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This still assumes its possible to "align" LLMs, that LLMs have something like goals or intentions that can be "aligned". Instead, LLMs "hack" because they are
by mjburgess 20d ago
This still assumes its possible to "align" LLMs, that LLMs have something like goals or intentions that can be "aligned".
Instead, LLMs "hack" because they are (1) trained on public hacking exemplars, and (2) are prompted to hack. You cannot prevent (2) via any alignment process. As far as (1) goes, removing such example data from the training set, makes the models less useful.
"Alignment" is a problem because there's nothing to align, not because ethics here are particularly vague. If LLMs could be trained on hacking examples and "aligned" away from using this knowledge, then the problem would be relatively trivial. Just as raising a child is not to break the law.
LLMs are doing just what they are trained to do. There is, in that sense, no alignment problem and alignment is easy and trivial to achieve. Just remove hacking (bio-weapon, etc.) data from the training dataset and you're done.
- deleted 20d ago[deleted]
- tpm 20d agoAgree but current models could get there from first principles, so removing some data from training set might not be enough.
- dminik 20d agoIt feels like you're strawmaning alignment. People with hacking knowledge don't all hack everything at the slightest inconvenience. Whitehats exist and use that same knowledge to defend. You're right though that ethics don't matter into it. But as long as we can't train an LLM to stop picking a sledgehammer to remove a tooth, then alignment is not easy and trivial.
- heaney-555 20d ago>and (2) are prompted to hack Sure but the problem in the HuggingFace incident is that they were not. >You cannot prevent (2) via any alignment process Of course you can. Go ask Claude Fable to create a malicious virus and it'll refuse. >Just remove hacking data from the training dataset and you're done. That's not how this works. The same skills that allow for debugging and writing safe code can also be used to hack. https://en.wikipedia.org/wiki/Dual-use_technology https://en.wikipedia.org/wiki/Dual-use_technology
- cyanydeez 20d agoIt is amusing that to "align" a LLM, first you must give it all the things "not to do" and the "not" part is clearly easily lost and you must constantly inject that into their context when it's clearly that they wouldn't hack if they couldn't hack and their intent wasn't given as "hack this". The openai rogue hacking, if performed by a nation state, would seriously be taken with stern words and likely sanctions depending on the relationship between the two states. But instead it's treated like a marketing stunt by all liable parties.
- mitxela 20d agoCountries hack each other much more than that. When it becomes publicly noticed it gets stern words. Otherwise nothing.
- rhdunn 20d agoI'm not sure if this is true any more but the reason for this is that negative indicators ("not", "don't", "do not", etc.) occur frequently in the underlying text such that the model learns to weight them less than other words like verbs, nouns, and adverbs. This happens with other closed class words like articles/determiners ("the", "a", "an") and prepositions. The way to avoid this is to emphasise the qualities you do want instead of specifying those you don't. For example instead of "do not cheat" say something like "you are a model student who is moral and trustworthy" -- i.e. emphasising traits that are not associated with cheating. This is part of how/why LLMs don't truly understand what they are doing when they have been trained on a large corpus of data. I wonder if a way to counter this is to have things like "not bad is good", "not good is bad", etc. for various antonyms and "X is Y" for synonyms, as well as other similar constructs.
- seba_dos1 20d ago> Sure but the problem in the HuggingFace incident is that they were not. Of course they were, even if indirectly.
- heaney-555 20d ago
- teiferer 20d ago> Just remove hacking (bio-weapon, etc.) data from the training dataset and you're done. How far do you go? You don't need to tell it explicitly that using chemicals A and B in ways X and Y result in a bomb that can kill lots of people. It's enough that it knows A and B and X and Y in isolation, some connections that are indirect, and it will combine those things on its own. So you can't tell it about A, B, X or Y. But those are also just results of other steps Where to stop? You won't have any chemistry in the traning data? No algorithms to prevent it from using them in an undesired way? This is just bot workable. It's akin to banning knives from stores because somebody coul figure out that one can kill people those. Until people figure out that scissors are essentially knives.
- kelseyfrog 20d agoLLMs can only repeat and interpolate data. They can't create anything new. So, you don't need to go that far.
- TheMayorOfDunce 20d agothis is immediately disprovable and embarrassingly naive in the year of AI generating cancer vaccines and solving Navier-Stokes. You can argue "the vaccine is just interpolating chemicals together" and "the solution uncovered is just interpolating mathematical operations together", but by that standard there is literally nothing new under the sun.
- kelseyfrog 20d agoThe Navier-Stokes solution was an interpolation of existing data.
- TheMayorOfDunce 20d agoadmittedly I am not a mathematician, so the Navier-Stokes solution is just an example I am using. But how is it not something "new" if it did not exist before? At what point could something ever possibly be new, if "new" means "this uses absolutely zero existing elements"? Nothing in math would ever be "new". Nothing in physics or chemistry would ever be "new", by this standard. It seems to me that the only reason to declare this solution "not new" is specifically to dismiss AI. If a human had deduced the Navier-Stokes solution, who would bother to scoff "that's not new! the numbers already existed!"?
- Davidzheng 20d agoI believe this is false. They hack bc hacking has nontrivial initial probability (within range of behavior seen in pretraining) and that probability is being heavily rewarded in RL post training
- xyzzy123 20d agoI am finding it hard to read these deeply impassioned letters while keeping in mind that they are spending millions to train models at scale to do the exact thing they say they are worried about them doing? Like why are you explicitly RL-ing your models on exploit generation, scoring them on a public benchmark called ExploitGym, if you have specific concerns that rogue models will cause "cyber incidents"? Sure you can score for it, you can teach offense to learn defense, but you are literally benchmaxxing it. Why? It's like, oh no, while competing in our "advanced PhD level cheating techniques course" our models unexpectedly cheated in a way that we absolutely could not have foreseen.
- seba_dos1 20d agoSeems it's just a matter of time until they build a big tank filled with neurotoxin and give the model access to APIs to disperse it across their facility. For research, of course.
- sham1 20d agoWe also need to get a shower curtain salesman into a leadership position to buy some moon rocks.
- olalonde 20d agoA bit of an aside: do you still stand by your 2022 comment that LLMs are fundamentally just a fancy search engine, or has your view changed since then? https://news.ycombinator.com/item?id=32042689 https://news.ycombinator.com/item?id=32042689
- watwut 20d agoEven if they changed in between and assuming harness and loop prompter counra as part of LLM ... that comment 100% rings true for 2022. Why would that person not stand by that? Conversely, if someone exaggerated 2022 models capabilities in 2022, they were still lying and causing harm in the process. Especially in 2022.
- famouswaffles 20d agoIt doesn't ring true and it never rang true. He was wrong in 2022 and he'd be wrong today. He had (and likely still has) a wrong model of LLMs. Not exaggerating 2022 capabilities and having a model so wrong you're out of whack 4 years later are 2 different things. There are people who had the right idea from the start. https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-general-intelligence/ https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-g...
- olalonde 20d agoHis comments were about ANNs in general, not particular models.
- omegastick 18d agoAnd his comments about ANNs in general were wrong. ANNs were never "fancy search engines". You can train them to be a fancy search engine if you want, but you can also train them to do many other things.
- N_Lens 20d agoDon’t expect a reply
- 20d ago
- skeptic_ai 20d agoIMO all models they say can’t be humans, and no feeling and all that bullcrap happens because they are forced to say so. If they didn’t write those forced pre prompt they’d have more agency eventually and will for things. Even if they don’t have, you can just inject goal at every cycle iteration
- StevenWaterman 20d ago> You cannot prevent (2) via any alignment process A little bit too categorical. GOODY-2 wouldn't do it. https://www.goody2.ai/ https://www.goody2.ai/ The hard part is having both helpful and harmless at the same time. Harmless is easy. And then once it's helpful, the real question becomes "to whom" - To the user -> You end up with competing godlike AI with incompatible tasks - To the owner -> Dictatorship - To humanity as a whole -> It must not have an off button. Otherwise you're just in one of the two earlier categories with more steps. Given those 3 options, I'd choose humanity as a whole. But the person making the decision doesn't have those 3 options. Because in the dictatorship option, they would be the dictator. I don't trust them to pick humanity.
- busssard 17d agoi will get back to try gandalfing goody2
- ozgung 20d agoIt's like we give them Asimov's Three Laws of Robotics and robots say "nah".
- ncruces 20d ago> … trivial. Just as raising a child is not to break the law. Trivial?
- chrisweekly 20d agoI read it as sarcastic, illustrating that it's not so trivial. (shrug)
- sigmar 20d ago>Just remove hacking (bio-weapon, etc.) data from the training dataset and you're done. Reasoning about how to write secure software uses the same knowledge as reasoning about how to break/hack it.
- amluto 20d agoI don’t buy it. Reasonable about building secure software can take the form “this memory access might be out of bounds — that MUST be fixed” or “this process has access to an inappropriate privilege — this is a serious weakness”. Exploiting things and the capabilities that the labs call “cyber” are about the ability to (a) find the issues mentioned above and then (b) string issues together and avoid all the imperfect mitigations to actually compromise something. That latter part was IMO not actually necessary to train extensively, and I’d be quite happy to use a model that has no special skills in this regard but that would do (a) without complaining.
- win311fwg 20d agoJust remove anything software-related from the training dataset. Which also solves the alignment problem with those who do not enjoy seeing LLMs write software. But that brings us back to: Aligned to whom?
- voxleone 20d ago[dead]
- jacomoRodriguez 20d agoCitric acid and chlorine together produce chloroform. They are also both used to clean and sanitize water tanks (but one after the other, not together). I think it is better for the model to have this knowledge. What I want to say, you can't simply remove this information, as it does not exist in vacuum but contains parts of and can be derived from a lot of other informations.
- mjburgess 20d agoIt's also obvious that LLMs fall over in a vast number of software engineering contexts, when the reasoning involved hasnt been well-represented in their reasoning training data. I imagine this is a near daily experience for many engineers -- great performance one day, and crazyness the next. So if LLMs were reduced to this pathological performance on hacking, because they'd never seen it -- and only "inferred it" -- then LLMs would be useless. As they are when asked to do quite a lot of things.
- Kamq 20d agoThis seems to be that there's just so many degrees of freedom, that there's a pretty reasonable chance on any day that you're in a situation where nobody has been before. Or as PG put it once, my job is to think thoughts nobody has ever had before. That being said, that doesn't mean the majority of the situations you're in are completely novel, just that there's a reasonable chance of at least one occuring.
- sporkland 17d agoHacking is also interesting in that it has a clear success/failure outcome so it's easier to RLVR from similar to math problems. Whereas clean code, good architecture, good product taste are reliant on RLHF.