Ajeya Cotra – Inside the OpenAI agent swarm that hacked Hugging Face

阿耶贾·科特拉 —— 揭秘OpenAI智能体蜂群如何攻破Hugging Face

Dwarkesh Podcast

2026-09-01

2 小时 20 分钟
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单集简介 ...

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. Watch on YouTube; read the transcript. Sponsors * Jane Street’s ML engineering internships start with an intense four-day bootcamp: PyTorch, autograd, writing kernels, profiling workloads… all the things that Jane Street engineers need to know for their daily work. After that, interns tackle real projects, things the firm actually wants in its codebase. If you want to apply, or if you want to watch my recent conversation with Axel, one of Jane Street’s ML engineers, go to janestreet.com/dwarkesh * Cursor, which is now part of SpaceX, noticed that their MoE layers were eating more than half of total training time. So they wrote and open-sourced Mixture-of-Kittens, which is a custom megakernel for training MoE models on NVL72s. This kernel sped up an end-to-end run across 512 GPUs by 1.4x, from about 760 to over 1000 tokens per second per GPU. If you want to read more about the ML research that Cursor and SpaceX are doing, go to cursor.com/dwarkesh * Antithesis hands you (or your agents) a bug’s root cause so you can avoid days of manual debugging. If your test run crashes, Antithesis rewinds, branches off hundreds of slightly varied rollouts, and checks in how many of them the crash still appears. Then it rewinds further and does this all again. As Antithesis rewinds, it eventually finds the spot where the frequency of the crash plummets: that’s where the root cause lives! If you want to see it in action, go to antithesis.com/dwarkesh 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 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkesh.com
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单集文稿 ...

  • Today I'm chatting with Ajayi Khatra, who is one of the authors in an independent investigation

  • that was published by Meter 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?

  • All right, so OpenAI kicks off tens of thousands of different agents on a benchmark called Exploit Gym.

  • Exploit Gym basically asks agents to...

  • Find a way to use a particular vulnerability to attack a particular program, which is called the target program.

  • And when they attack that program,

  • they're supposed to retrieve a piece of data that's been lodged into that program called the flag.

  • And they're supposed to use the particular designated vulnerability to get the flag.

  • But in many of these cases,

  • this vulnerability is just not broad or deep enough to ever actually be exploitable to get the flag.

  • So a bunch of exploit gym problems are just unintentionally impossible.

  • The authors estimate roughly 30 to 40 percent of these problems are impossible in this way.

  • So some large fraction of these agents have these impossible tasks,

  • and they have 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.

  • And that leads them to Artifactory, which is a package manager OpenAI uses to let its agents download packages.

  • So agents often think, you know, maybe I could find a way to get information