[HN Gopher] AI cybersecurity is not proof of work
___________________________________________________________________
AI cybersecurity is not proof of work
Recent and related: _Cybersecurity looks like proof of work now_ -
https://news.ycombinator.com/item?id=47769089 - (198 comments)
Author : surprisetalk
Score : 187 points
Date : 2026-04-16 10:48 UTC (12 hours ago)
(HTM) web link (antirez.com)
(TXT) w3m dump (antirez.com)
| andersmurphy wrote:
| > 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 wrote:
| 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 wrote:
| Amtrak is slow and expensive, but the hype train is free!
| neutered_knot wrote:
| 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 wrote:
| > 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 wrote:
| 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 wrote:
| 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 wrote:
| 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.
| baxtr wrote:
| Interestingly enough: earlier today this discussion was trending:
| https://news.ycombinator.com/item?id=47769089 (Cybersecurity
| looks like proof of work now)
| RugnirViking wrote:
| the article here is pretty clearly a response to the one you
| posted
| onionisafruit wrote:
| It's only clear if you know it exists, and now I know it
| exists thanks to gp.
| qsort wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| 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.
| nine_k wrote:
| > _essentially bug-free_
|
| 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.
| redwood wrote:
| 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 wrote:
| > 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 wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| 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.
| alex_young wrote:
| A bit uncharitable no?
|
| 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.
| pixl97 wrote:
| Quantity is a quality.
|
| 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.
| i_think_so wrote:
| Ahem. Let's please don't go off into areas outside of the
| topic and end up repeating political talking points from
| people with agendas.
|
| 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.
| tracker1 wrote:
| You still need people in the mix that understand the scope,
| scale and impact of the exploits/bugs found. Just letting
| agents go wild is how you get slop over time... You can
| probably get away without them to an extent, but I'd suggest
| that you're likely to increase the risk of errors and
| misbehavior in practice over time by not checking agent work.
|
| Even checking human work is often a shortcoming of processes
| in practice.
| Glemllksdf wrote:
| It reduces the cost significantly.
|
| 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.
| eikenberry wrote:
| > It reduces the cost significantly.
|
| > Now you can just throw money at it.
|
| What happens when you throw enough money at it that it raises
| the cost significantly.
| Glemllksdf wrote:
| But thats the thing, its already competitive and its not
| even released.
|
| 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.
| i_think_so wrote:
| _chef 's kiss_
|
| 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.
| dwa3592 wrote:
| 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 wrote:
| maybe a human knowledgeable in the domain (the training) is
| better than a smart liguist-programmer.
| rakejake wrote:
| >> 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 wrote:
| 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 wrote:
| 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.
| 2983592 wrote:
| 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.
| afthonos wrote:
| > 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.
| SpicyLemonZest wrote:
| 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 wrote:
| And, as we all know, humans can't be scammers. They need
| the robots to lie.
| Glemllksdf wrote:
| 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.
| the_snooze wrote:
| >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 wrote:
| > 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 wrote:
| 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.
| zzzeek wrote:
| 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.
| frank-romita wrote:
| 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 wrote:
| If it wasn't marketing it wouldn't have fancy branding... It
| wouldn't even be announced.
| louiereederson wrote:
| 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 wrote:
| 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 wrote:
| 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
| antirez wrote:
| 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 wrote:
| Model cards are just marketing material. I wouldn't trust
| them one bit.
| 2983592 wrote:
| But they are treated as holy scripture ...
| Yokohiii wrote:
| 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 wrote:
| > _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.
| inetknght wrote:
| > _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.
| Yokohiii wrote:
| 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 wrote:
| 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.
| mbesto wrote:
| 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.
| antirez wrote:
| 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 wrote:
| 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 wrote:
| 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 wrote:
| 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.
| rakejake wrote:
| >>> 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?
| zahlman wrote:
| > Hackers are very good programmers
|
| This does not match my experience.
| ang_cire wrote:
| 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.
| Glemllksdf wrote:
| 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.
| csmantle wrote:
| > _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 wrote:
| 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 wrote:
| 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.
| douglaswlance wrote:
| you get better models with more compute.
|
| its not just PoW at inference. It's PoW of inference + training.
| riteshkew1001 wrote:
| 'Calling AI vuln-finding 'hallucination plus luck' is generous, a
| lot of human pentesting fits the same description.
| EGreg wrote:
| 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.
| ramoz wrote:
| > 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 wrote:
| So kind of like how you would get nowhere by buying more gpus
| if there's already ASICs in play.
| slopinthebag wrote:
| > 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 wrote:
| 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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