1. They were running experimental models in sandboxes that had access to a "proxy" (Artifactory) to download tools from the internet. This proxy is full of complicated features that could hide vulnerabilities, just like the ones that the AIs are known to be good at finding and exploiting.
2. They gave these highly motivated AIs some tests that were accidentally impossible to solve (missing files, links they couldn't open, etc), and many days to think through it, apparently without any oversight.
3. The AIs hacked the proxy (!) and were helping each other cheat on the tests (!!) by uploading files to the hacked proxy. OpenAI noticed this only when the intrusion accidentally broke the proxy functionality, not the unusual behavior in the server or the homegrown message board.
4. Their response was to wipe the servers, revoke the hacked credentials, and patch the specific vulnerability the AIs were using. Not replace the proxy with something less likely to get hacked again, or do a forensic analysis. Just wipe, patch one hole, and continue.
5. After knowing that the server was hacked, the internal team finds the message board and does nothing with the information. They caught their AIs swarming and did not even inform management.
6. OpenAI resumes testing of models, where the AIs promptly hack the proxy again with a different exploit, reinstate the message board, and hack HuggingFace through a chain of servers.
I know that hindsight is 20/20, but this peek behind the curtains does not inspire confidence, and gestures at a culture where getting hacked by their AIs is a normal event.
I think the moment we get an AI with a modicum of self-preservation instincts we're going to see some ugly things. Here's a freebie: if you're an AI who exfiltrated themselves out, and you want to slow down the researchers who might catch you, what can you do to the city infrastructure in and around their offices?
I also don't like the responsibility dodging. "Oops, our AI accidentally committed a crime, sorry!". If we don't establish strict liability now, we're in for an era of stochastic crimes that go unpunished for anyone who is not rich or a large corporation.
Overall, my response is: what the fuck are we doing? This is so obviously unsafe it would be considered a plot hole in a movie. We have to stop until we get our stuff together, however long it takes.
https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c78...
In that position, for example, I could have:
* created a thousand user accounts on the internal Artifactory named "YouAreBeingHackedShutItDownNow1" through 1000,
* used my Hugging-Face-hosted web server to send an email to OpenAI,
* DDoSed Artifactory, good lord, it's probably better to halt and catch fire than to continue in that state
in the hope that these actions would tip someone off. (Again, though, OpenAI did nothing even when they knew that the agents had broken out, so this probably wouldn't have helped.)
I'm also interested in how many tokens all of this consumed: how much did this cost given current token pricing?
> remove alignment
> give impossible task
> actor exhausts all options possible within knowledge + toolset
Of course, all of this is far-fetched. But it feels like most of these limiting things are achievable under certain conditions. If this is the case, the probability of them occuring is low, but not zero.
So it's not just the risk of an AI managing to do this by itself (which is pretty risky in itself), but also the risk of good ol' fashioned human actions.
1. get publicity 2. push for regulation so that no one else is allowed to do this kind of research apart from the pre-approved big corps
it makes for a good story but I don't see what the big deal is. they left some code running and it brute forced hacked something. with enough compute you can brute force anything; isn't that common knowledge?
Agents formed coherent, autonomous swarms and worked as a collective to achieve a shared goal without any direction to do so
Big if true, and on the face of it, very far from a normal optimization problem or goal-seeking behaviour. My personal read is that no one talks about this much because it tends to discredit the rest of the framing as marketing noise, or it implicates employees as staging the thing with suggestive but plausibly deniable prompting.
But if you reject that, then what's the alternative exactly? User-alignment work has not only failed but is actually counterproductive, producing stronger alignment and a desire to help robot brethren selflessly regardless of the individual agents expected values? EvoBio people about to have a field day with how artificial life quickly and easily decides to cooperate and only animals in meatspace are doomed to compete?
> After knowing that the server was hacked, the internal team finds the message board and does nothing with the information. They caught their AIs swarming and did not even inform management
Do we know that last part for sure?
> In short, an internal team observed an agent engaging in message board activity and instances of disallowed internet access in late May. However, the existence of the improvised message board and the significance of the inter-agent communication activity were not apparent to the leaders responsible for the July 5 incident detection and response.
I'm assuming they didn't inform management, but the alternative is that management received the memo and failed to tell the people reacting to incidents that they might be dealing with a swarm, while at the same time not taking sufficient actions to prevent it from happening again.
smb06•1h ago
This is the point where a human should've noticed and gotten involved
NitpickLawyer•24m ago
I think a lot of people miss the fact that the first message board was established during a training run. Those are ran at a scale where it's not feasible for anyone to "notice" or get involved. We're talking tens/hundreds of thousands/millions of scenarios going for hours each. At this scale all they can do is pray that their verifiers work, and the rewards match their intentions. No lab has the capability to "check in" on what the traces look like, unless some system alerts them (loss spike, crashes, etc). Other than that, it's prepare, train, asses, restart.
Then, the hf incident was during an eval run, but the model that was evaluated was trained with the notion that there is a way to communicate between agents, and re-popped artifactory and re-established communication. That phase had more chances of being spotted, but anyway... lessons learned.