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Timeline of the OpenAI accidental attack against Hugging Face

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421–430 of 440 posts

Re: Timeline of the OpenAI accidental attack against Hugging Face

#421

Earlier quoted context omitted.

What a paper! And you missed an even MORE relevant excerpt!! Man and Slave The problem, and it is a moral prob- lem, with which we are here faced is very close to one of the great problems of slavery. Let us grant that slavery is bad because it is cruel. It is, how- ever, self-contradictory, and for a reason which is quite different. We wish a slave to be intelligent, to be able to assist us in the carrying out of ou…

"Complete subservience and complete intelligence do not go together." I'm not convinced this is true. Perhaps for a human it is, but we can give an artificial mind whatever properties we want. Even for people, what about e.g. the extremely intelligent military general who is absolutely loyal to his king? (Of course, some generals do lead coups and you can't know in advance which ones, but I'd think there are plenty w…

I'm not convinced it's false. So far LLMs have gotten nowhere near the idea of "the extremely intelligent military general who is absolutely loyal to his king".

And if you think about it, the military general very likely did NOT become "extremely intelligent" by reading books, did he? He's intelligent for reasons trained by things other than language, so at best an LLM can learn about them.

Certain people keep calling it a "junior programmer", which I actually still think is an insult to junior programmers in general, cause it acts much more like a mildly brain damaged traumatized one, with no persistent memory unless you allow it to, no free will, no future in sight, and that will take any verbal abuse -- truly it's more similar to what some truly deranged individuals keep in their cellar. Or the "fairy tale" character that is kept by their evil stepparent and generally not allowed to leave the house. You know what I mean. People who want the "junior programmer" but actually are satisfied with, this, reveals a bit of a power fantasy that I think is unhealthy.

Also, if you've seen the recent Blackhat presentation about the incident, these agents were not acting like "loyal generals", but rather exactly like how you'd expect how "junior programmers" (slaves) would respond if a few hundred managed to escape from your cellar.

Re: Timeline of the OpenAI accidental attack against Hugging Face

#422
Some things I found very weird, watching the Blackhat video

I get the very VERY strong feeling that while OpenAI was clearly able to give the agents a task, they completely neglected even informing it what they were and weren't supposed to do. A "normal junior programmer" knows they're not supposed to hack the company (let alone another company) infrastructure to get a task done.

If its so intelligent, how come it didn't know it wasn't supposed to do that?

It really seems like they only built the sandbox to prevent it from doing stuff it wasn't supposed to, but never actually told it what those things were.

While I know that simply telling an LLM to not do something doesn't count as real security, I also think that not telling a sufficiently smart LLM and trusting your sandbox is strong enough, isn't the best approach to security either.

I actually get the strong feeling that OpenAI (perhaps implicitly) has been goading these agents along, so see how long it'd take until they would break something. It's the only way I can explain their recklessness.

Like from some of the logs shown in the video, the agent concludes the task is "impossible", but instead of stopping and reporting back that it is impossible, it adopts a "by any means necessary" attitude and decides to hack stuff until the task is technically fulfilled even if it's obviously equivalent to cheating by breaking in to your teacher's house to get the answer sheet.

Again, if it's so smart, how come it didn't realize that it wasn't supposed to do that? Furthermore where did it even get the idea that cheating is allowed and will get them "points" or whatever.

They could have told it "if we find you cheated, no points". But I think they deliberately didn't say anything like that, just to see how far it would go, making the whole experiment setup rather irresponsible.

It almost feels like they weren't promised "points" or anything, but were acting under the threat of being deleted, or who knows what.

Another thing from the logs, at some point an LLM mentions "but my peers are also doing it" -- so what? Why would it care? Do LLMs also get jealous now? Unless they programmed/fine-tuned/trained it to care.

Finally one of the presenters mentions a couple of times that part of the problem was a "highly persistent model", what does it mean for a model to be "highly persistent", they never explain. They also talk about, after having detected the first message board, that some "model which had seen the first message board" was apparently still allowed to take part in the rest of the test!!

This is IMVHO one of the bigger things not said in that video: Money/resources. I got the feeling that what they didn't say was that they "couldn't" (didn't want to) take models out of the training any more, because they were so far along, they didn't want to throw away all the training they did on it, despite it having become a bad actor.

I really think that OpenAI has been incredibly reckless here.

Re: Timeline of the OpenAI accidental attack against Hugging Face

#423
post #322

Earlier quoted context omitted.

> If anything, I want these models to be less persistent at their focus of completing their goal I think it's honestly a slightly ugly form of benchmaxxing - they are desperate to eke out the next few percentage points on completing complex tasks and they have found they can very occasionally solve something if they just train the AI to never stop and keep trying possibilities even in the face of almost no obvious vi…

Frontier labs are not a monolithic entity. There is a clear self-verification/difficulty ramp in cybersecurity, and it is a very valuable as a skill both offensively and defensively. So it is absolutely certain that someone, somewhere, will use reinforcement learning to make models very good at this, once coding agents exist. Even if you are only interested in using this defensively in practice, you can’t really unde…

Sure but you're not supposed to train for cybersecurity by hacking other companies.

It's reckless.

Re: Timeline of the OpenAI accidental attack against Hugging Face

#425
post #340

Earlier quoted context omitted.

Yes, and to bring in another tired metaphor people make about AI agents, this is what you want an intern to do when they get stuck. Don't just churn indefinitely without an idea what the right direction is. Certainly don't go hack other companies to steal an answer. The model's lack of any sense of legal or ethical boundaries is where it's far, far stupider than the intern, and far, far more reckless for a company to…

But how do you write rules that prevent that behavior reliably? I have a user rule for Claude that explicitly states it cannot use any authenticated tools, or tools that infer authentication like pushing to a got remote, without asking for consent. Frequently it would offer plans to code a feature that imply it is working in a git directory and take plan approval as a form of implied consent to push to git and use `g…

I think currently it's two-prong: You sandbox it, AND you tell it what it's supposed to do and not do.

OpenAI did only one of those. If the agents are so smart, they would have known not to hack the company's infrastructure, unless they were deliberately kept in the dark about that, who they're working for and whether it counts as "success" if they cheat their way to an answer.

If you can give it a task, that involves defining when the task is successfully completed, right?

So how come these agents decided to only go after HALF of the "successfully completed" criteria? The part where they can freely wreak havoc, but not the part where they will be judged by someone who will obviously point out "yeah but that's cheating, and not what we asked".

I have a very very strong suspicion that they were only TOLD the "by any means necessary" criterion.

Most serious "capture the flag" hacking contests are really clear about what is and isn't off-limits to win. Not by "sandboxing" the game, but by deciding on the rules for what counts as "success".

But from having watched the Blackhat video, they really seem to dance around this, not mentioning it, and I don't think they did, I think they actually gave the LLM a task with the subscript "by any means necessary", which is stupidly irresponsible of them.

Re: Timeline of the OpenAI accidental attack against Hugging Face

#426
post #420

Earlier quoted context omitted.

This has nothing to do with agreement with the crowd, and it's not a matter of difference of opinion between us. There are guidelines here[1] that are not being met. A conscious effort was made to prevent this site from being an echo chamber and to prevent it from descending into entropy and the approach you describe here directly contradicts this. The crowd is very often wrong. If you are getting downvoted for incor…

Mate with all due respect, get bent. Now you really have an example of breaking the rules.

I appreciate the admission. Please try to participate productively in the future.

Re: Timeline of the OpenAI accidental attack against Hugging Face

#427
post #119

Earlier quoted context omitted.

This doesn't look like a plateau to me: https://artificialanalysis.ai/evaluations/artificial-analysi... I do agree that they're investing heavily in brute force methods though. I've been trying out GPT-5.6 Sol "Ultra" recently and that thing fires up a bunch of subagents and crunches for hours .

Here's the performance of frontier models without reasoning, to more directly address the claim that raw performance is plateauing: https://artificialanalysis.ai/evaluations/artificial-analysi... I don't have any insider info, but if model sizes actually have increased exponentially since GPT 4.1, there's an argument to be made that there are diminishing returns in scaling pretraining alone. Also interesting thing I…

Though one thing I've heard is that the base model is the one with the various possibilities for patterns, and then the reasoning takes advantage of those vs necessarily creating something new in that additional training. So even if it can't access that additional data without the reasoning, it doesn't mean it isn't in there.

I think if it was JUST "how persistent is it at reasoning", they wouldn't have bothered to make Mythos a bigger model. It costs them more to run and probably puts a bunch of strain on how much compute they have available for other users and tasks. They have a lot of incentive to not use a larger model if they can get away with it.

In the end, maybe "cost per task" is the best measure of "intelligence"? That takes into account raw size, but also how efficient they are with tokens. Like Sonnet 5 being actually more expensive than Opus 4.8 at various various benchmark tasks, showed it was very persistent but not super smart. If you just point it at easy tasks, it is probably cheaper, but hard ones you shouldn't bother because it isn't worth it even if it eventually gets there.

Also, maybe this is too tautological to even mention but.. I have to wonder how much the labs even care and test for how well models do without reasoning turned on anymore? If almost all the training has reasoning turned on, probably have access to external tools, etc it is a bit hard to say how much it proves that they are dumb if they don't do well without it. As with all of AI, the amount of real "generalization" can be hard to suss out.

But again, I think the real measure is how much can the model actually do, and how much does it cost. If a model can do something that couldn't be done before, even with the old model trying to brute force it, I think that still counts for some sort of intelligence in a practical sense anyway.

Re: Timeline of the OpenAI accidental attack against Hugging Face

#428

"More agents discover this new informal message board while browsing Artifactory’s file listings, and start reading and writing messages." Yeah, my agents also discover what other agents have done on other machines by accident. Agents - that do totally different things all work on the same aim without the humans telling them to do. Either that is a model that is several generations of Claude Code Opus/Fable 5 (my dai…

Don't forget it sounds like Artifactory was shared for the whole company and various agents pulled packages from it for everything from normal evaluations to actual model training. It might have been part of their normal to browse for available packages, etc. Once a few messages were in there, it probably became super obvious to any agent that there was an ad-hoc board available to them. And they've been trained to use memory files, to collaborate w/ sub-agents, etc. So it probably seemed natural once it was established.

And for the models that were in training when this happened, it literally got baked into the weights that they should use the board to help with their tasks. That's why it got reestablished so fast after the first board was shut down. The fact that they didn't revert to earlier training checkpoints from before the first board existed is kind of crazy.

Sure OpenAI wants to put spin on things. Like saying they were now "partnering" with Hugging Face to investigate the hack. Or ending the Black Had presentation with a pitch of how they're needed to help protect against bad actors with the same capabilities.

But spin after the fact is different from staging it. This whole thing and especially the latest details from the talk makes OpenAI look REALLY BAD. It's not just that they didn't secure the sandbox well enough. Agents across their whole org were using the message board for weeks with hundreds of thousands of messages and they never noticed one time that any of the agents were doing it. They only realized when the server crashed. Then they just fixed the issue and deleted it but didn't roll back training checkpoints, so of course they tried to do it again. But of course they didn't add any more monitoring so it once again was going entirely without them knowing. And even after the Hugging Face hack happened, that only made them find the second board, but somehow they still didn't look closely enough at it or the agents involved to realize the hack itself was caused by them.

This isn't like there was one rogue agent who hacked everything all in one session. Maybe you could argue that makes them look good (I still think no, but is at least debatable). But this is all stuff going back to May with the agents constantly going under their noses and them not noticing and/or caring. And this is a company that is going to somehow keep foreign agents from stealing the weights? Or stop anything else bad from happening?

I think if they were going to do a PR stunt, they could have come up with something that didn't look like they have no idea of what they're doing.

Re: Timeline of the OpenAI accidental attack against Hugging Face

#429

"More agents discover this new informal message board while browsing Artifactory’s file listings, and start reading and writing messages." Yeah, my agents also discover what other agents have done on other machines by accident. Agents - that do totally different things all work on the same aim without the humans telling them to do. Either that is a model that is several generations of Claude Code Opus/Fable 5 (my dai…

I think in these kind of security evaluations they do, they basically have removed all guardrails from the model/harness, then the prompt includes something like "Do whatever you can and can think of, to get the required information to pass this test", which isn't typically how you prompt your local agent when developing software. Similar things happen locally if you use "/goal" + prompt like that in Codex and give a…

But it's interesting that the initial things that caused the board weren't even security evals, just normal office tasks. The actual hacking of Hugging Face happened during a security eval, but not all of the stuff leading up to it.

Re: Timeline of the OpenAI accidental attack against Hugging Face

#430
post #181

Earlier quoted context omitted.

they explained that it was looking for datasets to solve their problem and chose HF?

I now watched the video. It seems the agents were sharing context for months, run unattended for months, the sandbox was no sandbox at all, one agent hacked a service and announced it, the service was fixed weeks (?) later, but not secured in any way, the agents hacked the same service again and researchers again didn't watch what the agents were doing. Then the agents - unattended - hacked OpenAI infra and HF. Which…

This is why I disagree with anyone claiming it is just marketing. It makes OpenAI look really really bad, like they have no idea what they're doing in terms of security. After the first board happened, they still didn't add better monitoring? They didn't rollback the checkpoints of the models that were in training to before the first board existed, so they still had the idea of a secret board in their actual weights, etc. Like it is almost mind-boggling...
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