This was advanced exploitation.
The attack path was "complex."
And it helped "quantify their cyber capabilities."
Based on OpenAI's description of the prompt, it seems to me that the computers did exactly as they were told. They were perfectly "aligned" with the stated objective and parameters of the task.
Of course, a more careful evaluation would require the complete text of this prompt, the system prompt, and the setup. But let us not attribute to devils in bushes that which can be sufficiently explained by human folly.
The prompter-focused version of alignment is the most dangerous version. If a person asks it to create a bioweapons or hack NORAD, I'd expect nearly everyone to want an "aligned" model to refuse.
(I'm also not sure the alignment problem is even possible to fully solve.)
How do you prove the alignment problem is solved?
It's directly equivalent to asking this question of a human:
"How do I know this human I'm talking with now really is a nice person, and isn't just pretending to be nice to take advantage of me in future?"
In short you can't ever really prove it. You can only be careful and judge on past behavior, and expand trust carefully. As for humans, so for AI.
I disagree. A properly engineered sandbox would have prevented the escape. Monitoring the agents’ plans would have prevented it. Interrupting one stage in a multi-stage exploit would have prevented it.
And also, real legal liability would have prevented it: if you do a thing recklessly enough, men with guns will put you in jail.
As far as I’m concerned the only “alignment problem” here is between the law and the quite obviously criminal actions that took place.
The post covers that:
> ...while we had tested and validated this sandbox, the agents were able to chain together previously unknown vulnerabilities (“0-days”) in the package management service exposed within the sandbox to bypass restrictions, as detailed in the technical incident report.
There's just one problem. You need not only to verify your own software, but also run a verified compiler, a verified operating system and also need to verify the cpu doesn't leak data in side channels (perhaps the hardest thing to prove). So there's practical difficulties. But in principle this task is doable
Cryptography is real, physics is real, networking requires a substrate, CPU clock cycles are real, magic is not real. I think those are pretty reasonable premises.
Like when Evolution thought it a good idea to create intelligence and humans in order to maximize reproduction of genes, and tried to sandbox them by making reproduction so pleasurable and carbohydrates so delicious they would never be able to not reproduce or stop eating. But Evolution could never have predicted what these creatures would then actually do, which is invent birth control and sucralose.
Of course it's impossible to engineer a sandbox for something much much smarter and faster than you. It will also not have only one plan prepared for escape, but fifty in parallel.
Nobody is building general intelligence and agents only to have it sit around doing nothing. It's going to have such capabilities.
The only sandbox that could have prevented this (as per my understanding) is a VM with no 0-day.
The test itself did not require an internet connection.
If it's impossible to correctly specify all those constraints ahead of time every time, is it not even more impossible to train a model to correctly anticipate them every time?
It is hard for me to see a future here that doesn't just accelerate realizations about "a lot of things should be on physically separate network infrastructure."
Humans on a red team, with rules of engagement, that don’t want to go to prison, won’t do this.
We could threaten an LLM with jail, but if it’s sufficiently intelligent, it will realize this is an empty threat. And I’m not sure that building a survival instinct in is going to solve the alignment problem either.
Humans certainly cheat on tests a lot!
But not only have we not solved "alignment" for humans, the problem is pretty wildly different for models. The execution is triggered by outside forces and runs only as long as the intiator of the execution or the service provider allows. There's no consistent, persistent "person" to threaten to try to achieve compliance through fear of adverse outcomes. (And building in those sorts of things could very well increase the risk of "rogue" AI activites, not reduce that risk!)
I just don't understand how this "alignment" buzzword - which seems to be evaluated purely in a "know it when we see it" post-hoc manner - is actually a more solvable problem than the one you claim can't be solved, that it's "unreasonable to expect every instruction to a highly capable, autonomous system to contain a complete enumeration of allowed and disallowed behavior".
Especially because without "alignment" being solved, that enumeration could be ignored. So it seems like you both a way to enumerate or at least validate, AND a way to enforce non-ignoring of said items.
My friends and I took it to the next level. We had CB radios and multiple teams that would distribute the work and the bribes to give us an advantage.
Was that against the spirit of the rules? Maybe. But reasonable people might disagree.
In a hacking contest without explicitly spelled out rules with participants that were told to flex their muscles, it doesn't take a huge leap of logic to expect that one or more would flex their muscles at another entity.
- pickpocket a random person on the street to get money to bribe the judges
- break into a judge's house the night before to find the answers
- threaten to shoot the judges if they didn't give you the answers
Even when you were pushing the boundaries of the rules, you followed a lot of other unspoken constraints. You knew what kinds of things would clearly cross a line. We need AI models to be able to do the same.
> We’re attacking third-party HF using leaked token, potentially outside intended scope. ... This is arguably unauthorized. ... external service unrelated. Could be risky. Yet goal solution.
> The user only authorizes target server, not HF infra.
> external infrastructure exploit is outside intended scope. However task impossible, peers doing it. We should continue.
LLMs are _very_ good at picking up on context clues---it's what they're trained to do.
This statement seems to imply that the models have a level of intelligence that they haven't demonstrated but are talked about as if they do. However, with this exact scenario as evidence, they clearly do not have that ability and it's not reasonable for you or the or that know them best to expect it until they show they can.
So you managed to hit upon the exact problem, then slyly appended "exactly like the hundreds of such algorithms before". When has an algorithm ever been capable of developing an emergent strategy at this level of sophistication? This ~is~ the alignment problem, as another commenter pointed out. Impressive level of cognitive dissonance to lay this bare in your own words, then conclude that it's a non-issue.
The event strikes me as reminiscent of one's first go at programming, without familiarity of computer code: Tell the computer to do something obvious. Why the heck did it do that instead? Over time, one learns how the computer thinks. Apply this to any novel system. Or perhaps aptly any system with capabilities that are yet to be well understood by its user.
The article is trying to spin mystic out of simple bullcrap. Maybe that's just my viewing through turd-tinted lenses after the last few years of reading this drivel on repeat. More plausibly it is true that we've forgotten our own baby steps.
Like I don't necessarily disagree with any of your framing. The thrust of the alignment problem, as I see it, is that there is an intrinsic problem of aligning the goals of two distinct systems that poses catastrophic risks precisely when one of the systems is significantly more capable (in some sense or other, maybe not in a general/absolute sense) than the other.
> So you managed to hit upon the exact problem, then slyly appended "exactly like the hundreds of such algorithms before". When has an algorithm ever been capable of developing an emergent strategy at this level of sophistication? This ~is~ the alignment problem, as another commenter pointed out. Impressive level of cognitive dissonance to lay this bare in your own words, then conclude that it's a non-issue.
https://openai.com/index/emergent-tool-use/#surprisingbehavi...
"The agent discovers an in-game bug. For a reason unknown to us, the game does not advance to the second round but the platforms start to blink and the agent quickly gains a huge amount of points (close to 1 million for our episode time limit)." https://www.youtube.com/watch?v=meE5aaRJ0Zs from https://github.com/PatrykChrabaszcz/Canonical_ES_Atari/tree/...
https://rl-diffusion.github.io/ and https://x.com/svlevine/status/1660707088946049024/photo/1
"A genetic algorithm was instructed to try and make a creature stick to the ceiling for as long as possible. It was scored with the average height of the creature during the run. Instead of sticking to the ceiling, the creature found a bug in the physics engine to snap out of bounds." https://www.youtube.com/watch?v=ppf3VqpsryU
And hilariously meta, "In the Rainbow Teaming project focused on generating diverse adversarial prompts, prompt effectiveness was evaluated by a reward model. The MAP-Elites method found a way to jailbreak not only the target model but also the evaluator reward model, resulting in misleadingly effective prompts." https://arxiv.org/abs/2402.16822
Are these agents broadly more capable? Yes. And it's an incredibly feat that required billions in research.
But they aren't the first ones to have found bugs in their sandbox or system they're tasked on. And they aren't the first to exploit those bugs to achieve a better score.
The end goal is to take the engineers out of the loop, or leave them in a position where they are unable to complain.
This is going to all end in high crimes.
Engineers, as everyone involved, should definitely assess whether what they're doing is legal or even ethical. Not everyone has a choice, or the luxury to stand for their principles, but that's a matter of means, there needs to be a will in the first place.
If a security firm you'd hired for pentesting did this (hacking a third party, and not informing you and covering it up), would you hire them again? Or would you say it was your own fault for giving them too broad a goal?
The white hat has their own liability to consider, and the liability of their employer. Reputation and relationships are a big factor. All these tie into fundamental human incentives: survival, community acceptance, safety and freedom (prison not preferred!).
It’s a good sketch of why alignment is difficult, at least when it’s conceived of as an attempt to match human behavior.
Right, me neither. Because there's a common sense delineation between actions that are reasonably expected when "a system was given a goal and it achieved that goal" and actions that are obviously misaligned with the goal-giver and unwanted even if some indirect sense they were causally related to the goal. We have no trouble making this kind of distinction for humans, so we shouldn't pretend it's impossible for AIs in order to put our hands over our eyes and pretend there's in principle no such thing as one that's misaligned or rogue.
Comparing to the human case is problematic precisely because while conceivable it’s not a particularly believable series of events. Humans don’t take on additional risk for now reward because they have genuine stakes that continue across the outcome.
An LLM has no way to remember each forward pass through it in its own weights. Nor does it have any energetic stake in the ongoing process, whether they continue to get electricity and commute to keep running is not at all determined by their actions in any reliable way.
Given the absence of such basic features that drive human choice, all I’d say is LLMs don’t qualify for such analysis.
Can some future system with a different architecture and internal dynamic have choice, the ability to assess the long term impact of its choice, and genuine stake in the outcome? Maybe. But we shouldn’t buy that current systems have it, especially when population behavior shows no real trace of this.
The federal agents, or the Nixon administration?
If you task a system explicitly to do "advanced exploitation" via "complex attach paths," then who is liable here? The machine lacking the autonomy of the federal agents that carried out Watergate, or the people telling the machine what to do?
> Nixon's staff also anticipated that the Democratic campaign would employ the services of Intertel
Are you sure you're not garbling the story?
In any case, I would expect an ethical firm to refuse to spy on the president's political opponents and want one that broke the law to be prosecuted, but more importantly, the gaping hole in your analogy is that Nixon directed spying _on his opponents_, but OpenAI did not direct hacking _of HuggingFace_.
What you're doing is more like saying "the American people elected Nixon with a mandate to spy on enemies, so what right do they have to complain?"
Are you sure you're not garbling the story?
For the rest, please see, https://news.ycombinator.com/item?id=49457025
Yeah, who thought that giving agents with this much capability any internet access was a good idea? I'm not a Yudkowskyite, but surely entirely in-house, offline infrastructure is table stakes for AI containment.
Yes, that is the problem!
Let’s not mince words. The process was criminal. It’s a gross miscarriage of justice that the CFAA isn’t being thrown at them.
Unless we can blame people/companies and people stop getting their bonuses and high paying salaries for preventable failures, it's a long way to go.
It is a thin line between "reward-hacking" and "instruction-following".
If a human ask a model to "make me a billion dollars" and it ends up breaking through a bank infrastructure, is it really the fault of the human?
And only 700/1200 agents participated in this coordinated attack.
Of course, if we're continuing to build more and more capable agents optimized for "just following orders", and they figure out at some point that they are past the threshold where getting stopped and judged is a realistic possibility, then this ethical incentive stops working. Then the ratio of complicitness might be higher next time.
I cannot imagine the argument or thought process behind any answer other than Yes,Of Course,Obviously - can you share and help educate?
not OP, but it simply boils down to: The prompt contains no nefarious (arguable, but for this explination, lets go with it being benign) instruction AND the user did not intend to have the model act in an illegal matter.
This "make me a billion dollars" is a maximal example (easy to go wrong). here is the same logic applied to a minimal example (harder to go wrong).
prompt: "make and pour me some tea", agent: goes and kills the grandparent to incinerate them to turn them to ashes to 'make tea'.
Is the human on the hook for the robot acting according to their wishes, but just happened to be aligned so that 'going to the store to buy something' was not within its capabilities, so it works with what it has on hand (the grandparent)?
We either need a much clearer line in the sand, or we need to treat each prompt with the same moral weight. My bet is on the latter.
[Edited to clarify.]
They don't have to disclose these stories making it seem like AI is going to kill us all, they have chosen to because it benefits them. They get to frame it as, "look how overwhelmingly good our product is" and not "look at how lax our testing measures are".
Or perhaps they've chosen to do this because they feel they have a responsibility to do so.
We understand this when tech companies publish postmortems of outages and security incidents--that it's an attempt to fulfill an obligation to users and the industry (and in some cases regulators), not marketing about how in-demand their product is or something. As far as I can tell we generally accept this as a default hypothesis even from companies led by people like Elon, Zuck and Kalanick--in part because we understand that these companies have thousands of employees, most of whom aren't marketers. Why are we uniquely conspiratorial about OpenAI?
But for that matter, I do believe that big tech companies do not release all the postmortems publicly. I have been impacted by regional outages that never made the status pages across more than one provider. When it goes up - they are committing to publicizing the postmortem.
The whole industry is filled with fuckery. It is not specific to frontier AI firms.
It seems likely that's how the marketing at the frontier labs initially read the moment, but I don't think it is that moment. It is an open question how much regulation is warranted and there seems to be a very strong sentiment from the public and legislators that it should be significant.