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  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.
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  dwa3592
  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).
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  alex_young
  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.
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  qsort
  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.
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  ramoz
  > 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 wintomato, tomato
• neutered_knot
  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.
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  4qwUz
  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.
• kang
  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.
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  egormakarov
  > Different LLMs executions take different branches, but eventually the possible branches based on the code possible states are saturatedWith LLMs even the halting problem is just the question of paying for pro subscription!
• gobdovan
  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.
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  baxtr
  Interestingly enough: earlier today this discussion was trending: https://news.ycombinator.com/item?id=47769089 (Cybersecurity looks like proof of work now)
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  csmantle
  > 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.
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  nottorp
  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.
• riteshkew1001
  'Calling AI vuln-finding 'hallucination plus luck' is generous, a lot of human pentesting fits the same description.
• andersmurphy
  > What happens is that weak models hallucinate (sometimes causally hitting a real problem)So the bigger models hallucinate better causally hitting more real problems?
• douglaswlance
  you get better models with more compute.its not just PoW at inference. It's PoW of inference + training.
• slopinthebag
  > 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 :)
• redwood
  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
• EGreg
  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.
• anon
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• jeremie_strand
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• kvikuz
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