6 ms·

AI cybersecurity is not proof of work

241 points by surprisetalk 7mo ago

Recent and related: Cybersecurity looks like proof of work now - https://news.ycombinator.com/item?id=47769089 https://news.ycombinator.com/item?id=47769089 - (198 comments)

hoppp 7mo ago
About smaller models finding bugs ... The openBSD bug cost 20k to find, maybe a weaker model would do it for a 100k if rerun many times
andersmurphy 7mo ago
> What happens is that weak models hallucinate (sometimes causally hitting a real problem) So the bigger models hallucinate better causally hitting more real problems?
nottorp 7mo ago
Seriously. We need a BuSab for IT. 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.
- WesolyKubeczek 7mo ago
  Amtrak is slow and expensive, but the hype train is free!
neutered_knot 7mo ago
It is also not proof of work because of asymmetries between attacker and defender. An attacker only needs to find one exploitable issue before the defender finds it and patches it, while the defender eventually needs to find all issues - and even then can't really be sure they remediated everything. 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.
- zozbot234 7mo ago
  > It is also not proof of work because of asymmetries between attacker and defender. An attacker only needs to find one exploitable issue before the defender finds it and patches it, while the defender eventually needs to find all issues - and even then can't really be sure they remediated everything. 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.
4qwUz 7mo ago
While I fully agree with the headline I find it surprising that so many people implicitly claim familiarity with the aptly named "Mythos". Mythos is closed and currently has the status of an overhyped Anduril drone that failed contact with reality in Ukraine. If anyone has access to the mythical Mythos we'll see the contact with reality.
- RugnirViking 7mo ago
  my understanding is that employees of several of the largest companies in the world get access to it atm. Those employees are overrepresented in places like HN
  - WesolyKubeczek 7mo ago
    These employees may be as well under NDA, or their access may be predicated on them not sharing actual data (like Oracle and benchmarks). Anyway, you can’t verify any claims yourself, thus it might as well not exist.
deleted 7mo ago
[deleted]
baxtr 7mo ago
Interestingly enough: earlier today this discussion was trending: https://news.ycombinator.com/item?id=47769089 https://news.ycombinator.com/item?id=47769089 (Cybersecurity looks like proof of work now)
- RugnirViking 7mo ago
  the article here is pretty clearly a response to the one you posted
  - onionisafruit 7mo ago
    It’s only clear if you know it exists, and now I know it exists thanks to gp.
qsort 7mo ago
A couple of alternative scenarios, although I'm not sure how much stock we should put in them: - 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.
- Leomuck 7mo ago
  Honestly, if every software project ran an AI-based security check over their code, the software world would probably be more secure. Of course, there are lots of projects who don't need that, having skilled people behind it, but we've seen many popular software projects (even by big companies) who didn't care at all. So even a basic model would find issues. 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.
  - serial_dev 7mo ago
    Not only that, even if you would like to pay, the best model providers could decide any day that they want to save on cost, so they nerf the responses. Then you shipping on time is at the mercy of these companies. 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?
    - Leomuck 7mo ago
      Good point indeed! I've been feeling Claude Code has gotten worse for a while now, read many articles on it, overall probably due to cost saving. But if you set your things up to depend on it, that becomes a huge issue.
redwood 7mo ago
What seems to be getting lost in the noise on this topic is that security has always been about security in depth and mitigating controls, in other words applied paranoia. There are always threat vectors and we're seeing a change in the shape of those vectors with more rapidity than ever before which is certainly exhausting for everyone. But don't forget the fundamentals here
egormakarov 7mo ago
> Different LLMs executions take different branches, but eventually the possible branches based on the code possible states are saturated With LLMs even the halting problem is just the question of paying for pro subscription!
- dtech 7mo ago
  The proof of halting being unsolvable usually uses a specific "adverserial" machine. In practice it's incredibly likely for the halt question to be answerable for any specific real life program.
alex_young 7mo ago
The whole framing is kind of uninteresting imo. If you spend more time researching code you can find more bugs to exploit / patch is not an earthshaking observation. 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.
- drob518 7mo ago
  Right, but what is interesting is that you can buy it off the rack for the price of tokens. You don’t have to do a specialist search for a security expert, pay a recruiter, hire them, wait for the specialist to start, pay them a signing bonus, pay them an expert-level salary, pay their social security taxes, healthcare benefits, and finally pay for an exit package when you lay them off because the project got canceled. You buy tokens when you need them and you stop buying when you don’t. This was the same dynamic that made cloud computing more interesting than company-owned servers in a company-owned data center. It’s more responsive to business needs and it falls under the development expense budget, not payroll, so you can do it even during hiring freezes.
  - tracker1 7mo ago
    But, you do have to have at least an employee or contractor skilled enough to actually understand the scope of a given bug report from the agent in order to determine validity. I've seen plenty of legit bug reports by humans get dismissed because the reviewer didn't understand the material impact or how the bug/exploit worked.
    - drob518 7mo ago
      Yep, sure. So, maybe you hire one and not three. The point is, it’s going to be fewer. Of course, all that assumes the AI is actually as good as a human, which I’m still skeptical of.
- pixl97 7mo ago
  This is the weirdest take I've seen. 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.
dwa3592 7mo ago
Fighting over analogies is kind of pointless imo, but if you want me to indulge, here is what I will ask: Do you consider breadth first search better or depth first search better? - the good answer is it depends on the search surface. The same way bugs, vulnerabilities are hiding somewhere on the surface or inside(exploiting dependencies) the surface of the software. 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).
- kang 7mo ago
  maybe a human knowledgeable in the domain (the training) is better than a smart liguist-programmer.
rakejake 7mo ago
>> 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.
- solenoid0937 7mo ago
  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.
  - rakejake 7mo ago
    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.
csmantle 7mo ago
> So, cyber security of tomorrow will not be like proof of work in the sense of "more GPU wins"; instead, better models, and faster access to such models, will win. 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.
- tptacek 7mo ago
  There's no "proof" involved. That's the problem with the analogy. It's not about how much "financial capacity" you have. It's about how many bugs you find or fix. The bugs are there where the models help attackers/defenders or not.
gobdovan 7mo ago
Now two popular articles argue about if cybersecurity can be seen as proof of work. 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.
jeremie_strand 7mo ago
[dead]
douglaswlance 7mo ago
you get better models with more compute. its not just PoW at inference. It's PoW of inference + training.
riteshkew1001 7mo ago
[flagged]
EGreg 7mo ago
This just proves that we should stop using old environments and operating systems for mission-critical work, and build a completely new environment from the ground up, that's secure by default. Instead of trying to fix leaky buckets.
kvikuz 7mo ago
[dead]
ramoz 7mo ago
> So, cyber security of tomorrow will not be like proof of work in the sense of "more GPU wins"; instead, better models, and faster access to such models, will win tomato, tomato
- TZubiri 7mo ago
  So kind of like how you would get nowhere by buying more gpus if there's already ASICs in play.
slopinthebag 7mo ago
> Don't trust who says that weak models can find the OpenBSD SACK bug. I tried it myself. 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 :)
kang 7mo ago
The proof-of-work in ai(llm) 'can be' from the training side (not the inference side this blog explores) if a hashcash like 'proof' of model having being trained was defined. It should be possible to do so, since the very least measure of model having gotten smarter with some additional data, is that it will recognize/infer the said additional data correctly.
cremer 7mo ago
[dead]
thesuperevil 7mo ago
That’s interesting
niea_11 7mo ago
I'm confused by the last part saying that if "weak" models (like gpt oss) find the openbsd bug they are just hallucinating. and also stronger models not finding it is because they dont hallucinate but are not strong enough. 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
4qskhaqj 7mo ago
Mythos is just used to get new business contacts: 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-exposes-eu-ai-office-struggles/ 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-leaders-warn-over-claude-mythos-as-uk-banks-prepare-to-use-powerful-anthropic-ai-tool 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.
cowartc 7mo ago
Hallucination vs real finding distinction is the core problem and doesn't get solved by a better model alone. It gets solved by what you do with the output. The verification layer is what makes the system production grade.
HiramRoosevelt 7mo ago
If you're looking for affordable and effective AI tools, this might be the place for you: https://account-bar.com/ https://account-bar.com/

241 points by surprisetalk 7mo ago

Recent and related: Cybersecurity looks like proof of work now - https://news.ycombinator.com/item?id=47769089 https://news.ycombinator.com/item?id=47769089 - (198 comments)

hoppp 7mo ago

About smaller models finding bugs ... The openBSD bug cost 20k to find, maybe a weaker model would do it for a 100k if rerun many times

andersmurphy 7mo ago

> What happens is that weak models hallucinate (sometimes causally hitting a real problem) So the bigger models hallucinate better causally hitting more real problems?

nottorp 7mo ago

Seriously. We need a BuSab for IT. 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.

WesolyKubeczek 7mo ago
Amtrak is slow and expensive, but the hype train is free!

neutered_knot 7mo ago

It is also not proof of work because of asymmetries between attacker and defender. An attacker only needs to find one exploitable issue before the defender finds it and patches it, while the defender eventually needs to find all issues - and even then can't really be sure they remediated everything. 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.

zozbot234 7mo ago
> It is also not proof of work because of asymmetries between attacker and defender. An attacker only needs to find one exploitable issue before the defender finds it and patches it, while the defender eventually needs to find all issues - and even then can't really be sure they remediated everything. 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.

4qwUz 7mo ago

While I fully agree with the headline I find it surprising that so many people implicitly claim familiarity with the aptly named "Mythos". Mythos is closed and currently has the status of an overhyped Anduril drone that failed contact with reality in Ukraine. If anyone has access to the mythical Mythos we'll see the contact with reality.

RugnirViking 7mo ago
my understanding is that employees of several of the largest companies in the world get access to it atm. Those employees are overrepresented in places like HN
- WesolyKubeczek 7mo ago
  These employees may be as well under NDA, or their access may be predicated on them not sharing actual data (like Oracle and benchmarks). Anyway, you can’t verify any claims yourself, thus it might as well not exist.

deleted 7mo ago

[deleted]

baxtr 7mo ago

Interestingly enough: earlier today this discussion was trending: https://news.ycombinator.com/item?id=47769089 https://news.ycombinator.com/item?id=47769089 (Cybersecurity looks like proof of work now)

RugnirViking 7mo ago
the article here is pretty clearly a response to the one you posted
- onionisafruit 7mo ago
  It’s only clear if you know it exists, and now I know it exists thanks to gp.

qsort 7mo ago

A couple of alternative scenarios, although I'm not sure how much stock we should put in them: - 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.

Leomuck 7mo ago
Honestly, if every software project ran an AI-based security check over their code, the software world would probably be more secure. Of course, there are lots of projects who don't need that, having skilled people behind it, but we've seen many popular software projects (even by big companies) who didn't care at all. So even a basic model would find issues. 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.
- serial_dev 7mo ago
  Not only that, even if you would like to pay, the best model providers could decide any day that they want to save on cost, so they nerf the responses. Then you shipping on time is at the mercy of these companies. 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?
  - Leomuck 7mo ago
    Good point indeed! I've been feeling Claude Code has gotten worse for a while now, read many articles on it, overall probably due to cost saving. But if you set your things up to depend on it, that becomes a huge issue.

redwood 7mo ago

What seems to be getting lost in the noise on this topic is that security has always been about security in depth and mitigating controls, in other words applied paranoia. There are always threat vectors and we're seeing a change in the shape of those vectors with more rapidity than ever before which is certainly exhausting for everyone. But don't forget the fundamentals here

egormakarov 7mo ago

> Different LLMs executions take different branches, but eventually the possible branches based on the code possible states are saturated With LLMs even the halting problem is just the question of paying for pro subscription!

dtech 7mo ago
The proof of halting being unsolvable usually uses a specific "adverserial" machine. In practice it's incredibly likely for the halt question to be answerable for any specific real life program.

alex_young 7mo ago

The whole framing is kind of uninteresting imo. If you spend more time researching code you can find more bugs to exploit / patch is not an earthshaking observation. 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.

drob518 7mo ago
Right, but what is interesting is that you can buy it off the rack for the price of tokens. You don’t have to do a specialist search for a security expert, pay a recruiter, hire them, wait for the specialist to start, pay them a signing bonus, pay them an expert-level salary, pay their social security taxes, healthcare benefits, and finally pay for an exit package when you lay them off because the project got canceled. You buy tokens when you need them and you stop buying when you don’t. This was the same dynamic that made cloud computing more interesting than company-owned servers in a company-owned data center. It’s more responsive to business needs and it falls under the development expense budget, not payroll, so you can do it even during hiring freezes.
- tracker1 7mo ago
  But, you do have to have at least an employee or contractor skilled enough to actually understand the scope of a given bug report from the agent in order to determine validity. I've seen plenty of legit bug reports by humans get dismissed because the reviewer didn't understand the material impact or how the bug/exploit worked.
  - drob518 7mo ago
    Yep, sure. So, maybe you hire one and not three. The point is, it’s going to be fewer. Of course, all that assumes the AI is actually as good as a human, which I’m still skeptical of.
pixl97 7mo ago
This is the weirdest take I've seen. 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.

dwa3592 7mo ago

Fighting over analogies is kind of pointless imo, but if you want me to indulge, here is what I will ask: Do you consider breadth first search better or depth first search better? - the good answer is it depends on the search surface. The same way bugs, vulnerabilities are hiding somewhere on the surface or inside(exploiting dependencies) the surface of the software. 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).

kang 7mo ago
maybe a human knowledgeable in the domain (the training) is better than a smart liguist-programmer.

rakejake 7mo ago

>> 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.

solenoid0937 7mo ago
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.
- rakejake 7mo ago
  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.

csmantle 7mo ago

> So, cyber security of tomorrow will not be like proof of work in the sense of "more GPU wins"; instead, better models, and faster access to such models, will win. 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.

tptacek 7mo ago
There's no "proof" involved. That's the problem with the analogy. It's not about how much "financial capacity" you have. It's about how many bugs you find or fix. The bugs are there where the models help attackers/defenders or not.

gobdovan 7mo ago

Now two popular articles argue about if cybersecurity can be seen as proof of work. 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.

jeremie_strand 7mo ago

[dead]

douglaswlance 7mo ago

you get better models with more compute. its not just PoW at inference. It's PoW of inference + training.

riteshkew1001 7mo ago

[flagged]

EGreg 7mo ago

This just proves that we should stop using old environments and operating systems for mission-critical work, and build a completely new environment from the ground up, that's secure by default. Instead of trying to fix leaky buckets.

kvikuz 7mo ago

[dead]

ramoz 7mo ago

> So, cyber security of tomorrow will not be like proof of work in the sense of "more GPU wins"; instead, better models, and faster access to such models, will win tomato, tomato

TZubiri 7mo ago
So kind of like how you would get nowhere by buying more gpus if there's already ASICs in play.

slopinthebag 7mo ago

> Don't trust who says that weak models can find the OpenBSD SACK bug. I tried it myself. 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 :)

kang 7mo ago

The proof-of-work in ai(llm) 'can be' from the training side (not the inference side this blog explores) if a hashcash like 'proof' of model having being trained was defined. It should be possible to do so, since the very least measure of model having gotten smarter with some additional data, is that it will recognize/infer the said additional data correctly.

cremer 7mo ago

[dead]

thesuperevil 7mo ago

That’s interesting

niea_11 7mo ago

I'm confused by the last part saying that if "weak" models (like gpt oss) find the openbsd bug they are just hallucinating. and also stronger models not finding it is because they dont hallucinate but are not strong enough. 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

4qskhaqj 7mo ago

Mythos is just used to get new business contacts: 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-exposes-eu-ai-office-struggles/ 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-leaders-warn-over-claude-mythos-as-uk-banks-prepare-to-use-powerful-anthropic-ai-tool 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.

cowartc 7mo ago

Hallucination vs real finding distinction is the core problem and doesn't get solved by a better model alone. It gets solved by what you do with the output. The verification layer is what makes the system production grade.

HiramRoosevelt 7mo ago

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