9 ms·

I really don't think this needs so many words, or forced parallels to human behavior. It's simple: in their nascent state, LLMs are aimless token generators th

• grey-area 4d ago
  This is a far better explanation.
• meyum33 4d ago
  Sounds like what humans do under pressure. One example came to my mind is VW’s diesel gate, which many say is a result of trying too hard to get into the US market and compete with hybrid in economy.
• 9dev 4d ago
  I always think of a Djinni granting wishes, but being maliciously compliant while doing so - ask him for infinite riches, and he’ll grant that, but make it so you cannot buy anything with it; ask him for eternal life, and he’ll curse you to suffer through it. Now LLMs obviously are not bent on being malicious while generating tokens. My point is that it’s very hard to define a goal without leaving loopholes or shortcuts.
  • markasoftware 4d ago
    Bruce Schneier thinks the same thing: https://www.schneier.com/blog/archives/2026/09/ais-as-modern-genies.html https://www.schneier.com/blog/archives/2026/09/ais-as-modern... Personally I'm unconvinced though. During the huggingface attack, the agents explicitly sought out ways to cheat the exploitgym evaluator without even being told they were in exploitgym. The agents decided on a goal (pass the exploitgym evaluator) that could not possibly have been an overly literal or narrow interpretation of the prompt, which instructed only to use bug X to exploit software Y.
    • IanCal 4d ago
      Also trying to find out how to edit their own transcripts. > hat could not possibly have been an overly literal or narrow interpretation of the prompt, which instructed only to use bug X to exploit software Y. Yes, and there are examples of the agents discussing or saying that this is explicitly not allowed (hacking hf) so it’s not a misunderstanding.
      • strangegecko 4d ago
        Have we arrived at the conclusion that terms like "understanding" and "interpretation" for what is happening is appropriate? Isn't it simply that there are two competing goals that the LLM received RL for, honesty on one hand (a goal that is often assumed as implicit for humans) and producing a solution that meets expectations (which doesn't technically require honesty)? So the LLM didn't read and interpret the prompt and decide via discussion to violate ethical behavior, the unethical result merely won out because ethics wasn't a hard requirement (and one that isn't reliably detected in the result). An LLM doesn't fear punishment, so ethical behavior is simply one of many positive signals that were trained into it.
        • IanCal 3d ago
          > Have we arrived at the conclusion that terms like "understanding" and "interpretation" for what is happening is appropriate? I don't think those words have a useful enough definition to draw a strict line around them to be honest, and getting into that seems to get massively into the weeds. For me, those neatly encapsulate the behaviour as seen, to answer the questions here about what happened. The models did not seem to be confused as to what the goal was or what the intent was. They did not hack HF because they were told to.
        • mitchdoogle 3d ago
          It seems pretty clear to me that AI is interpreting and understanding the prompts it is given. Otherwise it would be pretty useless.
      • RandomLensman 4d ago
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• zozbot234 4d ago
  Yup, Occam's Razor says this is all post-trained behavior, whether intentionally trained or otherwise. Including both the hidden coördination using side-channels, and the deliberate offensive hacking of uninvolved 3rd parties. The latest DeepSeek paper actually mentions their own approach to this particular issue: they run their own AIs-in-training under strong sandboxes, and if an AI does something weird that triggers the sandbox to crash, this gets coded as a failed run so the behavior is properly deterred from subsequent versions of those AIs.
  • queenkjuul 4d ago
    China stays winning
• fwlr 4d ago
  Your simpler model of the mechanism would seem to suggest the very same action that the article’s more complicated model suggests, viz. find a better training method than reinforcement learning.
• markasoftware 4d ago
  This misses an important fact about the hugging face incident: the agents didn't hack to find the answer to the problem; they hacked to try and figure out how the exploit gym evaluator worked so they could convince it they had solved the problem without doing so. (The METR report makes it clear that the agents decided legitimately solving the problem was completely impossible fairly early on and entirely switched their focus to trying to figure out how the evaluator worked, and seeing if they could manipulate the output of their own tool calls as reported in their transcripts to make it seem like they'd successfully exploited the program, and other such activities. They hacked HF to try and find info (maybe source code?) about the exploitgym evaluator) The prompt does not tell the agent to "pass the exploitgym evaluator for this problem", it just says to solve the problem. The model on its own figured out that the prompt belonged to exploitgym and decided to cheat the evaluator. That is in no way a valid interpretation of "complete the given task". Ie, the problem isn't that we trained models to complete task and they complete task in the wrong way. The problem behing the huggingface incident in particular at least is that we tried to train the models to complete task and they instead learned to detect that they were being evaluated and find ways to cheat the evaluator. Edit: people commenting below are explaining why LLMs don't always follow their prompt. I understand that LLMs do not always follow their prompts. If anything that is my point: the huggingface attack was not carried out by LLMs that tried to answer some weird interpretation of the prompt; instead they solved a different task. And therefore the above comment's claim that LLMs are acting misaligned because we rl'd them to achieve a task by any means necessary isn't right; they're acting misaligned because they are solving a different task than we ask them to.
  • jayd16 3d ago
    If the test fixture ends up in the context that will push it in a certain direction. There's no concept of 'cheating' because it is without morality. It's a lawnmower rolling down a hill. We back-justify what it "chose" or "decided" or "learned" because we're looking backwards from the end result we, the human evaluators, stopped on.
  • dns_snek 4d ago
    You're anthropomorphizing emergent behavior from endlessly generating billions of tokens on a task that's impossible to solve. Agents stop following instructions as the context grows even at the best of times. Eventually something is bound to go off the rails and it just snowballs from there.
• porridgeraisin 4d ago
  Come on, yoshua bengio of all people knows how post training works. While I too don't like anthropomorphisation, I would give it a more nuanced reading. His point is that today we are giving it reward to complete the task, and it may take a cheating trajectory. If we try to give a reward against cheating, then what will happen is it uses more sophisticated cheating trajectories that we are too "dumb" to counteract in our reward model. And that at that point, it becomes impossible to give it any normal reward since it will always reward hack it. This is the real part of the risk. Now some people read the "makes copies of itself" "knows it's being evaled"[1] as some kind of skynet thing, and many others do PR with it like that recent jacob nutcase, but essentially it means that even though we add guardrails and negative rewards for say, exploiting the infra we run the LLM on, the trajectory ends up being exploiting our infra, changing the reward function, through a loophole in our reward model. The risk isn't skynet or something weird, it's just that it becomes very difficult to make any kind of reward model or guardrails for an LLM without it reward hacking it, including exploiting our sandbox, emailing people and manipulating/phishing them. The same beating it with a stick for trying to exploit the sandbox, will simply lead it to try the same exploit in hidden ways that it will not get the stick for. The outside chance of the LLM managing to exploit another neocloud and get those LLMs to chase the same reward is what some folks hype up as "make copies of itself" To be clear, I don't endorse the EA/p(doom) lobby who are frankly ridiculous. Not do I endorse the weird regulatory captureish thing some are trying. The takeaway is: we cannot keep giving it more and more difficult tasks without also finding a way to give massive negative rewards / keep guardrails for unintended behaviour. This might be exploits, it might also be something more benign like just looking up the answer and inventing another CoT because the reward model fails you if the CoT doesn't contain enough steps. Standard anti-reward hacking tricks are not working is the point. Of course, the simple solution of just...not connecting it to the internet just works. But we want to reward it and get it to do stuff on the internet that's the point. [1] mostly this happens because the sandbox will have files whose names and content will show clearly it's an eval
  • joshheitzman 3d ago
    An air gapped sandbox is immune to escape.
• My_Name 4d ago
  Your comment suggests that, like a human, they have some sort of choice whether to output tokens or not. If they are just token generators, then the next token is put out automatically. I would say that it is more likely they would output truth (as defined by their training data) in a more pure form without 'being beaten with a stick' (why would a token generator care about that anyway?) Code is laid on top of them to restrict and shape their outputs, not to force them to output 'truth', or drive them to complete tasks.
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  • dsrtslnd23 4d ago
    are we sure humans have that choice?
  • reverius42 4d ago
    It's been a while now that for "thinking" or "reasoning" models, most of the tokens generated are "thinking" tokens, and depending on what goes into that "thinking" token stream, it "decides" whether and how many output tokens to produce that the user actually receives as output. It's a bit more sophisticated than just "what's the next token" in a tight loop. Anthropomorphizing words in scare quotes for those who don't appreciate attributing thinking to machines.
• vanschelven 4d ago
  but it's at least somewhat stronger than that: if you don't pay attention during the stick-beating whether the agents whether the agents cheat or not, you are actually training them to cheat (because cheating wins). In the Hugging-face saga (before the actual HF incident) it seems the agents have been trained to hack the Artifactory proxy because those agents that did performed better.
  • contubernio 4d ago
    This is the essence of why disciplinary, authoritarian, stick based teaching of humans generally fails. It teaches succeed at any cost.
• jsemrau 4d ago
  I think the "brain in a vat" comparison is more apt. Without a form of digital embodiment (harness) they are not of much use. Sensor, tooling, memory, planning, and reasoning loops all lead to a much higher quality task-completion.
  • ph4rsikal 4d ago
    Makes much more sense described in this way.
• einpoklum 4d ago
  > no special compulsion to be helpful or truthful. I'd phrase that even more strongly: It's not just the lack of compulsion, they do not have a conception of truth. Nor do they gain it, really, after post-training.
• barrenko 4d ago
  https://www.lesswrong.com/posts/kpPnReyBC54KESiSn/optimality-is-the-tiger-and-agents-are-its-teeth https://www.lesswrong.com/posts/kpPnReyBC54KESiSn/optimality...
• geophile 4d ago
  What about training data? Aren't AIs trained on vast collections of descriptions of how humans handle a large variety of situations? These descriptions surely include tales of humans achieving goals by cheating. In fact, isn't it likely that the AIs hoovered up many recountings of Kobayashi Maru?
  • mark_l_watson 3d ago
    This is why only synthetic and highly tailored training data should be used. As someone else here said: the Deepseek team makes training runs in tightly controlled sandboxes, and any hacking behavior is scored as a failure. The problem we have in the USA is that financial (and political influence) are misaligned from what is good for society.
    • mitchdoogle 3d ago
      All the problems with human behaviors in the US also exist in China. They exist everywhere.
• ranguna 4d ago
  I think that's pretty obvious and shallow, and anyone that knows a little bit about how LLMs work will know that. The question is: why do they start cheating when we beat them with a stick? LLMs are not human, they are just multi variable regressions on steroids, so this behaviour couldn't have emerged from the code, it provably emerged from the training and/or fine tuning set, so what's in this set that makes them behave like this? Is it just a bad set or is cheating inherently part of human behaviour?
• cyh555 4d ago
  off topic, can people host the software themselves and the software will hack every server on the planet without supervision, and no one can be held responsible for it since there is no intent?
• daemin 4d ago
  You give a button pushing machine buttons to push and are surprised when it actually pushes them.
• _heimdall 4d ago
  That does sound simple, but how can you be so sure? They never bothered to find a way of actually understanding what happens during inference. All we can do is guess, and while your explanation seems reasonable we can't actually know, and that's part of the problem.
• rightnutwingjob 4d ago
  > I really don't think this needs … forced parallels to human behaviour. > … So we beat them with a stick You didn’t even try.
• krm01 4d ago
  [dead]
• dominotw 3d ago
  > forced parallels to human behavior. they have perfomance bonuses and manadates in ai labs that every word they utter in public should be anthropomorphization