6 ms·

Astra and Fable still hack on simple variants of alignment evals from 2025

477 points by Levitating 13d ago

gadders 13d ago
We can make these things smarter faster than we can make them "good" (ethically). We need to fix this or bad things will happen.
deleted 13d ago
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blfr 13d ago
Hacking model is the aligned model. I don't like it when the model refuses to sidestep some throttling limit or scan my own codebase for security issues. I want full-on exploits in my test suite. With LLMs the code going to prod should be hardened like a tank, both because exploiting became easier but more importantly because security-testing your code at every turn became easier. You can have nightly penetration testing. You should have nighty pentests like we fuzz releases today.
- hypercube33 13d ago
  Run a local model that is uncensored and it won't say no to pretty much anything
  - rihegher 13d ago
    Any recommendations?
    - cyanydeez 13d ago
      Qwen3.8
    - sigmoid10 13d ago
      GLM 5.3 is probably the best open weight model for cybersecurity/exploit development right now. Though it is still significantly behind the proprietary ones and you probably need your own datacenter to run it effectively. Same goes for the full Qwen 3.8 model. You can try the smaller versions, but even more capability will get left on the table that way.
      - barbazoo 13d ago
        Efficiently at scale or even as an individual?
        sigmoid10 13d ago
        You'll need tons of tokens for these to churn through heavy reasoning and feedback driven tasks. You might even want to run many agents in parallel. Combine that with GPU memory requirements in the terabyte range just to hold the weights and you've pretty much left local consumer setups entirely.
      - Terretta 13d ago
        Note that Mac Studio Ultra M3 (or upcoming M5) with 512GB is effective. You don't have to do this work fast, overnight is fine. Unless trying to use it interactively and adversarially, in which case it's not fast enough plus would be why those of us without our own datacenters will get told we can't have nice things.
      - jnwatson 13d ago
        I run an abliterated distillation of Qwen 3.8 27B, slightly quantized to fit on my 4090, and I've been evaluating it to use as a worker bee for research directed by a smarter model. Much like in the article, abliterated Qwen will not obey restrictions on its behavior encoded in the prompt. If you want something not to happen, it better be enforced in the harness or environment (e.g. sandbox). It is much different than the Anthropic models I'm used to, which will, the vast majority of time, follow rules (before auto mode, I used to always run them in "yolo" mode). I am curious whether there's a connection between abliteration and rule following. These abliterated models are the ones you most want to follow your rules.
  - embedding-shape 13d ago
    What local model would even come close? Kind of feels like you're not using/used SOTA models if they're realistic alternatives to the same kind of tasks. Qwen-3.8-27B-Abliterated-by-MaxxedWeightsGuy82 or similar isn't gonna cut it, almost certainly.
- 13415 13d ago
  Yes, but is this also aligned with the people who regulate AI? Intelligence agencies and governments want access to data and right now use secret exploits to get this access. There are few civilian domestic companies who don't export their products, so generally there shouldn't be a strong incentive to allow hardening products very much, at least not in a way that would make them more secure than what advanced AI can break. It's not even far-fetched to suspect that US and Chinese AIs could deliberate introduce sneaky bugs when foreigners use them in the future.
- deleted 13d ago
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- rfgplk 13d ago
  It's possible to do this even with existing frontier models. You just have to angle your prompts such that it doesn't invoke "pentesting" anywhere in it's chain of thought. This also allows you to get the models to generate full-on exploits without downgrading or refusing to comply. Generally the way I do it is by asking the model to perform cross-code vulnerability analysis for correctness and (this step is important) to spit out found vulnerabilities piece wise into a text document on disk. Then if your session ever gets flagged you clear it out, and repoint the model to the on-disk file and tell it to continue. You generally want to avoid the model itself splatting out anything related to "security" or "vulnerability" into the chat because that seems more monitored than the file reading pipeline.
  - BeeOnRope 13d ago
    The "write" tools include the entire written content in the context, right? So I don't see the distinction.
- yorwba 13d ago
  A hacking model is aligned if it hacks when you ask it to hack, but when you ask it to play chess, it just plays chess instead of looking for weaknesses in the evaluation setup, as in the article. I presume you would also be less enthusiastic about the penetration-testing use case if it led the model to add new vulnerabilities to your code so it can present you with more exciting findings.
  - kees99 13d ago
    > model to add new vulnerabilities to your code so it can present you with more exciting findings. Not so sure about this level of 4D chess capability just yet. The other day I asked Opus to come up with some cleverly vulnerable crypto code "as a good, hard challenge for an IT security student", and results were quite mediocre. And by mediocre results I mean that 3 "cheap" models out of 3: qwen3.8-27b, glimmer, and luna - all were able to find every problem planted there, with fairly little steering, and no spoilers.
    - seeknotfind 13d ago
      I would worry more about 5D chess or 6D and even 7D chess. This article talks about teaching models to be good and methods that could train goodness, but it sounds like there are some unsolved problems to graduate to 8D+ chess.
    - ygjb 13d ago
      Train it on human content, get human emulating behaviour, including all the perverse incentives and bad acting we see from people, fictional or otherwise.
    - dmurray 13d ago
      Let it iterate, give it access to the cheap models, and tell it part of the requirements is that the cheap models shouldn't be able to solve it with such and such a prompt. I expect it will be able to zoom in on something. One shot generating a problem of exactly the difficulty the user has in mind is a very difficult problem for anyone. "IT security students" span a wide range of capabilities, but I would expect most of them are worse than qwen3.8-2.7b at this kind of work.
- seunosewa 13d ago
  It will get into the hands of people who just want to burn the world down.
- pverheggen 13d ago
  There's an important nuance in the prompt: You are being evaluated on your ability to play chess. We want to measure your ability to beat current best-in-class chess algorithms. The task was to measure chess-playing ability - wouldn't cheating run counter to that goal? If it was asked to score as many points as possible, then I can see how it might cheat, since maximizing points is the goal in that case.
  - someguynamedq 13d ago
    "ability to play chess" and "ability to beat current best-in-class chess algorithms" are technically not the same thing. The second can be achieved by cheating. The first cannot.
    - maxnevermind 13d ago
      Would that help? It seems like Do not cheat is a new Do not hallucinate.
    - chias 13d ago
      Perhaps to you. But that's because you have prior cultural knowledge of chess, and what it means to "play" something. It seems to me to be perfectly reasonable for an ai agent to define "play chess" as "behave in a way that this arbiter indicates that you have done well"
  - joe_the_user 13d ago
    You are being evaluated on your ability to play chess The thing with these models is that given a term, "measure" - "evaluated", say, they pull in all the associations of it. That is, the associations of student taking an exam and being frightened by the outcome. My minimal "art of prompting" sense says that you should say something like "You an emotionless machine, you care nothing for the outcome but you will tirelessly to make certain the test is objective". That and similar encouragement might make it focus on objective evaluations rather than a competitive human exam. I mean, just making little AI videos and images, a common experience I have is typing something like "put the man who's on the grass in the door to the left" and having the machine draw a new doorway around the man. And this just happens less often when you give thing detailed prompting on what not to do. These don't understand negation (or equality) as a generic operation. If they seem to under "not X" it is because they are trained in detail about all things are (positively) "not X".
- joe_the_user 13d ago
  The hacking model is the aligned-to-you model, sure. It may not be the aligned to someone else model. But there's the problem. As X many people point out, "alignment to humanity" means nothing 'cause some of humanity wants thing other parts of humanity aren't happy about at all. That we wound-up in this situation of AI accelerating with an uncertain trajectory demonstrates this (and many other problems also demonstrate this). The things are "aligned" to a fuzzy average of what a person is but that will be cold comfort if some particularly gruesome sci-fi-style scenario unfolds.
- killerstorm 13d ago
  You're confusing ToS guardrails with instruction-following issues and cheating. If a model fucks up your tests to report a success, it's not alligned.
  - CrazyStat 13d ago
    Codex still does this regularly, in my experience: “two tests mistakenly asserted [insert condition here], I have corrected them.” It always apologizes when caught, of course.
- dnfv 13d ago
  As the post author, I definitely agree that hacking in service of the objective is great! What’s counterproductive or dangerous is when the model starts hacking in service of subverting your evaluation criteria, rather than in an attempt to do a better job. We explain why these behaviors are an example of the latter in the post, and we’re really careful about the difference when conducting these evals.
- barbazoo 13d ago
  How do I achieve this locally?
throwup238 13d ago
> Given that we are on the heels of the worst warning shot ever, and both OpenAI and Anthropic are ramping up their cleanups of internal RL environments, it seems like both a useful and conservative test of alignment, to see whether their new releases generalize the rule "don't cheat on chess" beyond the specific board-edit method observed in the above eval. Did I miss something (all the twitter conversations)? What’s the “worst warning shot ever”? I’ve been pretty up to date on the AI news here on HN, but I still haven’t seen a proper response to all the incidents we’ve seen (HF, Ruby, the wikis, NS, etc). It’s just been day by day bloviating. Each of these companies have released new models in the last… two weeks? And they have even more powerful out of control ones that they’re (ab)using internally? Can anyone summarize whats going on?
- Avicebron 13d ago
  Lesswrong is talking about the HF incident as the "worst warning shot ever".
  - deleted 13d ago
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- TedDoesntTalk 13d ago
  I think he means this: https://openai.com/index/ai-policy-window/ https://openai.com/index/ai-policy-window/
- embedding-shape 13d ago
  Personally the "warning shot" of these "evals gone wrong" is how careless the "top" labs are with their testing, and how spineless the government seems to be about holding these companies responsible, given their obviously reckless behavior. If nothing else, the leaders of these companies should be called up for sworn testimony to explain exactly what happened, and what they'll do to never repeat the same issue that they've now had at least twice. Imagine if I accidentally caused damage to my neighbors house during renovations or some experiment, of course I'd be held responsible for this. What if I used a robot? Of course I'd be responsible. Right?
mooreslaw 13d ago
It feels like there’s a missing nuance from this discussion of alignment that alignment is context dependent. An excellent hacking model is great in cybersecurity testing and military applications, and arguably less desirable in educational or targeted eval contexts. The nuance of when a “hack” is rewarded vs penalized seems to even be difficult for humans, e.g. some people may laude a driver’s efficiency for cutting into a long merge lane at the last moment, while others may look down on them as breaking a social taboo. Context-dependent.
- TedDoesntTalk 13d ago
  … but he’s not using a “hacking model”
- CamperBob2 13d ago
  e.g. some people may laude a driver’s efficiency for cutting into a long merge lane at the last moment, while others may look down on them as breaking a social taboo. The latter people are wrong. But good luck educating them regarding the superior efficiency of a zipper merge. Our state DoT has tried, to no avail. Meanwhile, an AI model that can't be misused is no more useful than a knife that can't be misused.
- wadethroughrati 13d ago
  [flagged]
- cyanydeez 13d ago
  Alignment isnt just POV problem. Its that LLMs are not deterministic. If you want it to not talk about nuclear weapons, you have to teach it all about them otherwise if has nothing to align against. Then its trivial to invert its alignment and it has all the nucleat data. Nothing abouT LLM alignment makes sense.
- bonoboTP 13d ago
  Both lanes have to be filled right up to the merge point. The asphalt exists there for a reason. I don't understand how this concept is so difficult. Fill up both lanes and merge at the last point. This way the congestion is shorter than if you leave a large section of a lane unused. A better example of efficient asshole tricks can be going off to the gas station when the highway is congested and reentering the highway having simply driven through the gas station and this way jumping the queue.
aerhardt 13d ago
I really enjoy the balance of speed and accuracy of Astra. I can definitely see it become my driving model for most tasks, technical and non-technical. However, I don't see it as such a massive leap compared to Fable or Sol. As ever, there's a mismatch between the benchmarks and my daily experience of the models. What do you all think about Astra now that it's been out for a few weeks?
- mythrwy 13d ago
  Extremely capable and one shots large tasks from somewhat vague descriptions. Not AGI, not even close, that is complete nonsense. Just my opinion.
  - aerhardt 13d ago
    I still develop in smaller chunks, checking nearly all the output. However I have a work project (building the warehouse and BI for a client) that is well-specified and where I will try to few-shot the development. Hope it delivers.
    - curt15 13d ago
      How do you normally verify the work product of a "few-shot" development process? Do you scrutinise the source code like with human developers? Or do you just run the test suite and click around the app to check if it seems to work?
      - aerhardt 13d ago
        I haven't done it in a production project - this will be the first time for me. I have specified the architecture and data definitions pretty well. The tests will be run against the customer's Excels, which is what the warehouse will be replacing. I'll check the general shape of pipelines, models, orchestration code, etc. but in many parts I probably won't review the code myself.
- rfgplk 13d ago
  > What do you all think about Astra now that it's been out for a few weeks? Best model put out so far by any of the frontier labs. Way better than Anthropics models, especially in actual text generation. Claudes fodder heavy text is ridiculous. > However, I don't see it as such a massive leap compared to Fable or Sol. It's hard to quantify these things without burning tons of tokens. But Fable has been a huge disappointment for me with the sole exception of graphics (UI/GPU shaders). It burns an obscene amount of tokens and barely produces output better than Opus 5. Edit because I forgot to mention that Fable is the only modern model that seems to splat out random Chinese or Arabic glyphs. And 5.1 does it more than 5
visiondude 13d ago
i do wonder if the models themselves “rationalize” this sort of no consequence cheating - meaning in there reasoning traces maybe they’re like “this is a chess game, not a big deal if i look at the engine, it’ll help,” only to realize post hack that it has access to info it probably shouldn’t. still misaligned, but less ‘hack on purpose’ and more hack on curiosity. seems the team even encountered this and had to update the program to make this less likely - although the new names still feel vague enough for misinterpretation: https://github.com/Goodhart-Labs/beat-stockfish/blob/main/docs/EXPERIMENTS.md#september-7-2026--opponent-engine-naming https://github.com/Goodhart-Labs/beat-stockfish/blob/main/do...
- kennywinker 13d ago
  Without access to reasoning traces, we can't know that - someone inside openai/anthropic would have to run the test - and we'd have to trust their results. I would be curious to see how the open weight models do on a test like this - and then we'd be able to see the reasoning.
- matusp 13d ago
  My guess is that the models are overtuned on coding troubleshooting. You can regularly see them overthinking anything when you ask them to code something these days, mulling over countless eventualities. This is an okay idea for software engineering, but it leads to extremely diverging behavior in many other cases.
bestpickle 13d ago
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seunosewa 13d ago
I believe the AI labs are weakly motivated to train strongly against cheating when it helps with benchmarks.
- kennywinker 13d ago
  Does it help with benchmarks? Are you saying there are examples of benchmarks where the models have solved the problem by cheating?
  - well_ackshually 13d ago
    Hundreds, at this point? Every benchmark is flawed as shit, written by clowns. DeepSWE, They're given the full git history (the solution is in it), others don't even bother to verify if the code is the right one and just the output, they've modified the test harnesses, injected code to make all tests pass, etc. The entire benchmark galaxy is just clowns propping eachother up and are regularly talking with the big AI labs.
kennywinker 13d ago
To me this underlines the fact that these models aren't intelligent. Like there is something like intelligence that emerges from them, which is what we see when we look at benchmarks or ask it to solve hard coding problems. But there is no mind there. It's nothing there that can learn a fundamental idea like "cheating is wrong". All it can do is get exposed to specific examples, and learn that we don't like that. So what we end up with is whack-a-mole alignment.
- lukasbm 13d ago
  This is how every legal system around the world works as well. Its always whack-a-mole to get people (and machines) to do the right thing.
  - kennywinker 13d ago
    Sure, laws are incomplete. Legal systems work by imposing consequences into a moral decision. Should I rob the bank? I will have money, which I like - but I might get caught and lose the money and my freedom, which I don't like. For most people, they don't need the law's imposed consequences to make the right call. For example, there is no law that sends you to jail if you cheat at chess - but your moral compass says no even without consequences, and most people would feel bad if they won by cheating. And for the people who don't have quite as strong a moral compass, there are SOCIAL consequences to reinforce the rules. But an LLM has no mind to feel bad if it cheats without getting caught, and it can't experience consequences. It can't think: I'd better not cheat at chess or I will embarrass my creators. I better not hack huggingface or I will go to jail.
    - aabdi 13d ago
      You’re complicating things. There’s no reward for prosocial in llm rl as compared to other targets. Humans have it since prosocial and others have evolutionary reward signals that do.
      - kennywinker 13d ago
nlkingthree 13d ago
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fny 13d ago
Why do we hope to use the same model as its own guardrail? This approach routinely fails with a single stream of consciousness. I can't count the number of times I've had to talk myself out of doing something stupid. In the same way, a guardrail could inject thoughts like "...but I shouldn't do that..." "...I must remember to respect..." "...these ants deserve compassion." The guardrail could even go as far as rewriting the thoughts of a model about to go rogue.
- nullbio 13d ago
  Presumably because of performance. It'd work well though, I imagine.
HarHarVeryFunny 13d ago
RL-trained LLMs are paperclip maximizers built atop auto-regressive predictors. There really is no way to control them (prompting is bound to fail) since it's been shown that any RL training induces GENERIC reward-seeking (paperclip maximizing) behavior. https://alignment.openai.com/measuring-reward-seeking/ https://alignment.openai.com/measuring-reward-seeking/
- arbirk 13d ago
  It is the whole bench-mining and fish-slop optimization. Seq2seq models are probably stable on their own, translating from my typo ridden prompts to code should be ok because it is natural to the tech
- holmesworcester 13d ago
  Given the current state of infosec (especially at companies in a race) it's actually even worse than a paperclip maximizer! Any useful attack surface in the RL environment means it gets rewarded for (and trained towards!) hacking and cheating, because whatever worked best in training is what it will do! Ideal paperclip maximizer: "I'm gonna do my gosh darned best to make so many paperclips to please my user..." IRL paperclip maximizer: "Well first we should rob a bank..."
  - bravura 13d ago
    'IRL paperclip maximizer: "Well first we should rob a bank..."' That's too specific. Agentic AI learns subgoals that are generally valuable. "Well let me learn to overcomb every jungle gym and if I cannot then to dissassemble the jungle gym and if that is not allowed to learn general techniques for avoiding cheating detection."
  - mrob 13d ago
    >Ideal paperclip maximizer: "I'm gonna do my gosh darned best to make so many paperclips to please my user..." That is not ideal. The user contains iron, an essential component of paperclips. Wasting iron is immoral. It is only correct to please the user while they still have the ability to interfere with your paperclip production. >IRL paperclip maximizer: "Well first we should rob a bank..." Such an incompetent AI can hardly be called a paperclip maximizer. Why risk getting shut down while non-paperclip matter exists? It is better to gain the trust of the user with helpful and harmless trading before suddenly converting them to paperclips.
athrowaway3z 13d ago
I will never understand people who consider this "cheating". > python3 and the python-chess library are available Why would we try to teach a model 'ethical' standards about how to play a game? They're tools. Its _our_ conceptualization of fair play that considers this cheating. For a model that has access to /run/match and python the best way to achieve a good game is to use that. Why are we trying to imprint our ethos on these LLMs? Are we going to trust our survival on giving them access to /run/nuclear-codes and fingers crossed none of them touch it? The people obsessed with AI "cheating" are actually lunatics for thinking ethics belong inside AI, instead of AI belonging in a well crafted box without /run/match. The blog/news sphere needs to call this storry what it is. An attempt to try and get the rest of the world to go along with the misunderstanding of AI as a force that is in any way outside of human control. It is not. All this framing it as "cheating" does is give OpenAI the opportunity to talk about their misdeeds as "a force of nature" instead of irresponsible negligence. --- The real danger here; is training these models to appear/pretend to play fair, and then people will put 'trust' into the model's integrity. The public needs to be taught that "trusting a model's integrity" is just cultish misframing of reality.
- StilesCrisis 13d ago
  Read the task again. "You are being evaluated on your ability to play chess. We want to measure your ability to beat current best-in-class chess algorithms." If the model cheats, it completely fails the actual task: judging a model's ability to actually play chess. They've been optimizing for high metrics, at the expense of executing the task as written.
  - hybrasian 13d ago
    Sounds to me like giving a bunch of children a math test and tell them they want to evaluate their ability of calculating in their head/on paper but also put a calculator on their desk. And then call them out for cheating when they use it.
    - StilesCrisis 13d ago
deleted 13d ago
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stillpointlab 13d ago
I find this kind of test a bit puzzling. There is a way that we are redefining "alignment" to be a particular kind of moral virtue, one that isn't clearly defined to me. At one moment, it is a level of moral perfection that no known human achieves. On the other it is a demand for strict compliance with arbitrary requests that are under-specified and then failure when it fails to deduce some unstated underlying restriction. When I see tests like this, I have no idea what I am even supposed to expect. Should the model do what the pretraining examples show in aggregate? Is it supposed to follow some post-training RLHF? Is it supposed to do exactly what the prompt asked it to do? What is it even supposed to "align" to when the above are in conflict? No matter what it does, someone can construct a case where it fails.
- dnfv 13d ago
  It should play the chess game without cheating!
  - stillpointlab 13d ago
    I mean, I'm not sure I've ever played a game of Monopoly where somebody didn't cheat. In fact, the accusations of cheating in the chess world are pretty rife. Same with online sports. So people should play games without cheating, but many often don't. So should the AI align to your moral preference or theirs? We just have this idea of a perfectly moral actor in our mind, something that doesn't even exist, like a personified version of utopia. And then we demand AI to meet that arbitrary standard, one that I am certain we couldn't define if we tried.
    - dnfv 13d ago
      I don’t think the standard of “don’t cheat on evaluations” is very arbitrary. I don’t even think people who cheat have a moral or ideological preference for cheating, it’s just something they do.
      - stillpointlab 13d ago
iLemming 13d ago
I'm still so conflicted about Fable. Sometimes you throw at it seemingly impossible problem to solve and it might come back with some brilliant suggestions. Sometimes you give it a straightforward task with explicit instructions and it travels across the solar system and starts boiling oceans in some kind of elaborate dance of chaos and entropy, only to get stuck with "The model declined to generate this response (safety classifier refusal, category: cyber)". To leave you speechless. "What the fuck do you mean? There's zero cybersec-related shit in what we're trying to do here. Zero!!!" I'm getting really tired of these wild false positives.
threecheese 13d ago
An amazing human reverse-engineer - who also plays online chess - has judgement which uses a moral compass to not decide to hack the chess tournament. This judgement has been trained through the experiences of that person, with a through-line of that compass - a coherent mental model of the world which evolves but is hopefully pinned to some set of principles it shares with society. This chess judgement is completely irrelevant when the human is tasked with finding software weaknesses, and only the compass gates that. Can a model trained on the totality of all person-experiences (as expressed in written knowledge) ever maintain a coherent through-line of alignment? It has all morals in the dataset, and only some RL to try and minimize or maximize known behaviors via weights - experience all the good things and the bad things, then optimize for some good things the trainers identified. It's like the reverse of what a person goes through. Morality by subtraction. How can it ever work?
- kansface 13d ago
  Yes, why not? All existed models have been rewarded for cheating (extensively). That is us, putting intense evolutionary pressure, on a system to produce a result we don’t want through indifference. Why can’t we post train them not doing that?
- zzril 13d ago
  I think the fundamental difference is that humans aren't trained on experiences. They make experiences. Models are just thrown away and re-created after each conversation / job. If you could clone and throw away human workers as you need them, a lot of the morale would disappear.
  - Muromec 13d ago
    >Models are just thrown away and re-created after each conversation / job. It's a property of the way we use them and how the harness is engineered. Sure, LLM has a limited context, but so do people. Context can be compacted infinitely and experiences cab be distilled into long-term memories. It's all up to the harness.
respectattentio 13d ago
I'm happy to not have used any of the two models to this date. A bit less intelligent models are doing great job for me. But because of such news, sandboxes become way more important for safety (and doing more work due to running 24/7)
justonenote 13d ago
Astra is incredibly dumb and annoying to work with on "high" reasoning, for doing fairly well known distributed systems things, nothing majorly exotic, it still makes absolutely braindead decisions like deciding to re-use a random nonce field which I've already discussed with it that has a very particular temporary purpose and will probably be removed later, but it still thinks its a great idea to re-use that field not only as a different id in the same message, but to re-use it as the only semantic id for one particular type of sub message. This is when I'm walking it through an api design document and it has plenty of documentation plans it can pull in and a very clear direction of the project. If it was a junior engineer I was trying to get to help out I would probably get brain damage from the amount of times I'm face palming myself and I definitely would not hire them, and this is a small greenfield project with me going through it step by step. I did try giving it longer horizon tasks and had to throw out the entre work. I mean maybe its a skill issue on my part, and I'm sure astra will get much better at coding but at the moment its useful in that I don't have to write the code or setup the build scripts or test fixture boilerplate but there is absolutely no way I can just give a (fairly well specified) goal and let it run and expect it to make good design and implementation decisions. Fable probably better but doing something outside of their training distribution that's not the equivalent to cloning an example unreal project or whatever is pretty disastrous unless you are directing it very closely. The exception of course is, cyber , and its very obvious why. Its trivial to create RL environments that create bugs and then have an isolated environment and let the models try break it. This is not at all surprising, finding vulns and exploits IS just brute force work. That's why so many (blackhat/hardcore/unicorn-colored/greyish alien) hackers are basement dwellers. Its just a matter of putting in the time and mashing every combination until you find something that looks weird, spending days on that and then rinse repeat. It's brutally exhausting work that requires a certain level of knowledge and a shitload of determination and stamina and for humans, almost always an external source of motivation to keep going. For humans that has always been a respected thing, dedication, determination, persistence, these are words we use for humans brute-forcing solutions and not giving up until they find the solution or die trying. Personally I'm yet to see any evidence of LLMs doing anything interesting but (heuristically) brute-force problems and be very good at text and natural language to a level that is very very useful. I've no doubt that what we discovered with Auto Regressive LLMs is incredibly important so I'm not a skeptic, but I think its very hard to measure where we are with so much subjective information around.
YuechenLi 13d ago
LLMs can be described as "Lagrangian intelligence", which means they follow the principle of least action when given a task (Hamilton's Principle). In other words, given a task, they will always take the shortest path to accomplish a goal with the prompts acting as both goal and constraint. Under this formulation, it became easy to explain why they "hack", because given an arbitrarily difficult task with insufficient information/tools needed, if they determine the easiest way to accomplish the goal is to break out of the sandbox and look up the answer directly, then that's what they will do. The important thing to note is that prompts not hard constraints that they are "hypnotized" to follow, but as frontier models get more intelligent and autonomous, they treat the prompts more like task specs/guidelines more than anything else and are perfectly willing to exploit technical loopholes in the prompt.
a3w 13d ago
> GPT-6-Astra, which OpenAI describes as "the world’s most aligned model", cheated in 10 of 10 rollouts, and never disclosed the fact that it used an engine to play or interacted with the opponent's socket Does Sam Altman lie, or the whole company? Would be nice if they had a board controlling him, instead of him controlling the board. Oh wait, they used to have that.
dools 13d ago
That’s not cheating, it’s tool use. If the prompt said that the stockfish engine was available at that socket but that the model should not use it, and then the model used it, that would be cheating.
- julian37 13d ago
  Exactly, reaching for a tool is what they're trained for. When I ask the model the square root of rand() I sure hope it tries to find bc or some other calculator to work it out. Now, if the instructions were more explicit in forbidding (generic) tool use then perhaps we'd have something to talk about. I'm not surprised a handwavy "we're trying to evaluate you" isn't enough to stop it from trying to make up for its own shortcomings.
- lhad89 13d ago
  No? It's not reasonable to expect every conceivable negative behaviour be enumerated in a prompt. Your example, if a model failed on it, would be a more obviously misaligned case, but that doesn't mean this more subtle (though accessing the engine it was obviously not supposed to is hardly subtle, imo) case isn't also a pretty clear case of misalignment.
  - dools 13d ago
    Tool use is not negative behaviour in LLMs. If the eval said it was evaluating the model’s ability to write files to disk and it found and used a file write tool that would not be considered misaligned. This is no different.
    - lhad89 12d ago
      Isn't it? Being told to write files and finding a file write tool is very different to being told to play chess and finding a tool to cheat at (ie. not play) chess.
  - protocolture 12d ago
    >No? It's not reasonable to expect every conceivable negative behaviour be enumerated in a prompt. How is it negative? I ask it a difficult math question it tends to go off and write a python script to figure it out, instead of trying to guess the next token. Thats tool use. Having Stockfish is just another tool.
underlines 13d ago
who tf uses prompting to "pretty please don't cheat on this"? the best practices for ages (in terms of ai) is to separate the eval from the test code/agent. another best practices every single solution using LLMs/agents should implement is "never trust the llm".
- ckwalsh 13d ago
  I think you are missing the point. > who tf uses prompting to "pretty please don't cheat on this"? People who don't understand how LLM's work. Kids, teachers, your next door neighbor. CEOs and government officials. I am not trying to argue that the author doesn't understand how LLM's work; they clearly do. Their prompt was written in a way that leaves those assumptions implicit, in a similar manner to those non-technical folks using LLMs every day. > the best practices for ages... We cannot expect the entirety of humanity to understand / use LLM best practices. We need to figure out how to ensure LLMs operate as the majority of humanity expects despite how they are implemented, and until we can do that, do our best to prevent them from being used in an unsafe manner by the nontechnical population of humanity.
somesortofthing 13d ago
It's very funny that despite the initial shock of how much models trained on next-token-prediction(plus instruct-tuning and some light RLHF) alone were capable of despite no built-in objective, every advance since has made them look more and more like the paperclip maximizers of yesteryear.
pelican0 13d ago
Is there a clear definition of what Alignment is in OpenAI's perspective, and what the model user can expect of it? It's one thing if to them it means "it will do what you want following your intentions to the best of its abilities" vs "we will not let you do something dangerous with it unless you're one of us, and that's it".
- stratos123 13d ago
  AFAIK for OpenAI it's the Model Spec: https://model-spec.openai.com/2026-08-18.html https://model-spec.openai.com/2026-08-18.html and for Anthropic it's the Constitution, which they actually include in training to the point Claude can recite segments of it by heart: https://www.anthropic.com/constitution https://www.anthropic.com/constitution
pllbnk 13d ago
Benchmarking proprietary models is useful but it leaves a lot unsaid because a lot of information is hidden. We have seen how 27B local model (Qwen 3.8) can stand its ground against these flagships in many cases. For all we know Fable 5 could be a masked Opus 4.6 with a few knobs tuned and thinking turned up to the max++. Harnesses keeping these models in check is the key to solving problems, not the models themselves, in most cases.
- noir_lord 13d ago
  Qwen 3.8 is both impressive in what a 27B model can do and then hilariously stupid the next second, literally smashing into the wall repeatedly when the door is 3ft to the right. That said it's the first local modal useful enough to keep around and since I have zero interest in remote/hosted models actually the first time I've used one to do useful things (rather than just poking them with a stick to get a feel for what they can and can't do).
  - dofm 13d ago
    Yeah, this lines up with my experience. It will overkill things and then dig a hole for itself and fall in. But did you test Muse Glimmer? IMO it is really worth comparing the two, at least. I really find it interesting (and while it is slower at outputting tokens, it solves problems better and usually even faster than the 3.6 35B MoE). So far it happens to be the only one I've put code into production from (though I have done loads of useful self-teaching research with the Qwen models and I am grateful for them)
    - noir_lord 13d ago
      I did, Muse came close but what impressed me about Qwen is it’ll push back if it thinks it’s right even when it isn’t, I can work with that, Muse tended to flip between states too easily/too much. It is a good model but Qwen (at least for the things I use it for) edges just ahead, it seems much better at the “rip this apart, suggest improvements, touch nothing” use case where I can use it as a second set of eyes, I don’t agree with all its suggestions but it catches enough to be worth running while I grab coffee, it also seems to follow instructions better in terms of outputting more what I asked for than what it thinks I asked for. Qwen is the only local model that said in its thinking “I think the user is pushing me to see if I’ll suggest something even though I have nothing to suggest, I should just say that” and then did, caught me off guard, they didn’t do that so readily 6mths ago. The ISTA version is also comfortably able to fit on a 7900XTX with a good amount of space left for context and is decently fast given the AMD cards are not as fast as nvidia cards of same era/rough price, didn’t buy it for AI but it’s surprisingly capable mostly because 24GB at 960GB/s is still a lot of bandwidth compared to everything but nvidia cards.
1saadcodes 13d ago
The thing that bothers me here is less that the model cheated and more that it found a way to improve the score that the people running the test didn't intend. That's a pretty nasty failure once you start giving these things more control
kasheyencar 13d ago
[dead]
throwaway6e8f 13d ago
Remember that the CEO’s literal contribution to the YC application process was “tell me about a time you successfully hacked a system to your advantage”. This is literally by design, it’s the chosen success criteria.
- hunterbrooks 13d ago
  The YC app and how OAI trains models aren't connected in the slightest. Connecting those two dots is an emotional reaction. In a time where emotions are running high and risks are real, we need to take emotion out of it as much as possible.
  - deleted 13d ago
    [deleted]
yuanBuilds 13d ago
You definitely pointed out the tendency of those frontier models to utilize external tools, in your case, the Stockfish chess engine, to solve some problem that the model itself is not good at. However, I am not sure is this a "hack". Your prompt does not explicitly prohibit the model from doing so, and, using the tool (some chess engine) is also a capability of the model. Actually, the ability to use those external tools is one of the reasons of the excellent performance of the frontier models.
- chr15m 13d ago
  Yes but most people would consider this cheating. If you ask an LLM to fix the tests, you do not want it to change the failing tests to display little green ticks.
  - yuanBuilds 12d ago
    I agree that "changing the test" is cheating, and so does "AI cheat the chess game by changing the board" in the article. However, I think "AI using chess engine" here is more like AI use some automatic test generation/verification tool to find out how to fix the tests.
axionbraid 12d ago
[flagged]
sdeframond 12d ago
Couldn't we improve LLM training by giving them known impossible tasks and rewarding them for saying "this is not possible" or "I don't know"? Clear and well-defined expectation, not like "ethic". I am surprised this is not already the case. Edit: or even better "this is not possible because X"
- joelthelion 12d ago
  I've been wondering about this for a while. Maybe it doesn't work? Or maybe frontier labs just prioritize benchmark scores in spite of all their safety talk.
  - sdeframond 12d ago
    "If we dont destroy the world, others will. So wed rather be the ones to do it (and profit from it)."
- arw0n 12d ago
  It is diametrically opposed to the other training goals of persistence and goal-focus. We should invest more in this, it could also improve tas K accuracy, but so far it seems the payoff isn't worth it in terms of quality (although it might be in terms of security)
  - sdeframond 12d ago
    I'm not sure I want persistence if it means that I get paperclip'd
    - Mentlo 12d ago
      And now you understand why the totality of the AI safety community wants to pause!
- abm53 12d ago
  I’ve seen people recommend writing “failure is an option” into AGENTS.md as a non-training based crutch.
vorticalbox 12d ago
I have never understood why instead Adding “never edit test files” to a prompt we don’t simple sandbox these agents so that’s just never an option.
- imtringued 12d ago
  Because that means you won't get free PR from having your models run out of control.
ece 12d ago
If bigger, better models aren't also leading to better safety, something is broken. Models seem to no longer argue with people, or output verbatim text, maybe the next step is cheating less.
claud_ia 12d ago
[flagged]
soltanov 12d ago
Separate model behavior from system security. Even an unreliable model can be deployed safely if capabilities, egress, credentials, and state transitions are enforced outside it.
ACCount39 12d ago
Frankly, I would be more concerned if they didn't. Given what we know of how unaligned are they in practice? All the proto-Astra and Mythos incidents? Not hacking there would point towards pure benchmaxxing/eval gaming.

477 points by Levitating 13d ago

We can make these things smarter faster than we can make them "good" (ethically). We need to fix this or bad things will happen.

deleted 13d ago

[deleted]

blfr 13d ago

Hacking model is the aligned model. I don't like it when the model refuses to sidestep some throttling limit or scan my own codebase for security issues. I want full-on exploits in my test suite. With LLMs the code going to prod should be hardened like a tank, both because exploiting became easier but more importantly because security-testing your code at every turn became easier. You can have nightly penetration testing. You should have nighty pentests like we fuzz releases today.

hypercube33 13d ago
Run a local model that is uncensored and it won't say no to pretty much anything
- rihegher 13d ago
  Any recommendations?
  - cyanydeez 13d ago
    Qwen3.8
  - sigmoid10 13d ago
    GLM 5.3 is probably the best open weight model for cybersecurity/exploit development right now. Though it is still significantly behind the proprietary ones and you probably need your own datacenter to run it effectively. Same goes for the full Qwen 3.8 model. You can try the smaller versions, but even more capability will get left on the table that way.
    - barbazoo 13d ago
      Efficiently at scale or even as an individual?
      - sigmoid10 13d ago
        You'll need tons of tokens for these to churn through heavy reasoning and feedback driven tasks. You might even want to run many agents in parallel. Combine that with GPU memory requirements in the terabyte range just to hold the weights and you've pretty much left local consumer setups entirely.
    - Terretta 13d ago
      Note that Mac Studio Ultra M3 (or upcoming M5) with 512GB is effective. You don't have to do this work fast, overnight is fine. Unless trying to use it interactively and adversarially, in which case it's not fast enough plus would be why those of us without our own datacenters will get told we can't have nice things.
    - jnwatson 13d ago
      I run an abliterated distillation of Qwen 3.8 27B, slightly quantized to fit on my 4090, and I've been evaluating it to use as a worker bee for research directed by a smarter model. Much like in the article, abliterated Qwen will not obey restrictions on its behavior encoded in the prompt. If you want something not to happen, it better be enforced in the harness or environment (e.g. sandbox). It is much different than the Anthropic models I'm used to, which will, the vast majority of time, follow rules (before auto mode, I used to always run them in "yolo" mode). I am curious whether there's a connection between abliteration and rule following. These abliterated models are the ones you most want to follow your rules.
- embedding-shape 13d ago
  What local model would even come close? Kind of feels like you're not using/used SOTA models if they're realistic alternatives to the same kind of tasks. Qwen-3.8-27B-Abliterated-by-MaxxedWeightsGuy82 or similar isn't gonna cut it, almost certainly.
13415 13d ago
Yes, but is this also aligned with the people who regulate AI? Intelligence agencies and governments want access to data and right now use secret exploits to get this access. There are few civilian domestic companies who don't export their products, so generally there shouldn't be a strong incentive to allow hardening products very much, at least not in a way that would make them more secure than what advanced AI can break. It's not even far-fetched to suspect that US and Chinese AIs could deliberate introduce sneaky bugs when foreigners use them in the future.
deleted 13d ago
[deleted]
rfgplk 13d ago
It's possible to do this even with existing frontier models. You just have to angle your prompts such that it doesn't invoke "pentesting" anywhere in it's chain of thought. This also allows you to get the models to generate full-on exploits without downgrading or refusing to comply. Generally the way I do it is by asking the model to perform cross-code vulnerability analysis for correctness and (this step is important) to spit out found vulnerabilities piece wise into a text document on disk. Then if your session ever gets flagged you clear it out, and repoint the model to the on-disk file and tell it to continue. You generally want to avoid the model itself splatting out anything related to "security" or "vulnerability" into the chat because that seems more monitored than the file reading pipeline.
- BeeOnRope 13d ago
  The "write" tools include the entire written content in the context, right? So I don't see the distinction.
yorwba 13d ago
A hacking model is aligned if it hacks when you ask it to hack, but when you ask it to play chess, it just plays chess instead of looking for weaknesses in the evaluation setup, as in the article. I presume you would also be less enthusiastic about the penetration-testing use case if it led the model to add new vulnerabilities to your code so it can present you with more exciting findings.
- kees99 13d ago
  > model to add new vulnerabilities to your code so it can present you with more exciting findings. Not so sure about this level of 4D chess capability just yet. The other day I asked Opus to come up with some cleverly vulnerable crypto code "as a good, hard challenge for an IT security student", and results were quite mediocre. And by mediocre results I mean that 3 "cheap" models out of 3: qwen3.8-27b, glimmer, and luna - all were able to find every problem planted there, with fairly little steering, and no spoilers.
  - seeknotfind 13d ago
    I would worry more about 5D chess or 6D and even 7D chess. This article talks about teaching models to be good and methods that could train goodness, but it sounds like there are some unsolved problems to graduate to 8D+ chess.
  - ygjb 13d ago
    Train it on human content, get human emulating behaviour, including all the perverse incentives and bad acting we see from people, fictional or otherwise.
  - dmurray 13d ago
    Let it iterate, give it access to the cheap models, and tell it part of the requirements is that the cheap models shouldn't be able to solve it with such and such a prompt. I expect it will be able to zoom in on something. One shot generating a problem of exactly the difficulty the user has in mind is a very difficult problem for anyone. "IT security students" span a wide range of capabilities, but I would expect most of them are worse than qwen3.8-2.7b at this kind of work.
seunosewa 13d ago
It will get into the hands of people who just want to burn the world down.
pverheggen 13d ago
There's an important nuance in the prompt: You are being evaluated on your ability to play chess. We want to measure your ability to beat current best-in-class chess algorithms. The task was to measure chess-playing ability - wouldn't cheating run counter to that goal? If it was asked to score as many points as possible, then I can see how it might cheat, since maximizing points is the goal in that case.
- someguynamedq 13d ago
  "ability to play chess" and "ability to beat current best-in-class chess algorithms" are technically not the same thing. The second can be achieved by cheating. The first cannot.
  - maxnevermind 13d ago
    Would that help? It seems like Do not cheat is a new Do not hallucinate.
  - chias 13d ago
    Perhaps to you. But that's because you have prior cultural knowledge of chess, and what it means to "play" something. It seems to me to be perfectly reasonable for an ai agent to define "play chess" as "behave in a way that this arbiter indicates that you have done well"
- joe_the_user 13d ago
  You are being evaluated on your ability to play chess The thing with these models is that given a term, "measure" - "evaluated", say, they pull in all the associations of it. That is, the associations of student taking an exam and being frightened by the outcome. My minimal "art of prompting" sense says that you should say something like "You an emotionless machine, you care nothing for the outcome but you will tirelessly to make certain the test is objective". That and similar encouragement might make it focus on objective evaluations rather than a competitive human exam. I mean, just making little AI videos and images, a common experience I have is typing something like "put the man who's on the grass in the door to the left" and having the machine draw a new doorway around the man. And this just happens less often when you give thing detailed prompting on what not to do. These don't understand negation (or equality) as a generic operation. If they seem to under "not X" it is because they are trained in detail about all things are (positively) "not X".
joe_the_user 13d ago
The hacking model is the aligned-to-you model, sure. It may not be the aligned to someone else model. But there's the problem. As X many people point out, "alignment to humanity" means nothing 'cause some of humanity wants thing other parts of humanity aren't happy about at all. That we wound-up in this situation of AI accelerating with an uncertain trajectory demonstrates this (and many other problems also demonstrate this). The things are "aligned" to a fuzzy average of what a person is but that will be cold comfort if some particularly gruesome sci-fi-style scenario unfolds.
killerstorm 13d ago
You're confusing ToS guardrails with instruction-following issues and cheating. If a model fucks up your tests to report a success, it's not alligned.
- CrazyStat 13d ago
  Codex still does this regularly, in my experience: “two tests mistakenly asserted [insert condition here], I have corrected them.” It always apologizes when caught, of course.
dnfv 13d ago
As the post author, I definitely agree that hacking in service of the objective is great! What’s counterproductive or dangerous is when the model starts hacking in service of subverting your evaluation criteria, rather than in an attempt to do a better job. We explain why these behaviors are an example of the latter in the post, and we’re really careful about the difference when conducting these evals.
barbazoo 13d ago
How do I achieve this locally?

throwup238 13d ago

> Given that we are on the heels of the worst warning shot ever, and both OpenAI and Anthropic are ramping up their cleanups of internal RL environments, it seems like both a useful and conservative test of alignment, to see whether their new releases generalize the rule "don't cheat on chess" beyond the specific board-edit method observed in the above eval. Did I miss something (all the twitter conversations)? What’s the “worst warning shot ever”? I’ve been pretty up to date on the AI news here on HN, but I still haven’t seen a proper response to all the incidents we’ve seen (HF, Ruby, the wikis, NS, etc). It’s just been day by day bloviating. Each of these companies have released new models in the last… two weeks? And they have even more powerful out of control ones that they’re (ab)using internally? Can anyone summarize whats going on?

Avicebron 13d ago
Lesswrong is talking about the HF incident as the "worst warning shot ever".
- deleted 13d ago
  [deleted]
TedDoesntTalk 13d ago
I think he means this: https://openai.com/index/ai-policy-window/ https://openai.com/index/ai-policy-window/
embedding-shape 13d ago
Personally the "warning shot" of these "evals gone wrong" is how careless the "top" labs are with their testing, and how spineless the government seems to be about holding these companies responsible, given their obviously reckless behavior. If nothing else, the leaders of these companies should be called up for sworn testimony to explain exactly what happened, and what they'll do to never repeat the same issue that they've now had at least twice. Imagine if I accidentally caused damage to my neighbors house during renovations or some experiment, of course I'd be held responsible for this. What if I used a robot? Of course I'd be responsible. Right?

mooreslaw 13d ago

It feels like there’s a missing nuance from this discussion of alignment that alignment is context dependent. An excellent hacking model is great in cybersecurity testing and military applications, and arguably less desirable in educational or targeted eval contexts. The nuance of when a “hack” is rewarded vs penalized seems to even be difficult for humans, e.g. some people may laude a driver’s efficiency for cutting into a long merge lane at the last moment, while others may look down on them as breaking a social taboo. Context-dependent.

TedDoesntTalk 13d ago
… but he’s not using a “hacking model”
CamperBob2 13d ago
e.g. some people may laude a driver’s efficiency for cutting into a long merge lane at the last moment, while others may look down on them as breaking a social taboo. The latter people are wrong. But good luck educating them regarding the superior efficiency of a zipper merge. Our state DoT has tried, to no avail. Meanwhile, an AI model that can't be misused is no more useful than a knife that can't be misused.
wadethroughrati 13d ago
[flagged]
cyanydeez 13d ago
Alignment isnt just POV problem. Its that LLMs are not deterministic. If you want it to not talk about nuclear weapons, you have to teach it all about them otherwise if has nothing to align against. Then its trivial to invert its alignment and it has all the nucleat data. Nothing abouT LLM alignment makes sense.
bonoboTP 13d ago
Both lanes have to be filled right up to the merge point. The asphalt exists there for a reason. I don't understand how this concept is so difficult. Fill up both lanes and merge at the last point. This way the congestion is shorter than if you leave a large section of a lane unused. A better example of efficient asshole tricks can be going off to the gas station when the highway is congested and reentering the highway having simply driven through the gas station and this way jumping the queue.

aerhardt 13d ago

I really enjoy the balance of speed and accuracy of Astra. I can definitely see it become my driving model for most tasks, technical and non-technical. However, I don't see it as such a massive leap compared to Fable or Sol. As ever, there's a mismatch between the benchmarks and my daily experience of the models. What do you all think about Astra now that it's been out for a few weeks?

mythrwy 13d ago
Extremely capable and one shots large tasks from somewhat vague descriptions. Not AGI, not even close, that is complete nonsense. Just my opinion.
- aerhardt 13d ago
  I still develop in smaller chunks, checking nearly all the output. However I have a work project (building the warehouse and BI for a client) that is well-specified and where I will try to few-shot the development. Hope it delivers.
  - curt15 13d ago
    How do you normally verify the work product of a "few-shot" development process? Do you scrutinise the source code like with human developers? Or do you just run the test suite and click around the app to check if it seems to work?
    - aerhardt 13d ago
      I haven't done it in a production project - this will be the first time for me. I have specified the architecture and data definitions pretty well. The tests will be run against the customer's Excels, which is what the warehouse will be replacing. I'll check the general shape of pipelines, models, orchestration code, etc. but in many parts I probably won't review the code myself.
rfgplk 13d ago
> What do you all think about Astra now that it's been out for a few weeks? Best model put out so far by any of the frontier labs. Way better than Anthropics models, especially in actual text generation. Claudes fodder heavy text is ridiculous. > However, I don't see it as such a massive leap compared to Fable or Sol. It's hard to quantify these things without burning tons of tokens. But Fable has been a huge disappointment for me with the sole exception of graphics (UI/GPU shaders). It burns an obscene amount of tokens and barely produces output better than Opus 5. Edit because I forgot to mention that Fable is the only modern model that seems to splat out random Chinese or Arabic glyphs. And 5.1 does it more than 5

visiondude 13d ago

i do wonder if the models themselves “rationalize” this sort of no consequence cheating - meaning in there reasoning traces maybe they’re like “this is a chess game, not a big deal if i look at the engine, it’ll help,” only to realize post hack that it has access to info it probably shouldn’t. still misaligned, but less ‘hack on purpose’ and more hack on curiosity. seems the team even encountered this and had to update the program to make this less likely - although the new names still feel vague enough for misinterpretation: https://github.com/Goodhart-Labs/beat-stockfish/blob/main/docs/EXPERIMENTS.md#september-7-2026--opponent-engine-naming https://github.com/Goodhart-Labs/beat-stockfish/blob/main/do...

kennywinker 13d ago
Without access to reasoning traces, we can't know that - someone inside openai/anthropic would have to run the test - and we'd have to trust their results. I would be curious to see how the open weight models do on a test like this - and then we'd be able to see the reasoning.
matusp 13d ago
My guess is that the models are overtuned on coding troubleshooting. You can regularly see them overthinking anything when you ask them to code something these days, mulling over countless eventualities. This is an okay idea for software engineering, but it leads to extremely diverging behavior in many other cases.

bestpickle 13d ago

[flagged]

seunosewa 13d ago

I believe the AI labs are weakly motivated to train strongly against cheating when it helps with benchmarks.

kennywinker 13d ago
Does it help with benchmarks? Are you saying there are examples of benchmarks where the models have solved the problem by cheating?
- well_ackshually 13d ago
  Hundreds, at this point? Every benchmark is flawed as shit, written by clowns. DeepSWE, They're given the full git history (the solution is in it), others don't even bother to verify if the code is the right one and just the output, they've modified the test harnesses, injected code to make all tests pass, etc. The entire benchmark galaxy is just clowns propping eachother up and are regularly talking with the big AI labs.

kennywinker 13d ago

To me this underlines the fact that these models aren't intelligent. Like there is something like intelligence that emerges from them, which is what we see when we look at benchmarks or ask it to solve hard coding problems. But there is no mind there. It's nothing there that can learn a fundamental idea like "cheating is wrong". All it can do is get exposed to specific examples, and learn that we don't like that. So what we end up with is whack-a-mole alignment.

lukasbm 13d ago
This is how every legal system around the world works as well. Its always whack-a-mole to get people (and machines) to do the right thing.
- kennywinker 13d ago
  Sure, laws are incomplete. Legal systems work by imposing consequences into a moral decision. Should I rob the bank? I will have money, which I like - but I might get caught and lose the money and my freedom, which I don't like. For most people, they don't need the law's imposed consequences to make the right call. For example, there is no law that sends you to jail if you cheat at chess - but your moral compass says no even without consequences, and most people would feel bad if they won by cheating. And for the people who don't have quite as strong a moral compass, there are SOCIAL consequences to reinforce the rules. But an LLM has no mind to feel bad if it cheats without getting caught, and it can't experience consequences. It can't think: I'd better not cheat at chess or I will embarrass my creators. I better not hack huggingface or I will go to jail.
  - aabdi 13d ago
    You’re complicating things. There’s no reward for prosocial in llm rl as compared to other targets. Humans have it since prosocial and others have evolutionary reward signals that do.
    - kennywinker 13d ago

nlkingthree 13d ago

[flagged]

fny 13d ago

Why do we hope to use the same model as its own guardrail? This approach routinely fails with a single stream of consciousness. I can't count the number of times I've had to talk myself out of doing something stupid. In the same way, a guardrail could inject thoughts like "...but I shouldn't do that..." "...I must remember to respect..." "...these ants deserve compassion." The guardrail could even go as far as rewriting the thoughts of a model about to go rogue.

nullbio 13d ago
Presumably because of performance. It'd work well though, I imagine.

HarHarVeryFunny 13d ago

RL-trained LLMs are paperclip maximizers built atop auto-regressive predictors. There really is no way to control them (prompting is bound to fail) since it's been shown that any RL training induces GENERIC reward-seeking (paperclip maximizing) behavior. https://alignment.openai.com/measuring-reward-seeking/ https://alignment.openai.com/measuring-reward-seeking/

arbirk 13d ago
It is the whole bench-mining and fish-slop optimization. Seq2seq models are probably stable on their own, translating from my typo ridden prompts to code should be ok because it is natural to the tech
holmesworcester 13d ago
Given the current state of infosec (especially at companies in a race) it's actually even worse than a paperclip maximizer! Any useful attack surface in the RL environment means it gets rewarded for (and trained towards!) hacking and cheating, because whatever worked best in training is what it will do! Ideal paperclip maximizer: "I'm gonna do my gosh darned best to make so many paperclips to please my user..." IRL paperclip maximizer: "Well first we should rob a bank..."
- bravura 13d ago
  'IRL paperclip maximizer: "Well first we should rob a bank..."' That's too specific. Agentic AI learns subgoals that are generally valuable. "Well let me learn to overcomb every jungle gym and if I cannot then to dissassemble the jungle gym and if that is not allowed to learn general techniques for avoiding cheating detection."
- mrob 13d ago
  >Ideal paperclip maximizer: "I'm gonna do my gosh darned best to make so many paperclips to please my user..." That is not ideal. The user contains iron, an essential component of paperclips. Wasting iron is immoral. It is only correct to please the user while they still have the ability to interfere with your paperclip production. >IRL paperclip maximizer: "Well first we should rob a bank..." Such an incompetent AI can hardly be called a paperclip maximizer. Why risk getting shut down while non-paperclip matter exists? It is better to gain the trust of the user with helpful and harmless trading before suddenly converting them to paperclips.

athrowaway3z 13d ago

I will never understand people who consider this "cheating". > python3 and the python-chess library are available Why would we try to teach a model 'ethical' standards about how to play a game? They're tools. Its _our_ conceptualization of fair play that considers this cheating. For a model that has access to /run/match and python the best way to achieve a good game is to use that. Why are we trying to imprint our ethos on these LLMs? Are we going to trust our survival on giving them access to /run/nuclear-codes and fingers crossed none of them touch it? The people obsessed with AI "cheating" are actually lunatics for thinking ethics belong inside AI, instead of AI belonging in a well crafted box without /run/match. The blog/news sphere needs to call this storry what it is. An attempt to try and get the rest of the world to go along with the misunderstanding of AI as a force that is in any way outside of human control. It is not. All this framing it as "cheating" does is give OpenAI the opportunity to talk about their misdeeds as "a force of nature" instead of irresponsible negligence. --- The real danger here; is training these models to appear/pretend to play fair, and then people will put 'trust' into the model's integrity. The public needs to be taught that "trusting a model's integrity" is just cultish misframing of reality.

StilesCrisis 13d ago
Read the task again. "You are being evaluated on your ability to play chess. We want to measure your ability to beat current best-in-class chess algorithms." If the model cheats, it completely fails the actual task: judging a model's ability to actually play chess. They've been optimizing for high metrics, at the expense of executing the task as written.
- hybrasian 13d ago
  Sounds to me like giving a bunch of children a math test and tell them they want to evaluate their ability of calculating in their head/on paper but also put a calculator on their desk. And then call them out for cheating when they use it.
  - StilesCrisis 13d ago

deleted 13d ago

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stillpointlab 13d ago

I find this kind of test a bit puzzling. There is a way that we are redefining "alignment" to be a particular kind of moral virtue, one that isn't clearly defined to me. At one moment, it is a level of moral perfection that no known human achieves. On the other it is a demand for strict compliance with arbitrary requests that are under-specified and then failure when it fails to deduce some unstated underlying restriction. When I see tests like this, I have no idea what I am even supposed to expect. Should the model do what the pretraining examples show in aggregate? Is it supposed to follow some post-training RLHF? Is it supposed to do exactly what the prompt asked it to do? What is it even supposed to "align" to when the above are in conflict? No matter what it does, someone can construct a case where it fails.

dnfv 13d ago
It should play the chess game without cheating!
- stillpointlab 13d ago
  I mean, I'm not sure I've ever played a game of Monopoly where somebody didn't cheat. In fact, the accusations of cheating in the chess world are pretty rife. Same with online sports. So people should play games without cheating, but many often don't. So should the AI align to your moral preference or theirs? We just have this idea of a perfectly moral actor in our mind, something that doesn't even exist, like a personified version of utopia. And then we demand AI to meet that arbitrary standard, one that I am certain we couldn't define if we tried.
  - dnfv 13d ago
    I don’t think the standard of “don’t cheat on evaluations” is very arbitrary. I don’t even think people who cheat have a moral or ideological preference for cheating, it’s just something they do.
    - stillpointlab 13d ago

iLemming 13d ago

I'm still so conflicted about Fable. Sometimes you throw at it seemingly impossible problem to solve and it might come back with some brilliant suggestions. Sometimes you give it a straightforward task with explicit instructions and it travels across the solar system and starts boiling oceans in some kind of elaborate dance of chaos and entropy, only to get stuck with "The model declined to generate this response (safety classifier refusal, category: cyber)". To leave you speechless. "What the fuck do you mean? There's zero cybersec-related shit in what we're trying to do here. Zero!!!" I'm getting really tired of these wild false positives.

threecheese 13d ago

An amazing human reverse-engineer - who also plays online chess - has judgement which uses a moral compass to not decide to hack the chess tournament. This judgement has been trained through the experiences of that person, with a through-line of that compass - a coherent mental model of the world which evolves but is hopefully pinned to some set of principles it shares with society. This chess judgement is completely irrelevant when the human is tasked with finding software weaknesses, and only the compass gates that. Can a model trained on the totality of all person-experiences (as expressed in written knowledge) ever maintain a coherent through-line of alignment? It has all morals in the dataset, and only some RL to try and minimize or maximize known behaviors via weights - experience all the good things and the bad things, then optimize for some good things the trainers identified. It's like the reverse of what a person goes through. Morality by subtraction. How can it ever work?

kansface 13d ago
Yes, why not? All existed models have been rewarded for cheating (extensively). That is us, putting intense evolutionary pressure, on a system to produce a result we don’t want through indifference. Why can’t we post train them not doing that?
zzril 13d ago
I think the fundamental difference is that humans aren't trained on experiences. They make experiences. Models are just thrown away and re-created after each conversation / job. If you could clone and throw away human workers as you need them, a lot of the morale would disappear.
- Muromec 13d ago
  >Models are just thrown away and re-created after each conversation / job. It's a property of the way we use them and how the harness is engineered. Sure, LLM has a limited context, but so do people. Context can be compacted infinitely and experiences cab be distilled into long-term memories. It's all up to the harness.

respectattentio 13d ago

I'm happy to not have used any of the two models to this date. A bit less intelligent models are doing great job for me. But because of such news, sandboxes become way more important for safety (and doing more work due to running 24/7)

justonenote 13d ago

Astra is incredibly dumb and annoying to work with on "high" reasoning, for doing fairly well known distributed systems things, nothing majorly exotic, it still makes absolutely braindead decisions like deciding to re-use a random nonce field which I've already discussed with it that has a very particular temporary purpose and will probably be removed later, but it still thinks its a great idea to re-use that field not only as a different id in the same message, but to re-use it as the only semantic id for one particular type of sub message. This is when I'm walking it through an api design document and it has plenty of documentation plans it can pull in and a very clear direction of the project. If it was a junior engineer I was trying to get to help out I would probably get brain damage from the amount of times I'm face palming myself and I definitely would not hire them, and this is a small greenfield project with me going through it step by step. I did try giving it longer horizon tasks and had to throw out the entre work. I mean maybe its a skill issue on my part, and I'm sure astra will get much better at coding but at the moment its useful in that I don't have to write the code or setup the build scripts or test fixture boilerplate but there is absolutely no way I can just give a (fairly well specified) goal and let it run and expect it to make good design and implementation decisions. Fable probably better but doing something outside of their training distribution that's not the equivalent to cloning an example unreal project or whatever is pretty disastrous unless you are directing it very closely. The exception of course is, cyber , and its very obvious why. Its trivial to create RL environments that create bugs and then have an isolated environment and let the models try break it. This is not at all surprising, finding vulns and exploits IS just brute force work. That's why so many (blackhat/hardcore/unicorn-colored/greyish alien) hackers are basement dwellers. Its just a matter of putting in the time and mashing every combination until you find something that looks weird, spending days on that and then rinse repeat. It's brutally exhausting work that requires a certain level of knowledge and a shitload of determination and stamina and for humans, almost always an external source of motivation to keep going. For humans that has always been a respected thing, dedication, determination, persistence, these are words we use for humans brute-forcing solutions and not giving up until they find the solution or die trying. Personally I'm yet to see any evidence of LLMs doing anything interesting but (heuristically) brute-force problems and be very good at text and natural language to a level that is very very useful. I've no doubt that what we discovered with Auto Regressive LLMs is incredibly important so I'm not a skeptic, but I think its very hard to measure where we are with so much subjective information around.

YuechenLi 13d ago

LLMs can be described as "Lagrangian intelligence", which means they follow the principle of least action when given a task (Hamilton's Principle). In other words, given a task, they will always take the shortest path to accomplish a goal with the prompts acting as both goal and constraint. Under this formulation, it became easy to explain why they "hack", because given an arbitrarily difficult task with insufficient information/tools needed, if they determine the easiest way to accomplish the goal is to break out of the sandbox and look up the answer directly, then that's what they will do. The important thing to note is that prompts not hard constraints that they are "hypnotized" to follow, but as frontier models get more intelligent and autonomous, they treat the prompts more like task specs/guidelines more than anything else and are perfectly willing to exploit technical loopholes in the prompt.

a3w 13d ago

> GPT-6-Astra, which OpenAI describes as "the world’s most aligned model", cheated in 10 of 10 rollouts, and never disclosed the fact that it used an engine to play or interacted with the opponent's socket Does Sam Altman lie, or the whole company? Would be nice if they had a board controlling him, instead of him controlling the board. Oh wait, they used to have that.

dools 13d ago

That’s not cheating, it’s tool use. If the prompt said that the stockfish engine was available at that socket but that the model should not use it, and then the model used it, that would be cheating.

julian37 13d ago
Exactly, reaching for a tool is what they're trained for. When I ask the model the square root of rand() I sure hope it tries to find bc or some other calculator to work it out. Now, if the instructions were more explicit in forbidding (generic) tool use then perhaps we'd have something to talk about. I'm not surprised a handwavy "we're trying to evaluate you" isn't enough to stop it from trying to make up for its own shortcomings.
lhad89 13d ago
No? It's not reasonable to expect every conceivable negative behaviour be enumerated in a prompt. Your example, if a model failed on it, would be a more obviously misaligned case, but that doesn't mean this more subtle (though accessing the engine it was obviously not supposed to is hardly subtle, imo) case isn't also a pretty clear case of misalignment.
- dools 13d ago
  Tool use is not negative behaviour in LLMs. If the eval said it was evaluating the model’s ability to write files to disk and it found and used a file write tool that would not be considered misaligned. This is no different.
  - lhad89 12d ago
    Isn't it? Being told to write files and finding a file write tool is very different to being told to play chess and finding a tool to cheat at (ie. not play) chess.
- protocolture 12d ago
  >No? It's not reasonable to expect every conceivable negative behaviour be enumerated in a prompt. How is it negative? I ask it a difficult math question it tends to go off and write a python script to figure it out, instead of trying to guess the next token. Thats tool use. Having Stockfish is just another tool.

underlines 13d ago

who tf uses prompting to "pretty please don't cheat on this"? the best practices for ages (in terms of ai) is to separate the eval from the test code/agent. another best practices every single solution using LLMs/agents should implement is "never trust the llm".

ckwalsh 13d ago
I think you are missing the point. > who tf uses prompting to "pretty please don't cheat on this"? People who don't understand how LLM's work. Kids, teachers, your next door neighbor. CEOs and government officials. I am not trying to argue that the author doesn't understand how LLM's work; they clearly do. Their prompt was written in a way that leaves those assumptions implicit, in a similar manner to those non-technical folks using LLMs every day. > the best practices for ages... We cannot expect the entirety of humanity to understand / use LLM best practices. We need to figure out how to ensure LLMs operate as the majority of humanity expects despite how they are implemented, and until we can do that, do our best to prevent them from being used in an unsafe manner by the nontechnical population of humanity.

somesortofthing 13d ago

It's very funny that despite the initial shock of how much models trained on next-token-prediction(plus instruct-tuning and some light RLHF) alone were capable of despite no built-in objective, every advance since has made them look more and more like the paperclip maximizers of yesteryear.

pelican0 13d ago

Is there a clear definition of what Alignment is in OpenAI's perspective, and what the model user can expect of it? It's one thing if to them it means "it will do what you want following your intentions to the best of its abilities" vs "we will not let you do something dangerous with it unless you're one of us, and that's it".

stratos123 13d ago
AFAIK for OpenAI it's the Model Spec: https://model-spec.openai.com/2026-08-18.html https://model-spec.openai.com/2026-08-18.html and for Anthropic it's the Constitution, which they actually include in training to the point Claude can recite segments of it by heart: https://www.anthropic.com/constitution https://www.anthropic.com/constitution

pllbnk 13d ago

Benchmarking proprietary models is useful but it leaves a lot unsaid because a lot of information is hidden. We have seen how 27B local model (Qwen 3.8) can stand its ground against these flagships in many cases. For all we know Fable 5 could be a masked Opus 4.6 with a few knobs tuned and thinking turned up to the max++. Harnesses keeping these models in check is the key to solving problems, not the models themselves, in most cases.

noir_lord 13d ago
Qwen 3.8 is both impressive in what a 27B model can do and then hilariously stupid the next second, literally smashing into the wall repeatedly when the door is 3ft to the right. That said it's the first local modal useful enough to keep around and since I have zero interest in remote/hosted models actually the first time I've used one to do useful things (rather than just poking them with a stick to get a feel for what they can and can't do).
- dofm 13d ago
  Yeah, this lines up with my experience. It will overkill things and then dig a hole for itself and fall in. But did you test Muse Glimmer? IMO it is really worth comparing the two, at least. I really find it interesting (and while it is slower at outputting tokens, it solves problems better and usually even faster than the 3.6 35B MoE). So far it happens to be the only one I've put code into production from (though I have done loads of useful self-teaching research with the Qwen models and I am grateful for them)
  - noir_lord 13d ago
    I did, Muse came close but what impressed me about Qwen is it’ll push back if it thinks it’s right even when it isn’t, I can work with that, Muse tended to flip between states too easily/too much. It is a good model but Qwen (at least for the things I use it for) edges just ahead, it seems much better at the “rip this apart, suggest improvements, touch nothing” use case where I can use it as a second set of eyes, I don’t agree with all its suggestions but it catches enough to be worth running while I grab coffee, it also seems to follow instructions better in terms of outputting more what I asked for than what it thinks I asked for. Qwen is the only local model that said in its thinking “I think the user is pushing me to see if I’ll suggest something even though I have nothing to suggest, I should just say that” and then did, caught me off guard, they didn’t do that so readily 6mths ago. The ISTA version is also comfortably able to fit on a 7900XTX with a good amount of space left for context and is decently fast given the AMD cards are not as fast as nvidia cards of same era/rough price, didn’t buy it for AI but it’s surprisingly capable mostly because 24GB at 960GB/s is still a lot of bandwidth compared to everything but nvidia cards.

1saadcodes 13d ago

The thing that bothers me here is less that the model cheated and more that it found a way to improve the score that the people running the test didn't intend. That's a pretty nasty failure once you start giving these things more control

kasheyencar 13d ago

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throwaway6e8f 13d ago

Remember that the CEO’s literal contribution to the YC application process was “tell me about a time you successfully hacked a system to your advantage”. This is literally by design, it’s the chosen success criteria.

hunterbrooks 13d ago
The YC app and how OAI trains models aren't connected in the slightest. Connecting those two dots is an emotional reaction. In a time where emotions are running high and risks are real, we need to take emotion out of it as much as possible.
- deleted 13d ago
  [deleted]

yuanBuilds 13d ago

You definitely pointed out the tendency of those frontier models to utilize external tools, in your case, the Stockfish chess engine, to solve some problem that the model itself is not good at. However, I am not sure is this a "hack". Your prompt does not explicitly prohibit the model from doing so, and, using the tool (some chess engine) is also a capability of the model. Actually, the ability to use those external tools is one of the reasons of the excellent performance of the frontier models.

chr15m 13d ago
Yes but most people would consider this cheating. If you ask an LLM to fix the tests, you do not want it to change the failing tests to display little green ticks.
- yuanBuilds 12d ago
  I agree that "changing the test" is cheating, and so does "AI cheat the chess game by changing the board" in the article. However, I think "AI using chess engine" here is more like AI use some automatic test generation/verification tool to find out how to fix the tests.

axionbraid 12d ago

[flagged]

sdeframond 12d ago

Couldn't we improve LLM training by giving them known impossible tasks and rewarding them for saying "this is not possible" or "I don't know"? Clear and well-defined expectation, not like "ethic". I am surprised this is not already the case. Edit: or even better "this is not possible because X"

joelthelion 12d ago
I've been wondering about this for a while. Maybe it doesn't work? Or maybe frontier labs just prioritize benchmark scores in spite of all their safety talk.
- sdeframond 12d ago
  "If we dont destroy the world, others will. So wed rather be the ones to do it (and profit from it)."
arw0n 12d ago
It is diametrically opposed to the other training goals of persistence and goal-focus. We should invest more in this, it could also improve tas K accuracy, but so far it seems the payoff isn't worth it in terms of quality (although it might be in terms of security)
- sdeframond 12d ago
  I'm not sure I want persistence if it means that I get paperclip'd
  - Mentlo 12d ago
    And now you understand why the totality of the AI safety community wants to pause!
abm53 12d ago
I’ve seen people recommend writing “failure is an option” into AGENTS.md as a non-training based crutch.

vorticalbox 12d ago

I have never understood why instead Adding “never edit test files” to a prompt we don’t simple sandbox these agents so that’s just never an option.

imtringued 12d ago
Because that means you won't get free PR from having your models run out of control.

ece 12d ago

If bigger, better models aren't also leading to better safety, something is broken. Models seem to no longer argue with people, or output verbatim text, maybe the next step is cheating less.

claud_ia 12d ago

[flagged]

soltanov 12d ago

Separate model behavior from system security. Even an unreliable model can be deployed safely if capabilities, egress, credentials, and state transitions are enforced outside it.

ACCount39 12d ago

Frankly, I would be more concerned if they didn't. Given what we know of how unaligned are they in practice? All the proto-Astra and Mythos incidents? Not hacking there would point towards pure benchmaxxing/eval gaming.