AI cybersecurity is not proof of work
antirez.com
Recent and related: Cybersecurity looks like proof of work now - https://news.ycombinator.com/item?id=47769089 - (198 comments)
antirez.com
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.
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.
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.
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.
The platonic ideal of how to dismiss any argument by anyone about anything.
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.
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.
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”.
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.
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.
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.
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
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.
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.
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.
My point is that more experienced programmers are better at security on average, not that they are security experts.
I guess we'll never learn.
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.
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?
This does not match my experience.
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…
Rumors say it has 10 trillion parameter vs. 1 trillion.
In conclusion - Having a lot of tokens help! Having a better model also helps. Having both helps a lot. Having very intelligent humans + a lot of tokens + the best frontier models will help the most (emphasis on intelligent human).
The defender also not only has to discover issues but get them deployed. Installing patches takes time, and once the patch is available, the attacker can use it to reverse engineer the exploit and use it attack unpatched systems. This is happening in a matter of hours these days, and AI can accelerate this.
It is also entirely possible that the defender will never create patches or users will never deploy patches to systems because it is not economically viable. Things like cheap IoT sensors can have vulnerabilities that don't get addressed because there is no profit in spending the tokens to find and fix flaws. Even if they were fixed, users might not know about patches or care to take the time to deploy them because they don't see it worth their time.
Yes, there are many major systems that do have the resources to do reviews and fix problems and deploy patches. But there is an enormous installed base of code that is going to be vulnerable for a long time.
It depends. Some classes of vulnerabilities can be excluded by construction. This is usually seen as too hard to be practicable, but AI potentially changes this.
Adding the words “by Claude” to it doesn’t materially change it. One could also pay a few humans to do the same thing. People have done that for decades.
It takes humans a very long time to learn how to code/find bugs. You just can't take any human and have them do it in a reasonable amount of time with a reasonable amount of money.
Claude is effectively automation, once you have the hardware you can run as many copies of the model as you want. Factories can build hardware far faster then they can train more people.
It's weird to see a denial of the industrial revolution on HN.
I’m not denying that LLMs can be used to improve security research, suggesting that their use is wrong or anything like that.
Humans have used software to research security for a long time. AI driven SAST is clearly going to help improve productivity.
Humans burned stuff for a very long time now, it's when we started burning coal in mass industrially that the global environmental impacts started stacking up to the point of considerable damage.
Coal, even a home coal fired boiler of the 1940s vintage, is just about as clean as solar, when compared to open cooking fires burning dung, which is the "most popular" method of harnessing combustion on Earth, measured per ton over per capita. Even going from wood to coal is a huge step up in pollution reduction compared to old school methods of burning randomly sourced trees. (Your rocket heater doesn't count. That wasn't even a twinkle in an inventor's eye when coal started to become popular.)
Source: did my senior P-chem work on smog. Then saw the theory made manifest (in a way that no amount of schoolwork could possibly replace) by looking at particulate build-up on a glacier with my own eyeballs. Pollution you can see, and hold in your hand will make this more clear than any amount of chart and graph reading about PM2.5 this and that.
Also: I hate that I had to self-censor my use of emdashes because I don't want my lived experiences to get flagged as chatbot slop. Grrr.
Even checking human work is often a shortcoming of processes in practice.
A good security expert earns how much per year? And that person works 8/5.
Now you can just throw money at it.
CIA and co pay for sure more than 20k (thats what the anthropic red team stated as a cost for a complex exploit) for a zero day.
If someone builds some framework around this, you can literaly copy and paste it, throw money at it and scale it. This is not possible with a human.
> Now you can just throw money at it.
What happens when you throw enough money at it that it raises the cost significantly.
CIA and FBI and states easily pay 100k for a zero day.
Plenty of companies have security expert staff on file.
And it will become cheaper and easyer to use, fast.
Logged in just to show some love. +1 for the economics. +1 again (if I could) for the truth-to-power.
We need a lot more of this kind of multi-disciplinary skepticism to counterbalance the industrial grade rockstar ninja 10x Kool-Aid drinking.
Arms race
- what if at a certain level of capability you're essentially bug-free? I'm somewhat skeptical that this could be the case in a strong sense, because even if you formally prove certain properties, security often crucially depends on the threat model (e.g. side channel attacks, constant-time etc,) but maybe it becomes less of a problem in practice?
- what if past a certain capability threshold weaker models can substitute for stronger ones if you're willing to burn tokens? To make an example with coding, GPT-3 couldn't code at all, so I'd rather have X tokens with say, GPT 5.4, than 100X tokens with GPT-3. But would I rather have X tokens with GPT 5.4 or 100X tokens with GPT 5.2? That's a bit murkier and I could see that you could have some kind of indifference curve.
Also, I find myself thinking more and more that the ability to pay for tokens is becoming crucial. And it's unfair. If you don't have money, you don't have access. Somehow, a worsening of class conflicts. If you know what I mean.
If you spend months shipping slop, because “models will get better and tomorrow’s models can fix me today’s slop”, what happens when they not only do not get better, but actually get worse, and you are left with a bunch of slop you don’t understand and your problem solving muscles gotten weak?
I would say that most software is going to have few easily exploitable bugs. Presence of such bugs will immediately cost more than having them discovered and fixed.
Other bugs, those that do not lead to easy pwning of a system, circumventing billing, etc, may linger as much as they currently do.
If anyone has access to the mythical Mythos we'll see the contact with reality.
With LLMs even the halting problem is just the question of paying for pro subscription!
This continous rush is not healthy. npm updates, replies to articles that barely made HN 12 hours ago, anything like that. It's not healthy.
Slow down.
Interestingly enough, I was thinking of writing an article about how cybersecurity (both access models and operational assumptions) can be modeled as a proof (NOT proof of work) system. By that I mean there is an abstract model with a set of assumptions (policies, identities, invariants, configurations and implementation constraints) from which authorization decisions are derived.
A model is secure if no unauthorized action is derivable.
A system is correct if its implementation conforms to the model's assumptions.
A security model can be analyzed operationally by how likely its assumptions are to hold in practice.
AISLE demonstrated in the last few weeks that small (weak per the author) models can find the openBSD bug (when pointed at the code). And apparently did several runs with the same results. Was gpt oss hallucinating on all those runs?
And what separates a strong model from a weak one? Is qwen3.5 27b weak?
Don't trust who says that weak models can find the OpenBSD SACK bug. I tried it myself. What happens is that weak models hallucinate (sometimes causally hitting a real problem) that there is a lack of validation of the start of the window (which is in theory harmless because of the start < end validation) and the integer overflow problem without understanding why they, if put together, create an issue. It's just pattern matching of bug classes on code that looks may have a problem, totally lacking the true ability to understand the issue and write an exploit. 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. Stronger models hallucinate less, so they can't see the problem in any side of the spectrum: the hallucination side of small models, and the real understanding side of Mythos
tomato, tomato
It's not proof of work, but proof of financial capacity.
The big companies are turning the access to high-quality token generators (through their service) into means of production. We're all going direct to Utopia, we're all going direct the other way.
1) Create fear via the pro-American Axel Springer press (politico). Use UK/EU competition to make the EU jealous:
https://www.politico.eu/article/anthropic-hacking-technology...
2) Hype up the thing via clueless publications like the Guardian:
https://www.theguardian.com/technology/2026/apr/17/finance-l...
"As you would expect, the engagement I have had from UK CEOs in the last week has been significant."
3) Sell the damm thing that finds 20 vulns in an NNTP over CORBA written in INTERCAL app to EU and UK companies.
None of the people involved in "dealing with the threat" have the slightest clue. UK/EU always falls for the latest US hype and CEOs pay up.
So the bigger models hallucinate better causally hitting more real problems?
This is exactly the argument AI skeptics make btw. Also you say you tried GPT 120B OSS, that's like me proclaiming LLM coding doesn't work because I tried putting gpt 3.5 in Claude Code. Try it with GLM 5, Qwen, etc. Or improve your harness :)
its not just PoW at inference. It's PoW of inference + training.