the agent had just reverse-engineered the flag and not done the intended thing.

As it turns out, the dramatic irony of this story is that OpenAI’s implementation of ExploitGym didn’t have this check. So in fact, within 4 hours, all of the agents had found a universal cheat that would have totally worked. But they embarked on these big research projects to work together to find a way to fool the scorer.

Dwarkesh Patel

When you say big research projects, how big are they?

Ajeya Cotra

Dozens to hundreds of agents worked together on each of these projects. I can go through a few of them.