That was my thought too. For all of Anthropic's talk about their "adversaries", it seems Z.AI have been quietly offering fixes for single shot Remote Code Execution flaws in US software (Safari / WebKit) that Apple and Glasswing / Mythos missed, and that Apple would not attribute to GLM.
That was a wtf to me, so I checked Apple’s latest iOS release security content and GLM & z.ai is mentioned once (under WebKit), Anthropic is mentioned twice, Codex is mentioned once. Not clear if there are other instances where the model did most of the work but wasn’t credited. I didn’t bother to check other releases.
It’s very likely they found all of them, but that the same happened that happened to Microsoft a couple of decades ago: NSA orders not to disclose / fix them so that they can put it in their collection of unfixed zero days.
I'm not a gambling person, but if I was this would be my bet.
You have to consider that having an LLM scan for vulnerabilities is hardly infallible. It is a search guided by heuristics and given a large enough codebase, it is unlikely to identify all vulnerabilities.
Personally, I've had Fable 5, GPT 5.6 Sol, and GLM 5.2 all looking for correctness issues in an old abandoned WIP codebase of mine and all of them found some that the others hadn't discovered. Now, correctness issues aren't the same as vulnerabilities, but the same principle about using heuristics to find defects applies.
This makes perfect sense, but that conflicts with the impression put forward by Anthropic and OpenAI (in particular) that they alone occupy 'frontier model' spots. Frontier models should large dominate their competitors on a capability basis, but if GLM 5.2 (now 5.3) is routinely finding bugs / vulnerabilities missed by Fable and Sol then GLM might be genuinely a frontier-grade model by itself.
Not necessarily. Even near the frontier, we don't really have a total ordering of capabilities, but a partial order. And even frontier models make plenty of mistakes. Combined with the randomness inherent in searching large codebases for vulnerabilities or correctness issues, it is entirely plausible that even much weaker models (and GLM-5.2 isn't even weak) can stumble upon issues that stronger models missed.
My current hypothesis – for which I have only limited evidence, unfortunately – is that it is better to have multiple reasonably powerful (but not necessarily frontier) models looking for issues than just one very powerful one. And even then you're likely to miss out on some issues.
For normal software it would be as you say, but LLM progress is so ridiculously fast that things go from "bleeding edge" to "eh, you'll do" in about that timeframe, and "eh, you'll do" to "why even bother with this old rubbish?" in the same again.
Or, from a different perspective, we can expect some new frontier model from Anthropic in a week or two, and from OpenAI in a month or so.
We cannot trust a single company to report security issues, it’s good to see competition in that domain
Is it possible to build heavier traditional linting to catch whatever is being caught in a more deterministic way? It seems to me that would be far more efficient in the long run (even if the efficiency is only for the AI to know that aspect was already checked).
Not with Anthropic's models!
Maybe "It's really hard" is more accurate? We (humanity) for most part basically agreed to ban the usage of various chemical weapons in wartime, which seems to have drastically reduced the usage of it, even though it's still used by shit actors today from time to time. But it's hard to deny that usage didn't decrease after banning it, which makes "banning" maybe not completely useless for certain things.
"Banning" things that can be easily copied over cyberweb transportation pipes feels like an fool's errand though, regardless of what it is. It's just too easy to get around, compared to actual physical items I suppose.
Regardless of whether or not adversaries are using them, the US has by far the most compute available, and we've now hit the line where major providers are no longer releasing their best models. The public gets the "current" level of intelligence, while the US government gets to control access to the actual frontier of non-public AI. From their perspective, their enemies using GLM5.3 while they have GPT6 and Mythos6 or whatever is a fine trade.
I don't support a ban at all, nor the US's behavior, I'm just pointing out some facts that change the argument.
[1] https://www.washingtonpost.com/graphics/2020/world/national-...
>This kind of thing works when you have a significant lead in the field
No? It works even if the adversary has the same capabilities. It only stops working when everything is fixed.
I'm curious: to any professional vulnerability researchers reading this, what do you think?
Now i know that reality is worse than the worst conspiracy theorist.
It might not be the reason, but of course it's a contributing factor.
So we might ask if one of the reasons the US is being the bad guy is it's usual spying antics, and we're left asking why China is being the good guy.
Why do you _not_ believe it's a possibility?
Distillation is just forcing the model to use an exam prep workbook for training instead of generic publicly available textbooks. The models themselves has to be smart enough for that to work. It's the exact same thing as Asian tiger mom double schoolwork strategy, to paint a picture.
It's almost like there's an actual cost to developing these models, and the Chinese don't have magic dirt that allows them to do it at a fraction of the cost.
Maybe so, but I'm not sure I'd like to live in China of all places. (Don't get me wrong. Lotta places I'd like to visit if I ever got the chance, and China's on that list, but to live there? I don't think so.) Maybe one of the Nordic countries?
Complete speculation, but I wonder if they and Anthropic are scanning very different codebases and Anthropic's skew would be in the other direction.
Someone still has to run it. The analysis and fix could be someone's machine but not committed / published.