[rakejake]

>> Test it yourself, GPT 120B OSS is cheap and available. BTW, this is why with this bug, the stronger the model you pick (but not enough to discover the true bug), the less likely it is it will claim there is a bug.

I guess this is the crux of the debate. All the claims are comparing models that are available freely with a model that is available only to limited customers (Mythos). The problem here is with the phrase "better model". Better how? Is it trained specifically on cybersecurity? Is it simply a large model with a higher token/thinking budget? Is it a better harness/scaffold? Is it simply a better prompt?

I don't doubt that some models are stronger that other models (a Gemini Pro or a Claude Opus has more parameters, higher context sizes and probably trained for longer and on more data than their smaller counterparts (Flash and Sonnet respectively).

Unless we know the exact experimental setup (which in this case is impossible because Mythos is completely closed off and not even accessible via API), all of this is hand wavy. Anthropic is definitely not going to reveal their setup because whether or not there is any secret sauce, there is more value to letting people's imaginations fly and the marketing machine work. Anthropic must be jumping with joy at all the free publicity they are getting.

[antirez]

In the Anthropic Mythos model cards they explicitly remarked that they didn't want Mythos to be specifically good at security. They trained it to be good at coding, and as a side effect the model is (obviously) good at security. This what happens with flesh hackers too, mostly. Hackers are very good programmers, as a side effect they understand systems well enough that their understanding has security implications.

[Hendrikto]

Model cards are just marketing material. I wouldn’t trust them one bit.

[antirez]

You don't need to trust anyone. GPT 5.4 xhigh is available and you can test it for $20, to verify it is actually able to find complex bugs in old codebases. Do the work instead of denying AI can do certain things. It's a matter of an afternoon. Or, trust the people that did this work. See my YouTube video where I find tons of Redis bugs with GPT 5.4.

[Hendrikto]

I did not claim or deny anything. You cited the model card, I just pointed out that this is no reliable source. If you have better sources, like your YT video, you should cite those instead.

[otterley]

You are claiming something: that the model card is not reliable, therefore it's as useful as nothing. Sowing doubt without a possible solution adds little value to the conversation. Moreover, your rebuttal is unsubstantiated.

[cyanydeez]

Guys, think about all the security vulnerabilities you're aware of; now, think about how many of those you know how to technically reproduce. Now imagine that you actually don't know how to reproduce most things and you're never actually be able to judge the result.

Well, just cause these are all AI people doesn't mean they verified enough of the output of these models to actually provide the significant security implications they're advertising.

[mbesto]

And overfitting benchmarks can easily be gamed. Yet here we are with the top HN comment on the HN Mythos thread outlining it's benchmarking performance gains.

I guess we'll never learn.

[Yokohiii]

The whole discussion started out as an attempt to disprove/verify anthropics (model card) claims.

He also transfers the logic of their claims to the actual real world. You can say that model cards are marketing garbage. You have to prove that experienced programmers are not significantly better at security.

[root_axis]

> You have to prove that experienced programmers are not significantly better at security.

That has not been my experience. It's true that they are "better at security" in the sense that they know to avoid common security pitfalls like unparamaterized SQL, but essentially none of them have the ability to apply their knowledge to identify vulnerabilities in arbitrary systems.

[Yokohiii]

An expert level human doesn't have to be expert at every programming category. A webdev wouldn't spot a use after free. A systems engineer wouldn't know about CSRF. That is if both don't research security beyond their field. Requiring a programmer to apply their knowledge to an arbitrary system is asking too much. On the other hand and LLM can be expert level in every programming field, able to spot and combine vulnerabilities creatively. That is all pretty hard and I don't think an security expert with vast knowledge would say "that's easy".

My point is that more experienced programmers are better at security on average, not that they are security experts.

[tracker1]

I would think pwn2own competitions would signal the opposite. I'm consistently and often amazed at how a unique combination of exploits can bring a larger exploit and often in ways that most wouldn't even consider. I think it takes a level of knowledge, experience, creativity and paranoia to be really good with security issues all around as a person.

[inetknght]

> essentially none of them have the ability to apply their knowledge to identify vulnerabilities in arbitrary systems.

I've found it to be the opposite. Many of them do have the ability to apply their knowledge in that fashion. They're just either not incentivised to do so, or incentivised to not do so.

[2983592]

But they are treated as holy scripture ...

[zahlman]

> Hackers are very good programmers

This does not match my experience.

[ang_cire]

The missing part of their intended meaning is "skilled hackers". Unskilled hackers are everywhere, and they're bad at programming, but so are unskilled programmers.

[rakejake]

>>> the model is (obviously) good at security

Out of curiosity, are you one of the people who has access to the model? If yes, could you write about your experimental setup in more detail?

[_the_inflator]

Yep. Some are while others are more or less forum leeching and exploiting known risks and use tools.

But the some that really find certain bugs are really exceptional. Almost all are very hardware prolific and do assembler stuff. This alone is an impressive feast, I still enjoy 6510 and M68000 assembler here and there as a former scener who mainly coded demos and here and there improved games (so called trainers) or cracked few.

To be honest, the assembler guys scare me always because with it you can poke a whole in almost anything. No one in his sane mind uses assembler on x86 for professional development besides few special cases. But Python etc serve many MB of executable code for the abstraction and 20 bytes just kills it…

[Glemllksdf]

If its really more expensive per token, it might have more parameters and is then able to hold more context/scope of code.

Rumors say it has 10 trillion parameter vs. 1 trillion.

[rakejake]

Yes, that does track with my personal experience. More context, more params and no quantization is probably it. But my hunch is that all the training data they've been getting in the past year also plays a part here. More than any other lab, anthropic's focus on coding right from the beginning gives them access to the best training data (several githubs worth). Most of this code comes with human feedback and anthropic even has data on how many went to production, got reverted etc. No need to pay for human labeling when your customers are doing it for you. This is their secret sauce.

[solenoid0937]

Mythos isn't restricted for marketing purposes - that would be incredibly dumb because Anthropic would be giving up first mover advantage for next gen models.

It's restricted because it's genuinely good at finding vulnerabilities, and employees felt that it's not a good idea to give this capability to everyone without letting defenders front-run.

That's it. That's all there is to it. It is not some grand marketing play.

[the_snooze]

>It's restricted because it's genuinely good at finding vulnerabilities, and employees felt that it's not a good idea to give this capability to everyone without letting defenders front-run.

It's a possibility, but it doesn't eliminate the possibility that it's hype. If these claims were indeed serious, they would submit it for independent analysis somewhere.

This isn't some crazy process. Defense contractors are required to submit their systems (secret sauce and all) for operational test and evaluation before they're fielded.

[afthonos]

> If these claims were indeed serious, they would submit it for independent analysis somewhere.

They have. 40 different companies that have all committed resources to patching their systems based on vulnerabilities found by Mythos. One of them, Google, is a frontier AI lab that pointedly did not say that their own models have found similar vulnerabilities.

> Defense contractors are required to submit their systems (secret sauce and all) for operational test and evaluation before they're fielded.

Does this look something like having 40 separate companies look at the outputs of the system, deciding that it’s real and they should do something about it, and committing resources to it?

At some point, “cynicism” is another word for “lalala can’t hear you”.

[jerf]

Another cross-check I've run is, are the claims Anthropic is making for Mythos that out of line with the current status of AI coding assistents?

To which my answer is clearly, no, not even remotely. If Anthropic is outright lying about what Mythos can do, someone else will have it in a year.

In fact the security world would have to seriously consider the possibility that even if Mythos didn't exist that nation states have the equivalent in hand already. And of course, if Mythos does exist, nation states have it now. The odds that Antropic (and every other AI vendor) isn't penetrated enough by every major intelligence agency such that they have access to their choice of model approach zero.

I wonder about the overlap between people being skeptical of Mythos' capabilities, and those who are too skeptical of AI to have spent any time with it because they assume it can't be any good. If you are not aware of what frontier models routinely do, you may not realize that Mythos is just an evolution of existing capabilities, not a revolution. Even just taking a publicly-available frontier model, pointing it at a code base and telling it to "find the vulnerabilities and write exploits" produces disturbingly good results. I can see the weaknesses referenced by the Mythos numbers, especially around the actual writing of the exploits, but it's not like the current frontier models fall on their face and hallucinate wildly for this task. Most everything they produce when I try this is at least a "yeah, that's worth thinking about" rather than an instant dismissal.

[rakejake]

Sure, I am not precluding the possibility that they've trained a genuinely great model. All I am saying is that the "this model better than that model" is moot when on one side you have model weights, and on the other side a whitepaper and some accompanying comments on the danger.

I'm not that old but have been here long enough that I remember when GPT-3 was considered too dangerous to release. Now you have models 10x as good, 1/10th the size and run on 8GB VRAM.

[louiereederson]

I don't think you can say this with confidence, outside-in. It's not just about safety. The additional unknown is cost - I don't just mean API cost, but fully loaded cost for a given task. Is the model cost effective for tasks such that it has product market fit?

We don't yet know if Mythos was a level shift in the capability/cost frontier, or a continued extension of the same logarithmic capability/cost curve.

[solenoid0937]

Some people have access to the model for red team purposes as part of Glasswing and they came away quite spooked according to what I heard

[louiereederson]

I don't doubt it, I just mean the decision to release/not release generally may also be informed by the commercial/economic viability of the model for general usage patterns versus extremely high value patterns like vulnerability assessment

[dmix]

That safety stuff is almost always quacks whose job it is to exaggerate LLMs at their non profits or marketing hype that "our models are so powerful you should fear them". Then they release them and the world moves on and adapts.

Mythos will benefit security in the long run more than hackers, if it can do what they claim. And there's nothing that will stop an LLM like it from being released in the near term so it's very likely just resource constraints or marketing

[frank-romita]

Or, They created the illusion that it's restricted for security reasons but in reality they just lack the necessary for this to be used widespread!

[jayd16]

If it wasn't marketing it wouldn't have fancy branding... It wouldn't even be announced.

[zzzeek]

it seems likely it's both a better model to some unknown extent and doing this "we have to give it to the defenders first" thing is super great marketing material. it seems an entirely natural marketing campaign "announce that we can't even give the model to everyone at first, it's so great!", plus there's some truth to it, even better.

unless you are an employee at anthropic and shouldn't be talking about any of this at all, there's no way to know what the model's capabilities are.

[2983592]

How do you know? If you have access you are not unbiased, otherwise you cannot know by definition.

AI companies routinely claim that something is too dangerous to release (I think GPT-2 was the first case) for marketing reasons. There are at least 10 documented high profile cases.

They keep it secret because they now sell to the MIC with China and North Korea bullshit stories as well as to companies who are invested in the AI hype themselves.

[Glemllksdf]

I prefer a more cautios approach than the musk style were stuff gets fixed after.

And with gpt-2 the worry was mass emails a lot better and more detailed and personal, social media campaigns etc.

How many bots are deployed today on X and influencing democrazy around the globe?

Its fair to say it had an impact and LLMs still have.

[SpicyLemonZest]

GPT-2 was obviously too dangerous to release at the time! It's OK-ish now, when the knowledge that AI can produce arbitrary text is widely shared. It would have been a disaster for scammers and phishers to get GPT-2 at a time when almost everyone still assumed that large volumes of detailed text proved there's a real human being on the other end of the conversation.

[jayd16]

And, as we all know, humans can't be scammers. They need the robots to lie.

[afthonos]

> How do you know? If you have access you are not unbiased, otherwise you cannot know by definition.

The platonic ideal of how to dismiss any argument by anyone about anything.