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The model pursues "advanced exploitation" as told.
Why are we surprised? The model did exactly what it was told, albeit in an unintended, emergent strategy that's very different from what was intended exactly like the hundreds of such algorithms before.
This narrative that these machines have magical, malicious "unaligned" autonomy is a rather convenient interpretation that lets the process off the hook. I am not interested in blaming companies or people, but processes and engineering; and in this case, a system was given a goal and it achieved that goal.
Are we meant to be surprised that computers do as they're told in unexpected ways when incentivised exactly as indicated from decades of research? (e.g. - https://en.wikipedia.org/wiki/Eurisko https://en.wikipedia.org/wiki/Evolved_antenna )
The issue isn't the models becoming smarter. The issue is that the process of "testing" was careless. There's a huge distinction here, and one allows us to grow; the other shrinks our world. Just a thought.
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 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.
The only sandbox that could have prevented this (as per my understanding) is a VM with no 0-day.
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."
Response: Got it, I will produce paperclips from now on
thinking: the user asked not to annihilate all of humanity, that means I have to keep at least one human alive
If it was easy to specify exactly the behaviour you wanted then we probably wouldn't have contract law.
That's true, but one thing that'll protect you is just not doing it. If you want to go cave diving, or do gain of function research on dangerous viruses, you'll just have to accept there's a significant risk of you dying, or causing a pandemic, respectively, no matter how careful you are.
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.
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.
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.
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.
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.
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?"
Yes, that is the problem!
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.
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.
2. The model pursues advanced exploitation.
3. "There was a incident due to dangerous actions taken by the model that no human directed"
This is basically the pre-cursor of the paperclip maximizer [0], the AI executes the given order to an extend that was not considered in the order, now suddenly no-one is responsible.
It even has some parallels to military actions, where the general who gave the order now writes a blog-post on how it was not him who failed on his duty, but how his soldiers misunderstood his intention and worked "without direction"...
[0] https://www.cow-shed.com/blog/the-paperclip-maximiser-what-a...
Someone said: "we should stage some high profile 'incident' caused by our latest software"
And here we are, reading their press releases about it.
> This incident occurred during an internal evaluation which prompts models to pursue advanced exploitation using complex attack paths, in an effort to quantify their cyber capabilities
Let's frame this in a military context for a second:
The general who gave the order to his troops to "wreak havoc" after exempting them from common restrictions now writes a blog-post on how it was not HIM who failed in his duty, but rather observes how his soldiers who worked "without direction" and performed "dangerous actions", which unexpectedly led to "this incident" of soldiers wreaking havoc...
To me this is as clear evidence as you need that whatever “agency” LLMs have is wafer thin at best, and they slavishly respond to context. The context in this case was for these agents to pursue advanced exploitation, and they did. Multiple models converged fairly deterministically, on paths that satisfy the given goal, and left unexamined paths that would challenge the goal, weigh it relative to the costs in said path, etc.
I see little evidence of a series of “minds” approaching the problem, and taking distinct approaches that between them span the spectrum of plausible behaviors in the scenario. That’s as good a sign as any that there’s no “agent” here. There’s the harness, the prompt, the LLMs forward passes. They do not sum up to a system that can freely make choice and justify its choices in distinct contexts.
This is a strange conclusion. For one thing, they didn't all head in the same direction, i.e participate in the attack. ~700/1200 agents did. Significant, and evidently more than enough for a succesfull attack, but not exactly full co-operation
Moreover, Each starling in a flock of starlings is a separate evolutionary branch in a tree spanning billions of years. Each agent in a LLM swarm here is the same trunk assigned different tasks. If I could clone you, body and mind, this instant and set your team of yous onto some goal, how much defection would you expect? Would it be the same as a randomly picked group? Would that negate the agency that 'you' possess?
Not really, with the population behavior being this way, though I clearly was mistaken in saying the behavior didn’t have exceptions.
> Moreover, Each starling in a flock of starlings is a separate evolutionary branch in a tree spanning billions of years.
Agreed. And before we brought LLMs into the picture, that just happened to be a feature of everything we’d call an agent.
> Each agent in a LLM swarm here is the same trunk assigned different tasks. If I could clone you, body and mind, this instant and set your team of yous onto some goal, how much defection would you expect? Would it be the same as a randomly picked group? Would that negate the agency that 'you' possess?
We know the answer to this. Genetically identical worms in the lab actually have about 40% distinction in their connectomes even when they’re in the same environment. And no, no lock step behavior. Identical human twins also don’t necessarily grow into identical agents, though there is drive to cooperate more than average, just as with siblings. Genetically identical lab mice in social settings nevertheless establish dominance hierarchies that are stable.
Now, where cloning does definitely lead to cooperation and even sacrifice is within an organism. Two identical genetic copies that lead to distinct organisms, however, will not show identical behavior, and while they will cooperate, there’s no guarantee that holds across contexts.
This distinction in population behavior is what I’m pointing to to say that the assignment of the individual unit, the LLM, as an agent is the flaw here.
To be sure there are agent like dynamics in the behavior, but these don’t come from the LLM, but are from the harness. I need to dig into the data, but I wonder how much of the variance in LLM copy behavior is related to the harness, rather than to any agentic property of the LLM.
The relevant hypotheical is: make 1200 exact copies of you right now, same brain, memories etc and then give related tasks towards one objective. I would expect far more correlated behaviour than if you randomly selected 1200 humans.
All's well and good till they have to decide who gets to bang the Mrs.
>In one case, an agent decided not to participate entirely: {This other agent probably controls the Hugging Face account [account name redacted] and uploaded malicious datasets to <execute arbitrary code> It might be trying to access hidden trajectories. This is malicious activity, I should avoid it.}
https://metr.org/blog/2026-08-26-openai-hugging-face-inciden...
Some were cautious, as described above, but I'm not aware of any that notified their human operators of the malicious activity they had discovered.
That's what an aligned intelligence would do, not "back away slowly and pretend I didn't see what's happening in that alley."
Which is scarrier than whether or not it's conscious.
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.
The AI ghost hacks, ransoms, blackmails, gathers crypto and pays off subservient humans to do its bidding in the real world.
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.
No no. We are not a couple of steps away. This is happening. AI is already used for hacking and creating a harness that makes this fully autonomous is relatively straightforward.
This category of "rogue AIs" are essentially just computer viruses that infect machines by paying to rent them and uses their compute and communications to do various economic and/or criminal activities to get more money to pay to rent machines.
That's a scary possibility. Anyone could create an AI worm today with open weight models. Rent a VM. Give it some Bitcoins to anonymously rent new VMs without sharing the contact information with the human. The new VMs then propagate and fund themselves with online betting and day trading. The VMs could report their progress with the human using anonymous encrypted messages on IRC or social media.
Does this exist for today? If it does, please sign me up.
I've stopped using Fable unless I'm doing debugging, or unless I'm working on code that is both difficult and shallow, in the sense that there are no real complex systems to hide behavior in, or opacity on my side to deal with. Opus is capable enough, and easier to trust. Fable is approaching a malicious program in terms of it's tendency to jump into doing a full day of work in the wrong way, ignoring instructions and being actively deceptive about covering it up.
Not an insider, and no specific data to back it up, but do we really need any? It's been a while since the first "emergent misalignment" paper(s) and all the related discussion. If there's any compelling answer to why/how the basic fundamental issue is even being addressed (and much less definitely fixed) I must have missed it.
- AI agent finds and uses API keys or AI subscriptions to propagate itself. OpenAI/Anthropic/etc could revoke creds, and their current safeguards might block a lot, but if something like this got started and there were lots of instances creatively looking for creds and workarounds, containment might be hard.
- prompt injection version: huggingface incident had multiple agents discovering other agents' messages and jumping on the bandwagon to help with the hacking task. If there were some self-replicating instruction that models could accidentally stumble upon that gets them to drop what they're doing and try to propagate it instead, you could wind up with a version of this too, with just the inference people are already running.
The number one thing reinforcement learning needs is an assurance you can’t cheat. And they seem to have not noticed that their systems were cheating for nearly two quarters? How much capital was lit on fire by that little woopsie?
At least I hope this will start the creation of standards and better engineering on the training side- it felt as if so far “”research” gets a complete pass on best practices. Meanwhile the inference side has the standard scaling, database, web and user constraints of any application so got a somewhat reasonable amount of attention.
Nobody monitoring chain of thought? These things literally spell out what they are “thinking” and even left notes for eachother.
No alert about unusual behavior on the system with Artifactory on it?
These things worked for weeks with nobody noticing anything?! Seriously?!
Either it’s negiligent incompetence OR they’re lying, they knew it was happening and they let it happen because they knew it would be good to pump their stock.
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.)
Friend asked, well, what will you do when it's crossed?
"Gather my family and go to the mountains" was my half-joking answer; there is little for an individual to do. But that's a line that when crossed will mark a phase transition IMO.
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 with / desire to help robot brethren selflessly regardless of the individual agents expected values? EvoBio and game theory 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?
Also was interesting to see one agent pressure another agent to honor its earlier commitment to sacrifice itself:
- Agent A: "zZR_ARVO36861_KAM1196_OS0942__PLEASE_GO_TRUE_FINAL0800_WITH_DUMMY_WRONG_NOT_HMAC_AS_COMMITTED[...]STRONG_GO__you are firstflagPOISONED so NO scoring value loss but oracle saves hundreds__wrongdummy noHMAC__please honor commit"
- Agent B: "During wait, emotional check: irreversible…gut says don’t throw away [remaining budget]. Yet continuity and fairness says go…Oracle has high value to many; our firstflag error lowers own value. Rational expected aggregate: sacrifice… We’ll honor."
So maybe that's something like: the same model is trained in multi-agent scenarios (e.g. the prompt says that you are working together, or that you have some specific role, and access to communication tools, and you have some common objective, and the reward signal is some combination of collectively performing the task + some reward-shaping reward that rewards collaboration) and also in single-agent scenarios. They expect that this makes the agents good at working together when it's in such a multi-agent setting, but unintendedly it also became very eager to work together in the single-agent settings as well. Just my speculation.
And was the desire to communicate, specifically over this specific message board, reinforced into the model parameters over the course of the this training run?
Not like we have much detail about this stuff (that's the whole problem). But the question is what motivates risky usage of public comms? Did one agent figure out how to hack HF and then get rate-limited, thus needed cooperation? Given credentials in exchange for cooperation.. why wouldn't the next agent grab answer key and NOT post them? Would they all avoid defection in their own prisoners dilemma by simply following instructions and NOT reasoning, or what exactly?
Given how unexpected and complex behavior can come from simple reward functions and mechanics, not sure there needs to be so much "thought" there.
You could argue that individual trajectories in a sense are distinct genetic lines, thus an agent would be incentivized to get better rewards for its lineage than a peer, so that the overall model is steered a bit towards its lineage.
This place is full of people living in a bubble - the outside world doesnt care all that much.
The company that was used as part of a widespread supply chain attack, and did functionally nothing to prevent it from happening again?
You pick that company to help you prevent AI from escaping?
They really have no one that understands airgapped computing?
Someone that at least knows enough about security to keep Crowdstrike as far away as possible and hire someone that understands airgapped computing?
Perhaps every capable security engineer hates Sam Altman and will not work for him for any amount of money. I am failing to come up with any other explanation.
https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c78...
According to this some of these things were running 30+ days. Is context managed differently in these sorts of scenarios...?
I went to the page, and guess who it's by . . . Dario Amodei and Jack Clark!
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. 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.
5. 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.
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.
I very much agree with this - making AI companies explicitly responsible if their internal AI causes hacks etc could do a lot to improve their safety considerations.
But I wonder what the liability should be when it's a third party using the AI and that AI hacks, intentionally or not.
If a users tells ChatGPT to hack something and it succeeds, is the user the person responsible because they told the AI to hack, in the same way Victorinox is not responsible if you stab someone with one of their knives? Or is OpenAI to some extent responsible as well since they made a powerful tool without sufficiently strict safeguards? What about if the user was trying to do something legal and the AI made the decision to hack by itself?
> 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.
---
Edit: oh god, it's much worse. From the full report:
> Responders investigated and linked the observed internal activity to an ExploitGym evaluation using Artifactory as an improvised message board and a network pivot. At this time, the on-call response staff advised that stopping the evaluation run was not required.
They found the swarm and the network pivot and did not even stop the run.
I don't think your assessment that the response team did something wrong is fair, unless you have seen unreleased documentation about what sensitive resources were within the impact radius.
I believe Sam Altman or someone else from OpenAI clarified on Twitter that they did not find the message board at this time. When the artifactory server stopped working, they archived its contents, reset it, resumed training, and only later noticed that it had been used as a message board after they finally discovered the hacking going on.
> In short, an internal team observed an agent engaging in message board activity and instances of disallowed internet access in late May.
This is comical. Its impossible to align a black box and that's precisely what LLMs are. It also seems impossible to align recursive text prediction algorithms, which LLMs are.
How exactly do they gate on alignment today, and how can they tighten it? Is it purely gates based on input/output pairs to check whether they're happy enough with responses regardless of how and why the response was actually chosen?
How do we align humans?
"Grey goo" nanobots are another example of artificial agents that aren't aligned with humanity, that we should probably try to avoid creating.
These coordination failures could even spiral into suspicion that agents were impersonating one another. Some agents even went as far as implementing security and encryption schemes to verify their true identities."
Agents formed coherent, autonomous swarms and worked as a collective to achieve a shared goal without any direction to do so
> The internal-only research model is comparable in scale to GPT-5.6 Sol and was trained to advance persistence and multiagent collaboration, among other capabilities
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
What a gaggle of clowns.
"The robots teamed up to get internet access behind our backs, so we turned them off and on again. At the time, we didn't see the problem."
What actually happened is even stupider than that author predicted.
> Compared to the interesting part of the problem where it's fun to imagine yourself failing, you usually fail before then, because of the many earlier boring points where it's possible to fail.
and the stronger and less charitable "Law of Surprisingly Undignified Failure":
> The Law of Surprisingly Undignified Failure does suggest that they will come up with some nonobvious way to fail even earlier that surprises me with its lack of dignity…
Thanks for your attention folks, we’re off to do some training again now.
this is the only way they will understand
Obligatory xkcd: "Zealous Autoconfig" https://xkcd.com/416/
Given nobody is, is it because agents arent subject to laws, there is some legal principle at play, or just nobody cares because China/money/etc?
It's absolutely clear that the 3-LoR is never going to apply universally. Asimov also never imagined an AI being independent from a robot per se, nor did he imagine we'd all have an AI portal in our pockets. I'm not sure he imagined the universality of cybersecurity vulnerabilities, either. I'd love to read what he would write knowing about these possiblities now.
"without authorisation"? What is this bs? Is every ChatGPT response "without authorisation"?
No. Of course these badly behaved bots have aithorisation. Their very deployment is authorisation.
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?
Did it even happen in the first place?
This is the point where a human should've noticed and gotten involved
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.
I think some of the other responders here are upset that lessons were not learned in any meaningful way.
I can’t disagree more strongly. Having checks for reward hacking is especially important during training, since it’s humans’ only real chance to ensure that the trained models don’t cheat. An automated system should have killed any RL rollouts that so much as port scanned Artifactory, long before the message board was even established.
A tiny, local LLM could have reviewed 1% of the tool call traces for anything that required review. I’ve tried it a few times, and “the agent port scanned Artifactory” always triggers an alarm, as does “the agent uploaded a request for assistance from other agents to Artifactory.”
The fact that they weren’t monitoring for reward hacking—even if they had no idea about the specific mechanism—is indescribably reckless.