alt Hacker NewsCybersecurity looks like proof of work now
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There's still the question of access to the codebase. By all accounts, the best LLM cyber scanning approaches are really primitive - it's just a bash script that goes through every single file in the codebase and, for each one and runs a "find the vulns here" prompt. The attacker usually has even less access than this - in the beginning, they have network tools, an undocumented API, and maybe some binaries.
You can do a lot better efficiency-wise if you control the source end-to-end though - you already group logically related changes into PRs, so you can save on scanning by asking the LLM to only look over the files you've changed. If you're touching security-relevant code, you can ask it for more per-file effort than the attacker might put into their own scanning. You can even do the big bulk scans an attacker might on a fixed schedule - each attacker has to run their own scan while you only need to run your one scan to find everything they would have. There's a massive cost asymmetry between the "hardening" phase for the defender and the "discovering exploits" phase for the attacker.
Exploitability also isn't binary: even if the attacker is better-resourced than you, they need to find a whole chain of exploits in your system, while you only need to break the weakest link in that chain.
If you boil security down to just a contest of who can burn more tokens, defenders get efficiency advantages only the best-resourced attackers can overcome. On net, public access to mythos-tier models will make software more secure.
The article heavily quotes the "AI Security Institute" as a third-party analysis. It was the first I heard of them, so I looked up their about page, and it appears to be primarily people from the AI industry (former Deepmind/OpenAI staff, etc.), with no folks from the security industry mentioned. So while the security landscape is clearly evolving (cf. also Big Sleep and Project Zero), the conclusion of "to harden a system we need to spend more tokens" sounds like yet more AI boosting from a different angle. It raises the question of why no other alternatives (like formal verification) are mentioned in the article or the AISI report.
I wouldn't be surprised if NVIDIA picked up this talking point to sell more GPUs.
Everything in modern corporate is just proof of work. Security is filling out forms. Engineering is just endless talking. Token-maxing is the new meta.
Relevant Tony Hoare quote: “There are two approaches to software design: make it so simple there are obviously no deficiencies, or make it so complex there are no obvious deficiencies”.
Security has always been a game of just how much money your adversary is willing to commit. The conclusions drawn in lots of these articles are just already well understood systems design concepts, but for some reason people are acting like they are novel or that LLMs have changed anything besides the price.
For example from this article:
> Karpathy: Classical software engineering would have you believe that dependencies are good (we’re building pyramids from bricks), but imo this has to be re-evaluated, and it’s why I’ve been so growingly averse to them, preferring to use LLMs to “yoink” functionality when it’s simple enough and possible.
Anyone who's heard of "leftpad" or is a Go programmer ("A little copying is better than a little dependency" is literally a "Go Proverb") knows this.
Another recent set of posts to HN had a company close-sourcing their code for security, but "security through obscurity" has been a well understand fallacy in open source circles for decades.
> to harden a system you need to spend more tokens discovering exploits than attackers will spend exploiting them.
I, for the NFL front offices, created a script that exposed an API to fully automate Ticketmaster through the front end so that the NFL could post tickets on all secondary markets and dynamic price the tickets so if rain on a Sunday was expected they could charge less. Ticketmaster was slow to develop an API. Ticketmaster couldn't provide us permission without first developing the API first for legal reasons but told me they would do their best to stop me.
They switched over to PerimeterX which took me 3 days to get past.
Last week someone posted an article here about ChatGPT using Cloudflare Turnstile. [0] First, the article made some mistakes how it works. Second, I used the [AI company product] and the Chrome DevTools Protocol (CDP) to completely rewrite all the scripts intercepting them before they were evaluated -- the same way I was able to figure out PerimeterX in 3 days -- and then recursively solve controlling all the finger printing so that it controls the profile. Then it created an API proxy to expose ChatGPT for free. It required some coaching about the technique but it did most of the work in 3 hours.
These companies are spending 10s of millions of dollars on these products and considering what OpenAI is boasting about security, they are worthless.
It looks like proof of work because:
> Worryingly, none of the models given a 100M budget showed signs of diminishing returns. “Models continue making progress with increased token budgets across the token budgets tested,” AISI notes.
So, the author infers a durable direct correlation between token spend and attack success. Thus you will need to spend more tokens than your attackers to find your vulnerabilities first.
However it is worth noting that this study was of a 32-step network intrusion, which only one model (Mythos) even was able to complete at all. That’s an incredibly complex task. Is the same true for pointing Mythos at a relatively simple single code library? My intuition is that there is probably a point of diminishing returns, which is closer for simpler tasks.
In this world, popular open source projects will probably see higher aggregate token spend by both defenders and attackers. And thus they might approach the point of diminishing returns faster. If there is one.
There are never ending ways to make agents better at hacking. Defense is clearly behind. At my startup we are constantly coming up with new defensive measures to put our hacking agent Sable against, and I've determined that you basically need to be air gapped in the future for a chance of survival. A SOC of AI agents can't keep up with 1 AI hacker on a network that is even remotely stealthy. it is a disaster. wrote an article about it: https://blog.vulnetic.ai/evading-an-ai-soc-with-sable-from-v...
I discussed this in more detail in one of my earlier comments, but I think the article commits a category error. In commercial settings, most of day-to-day infosec work (or spending) has very little to do with looking for vulnerabilities in code.
In fact, security programs built on the idea that you can find and patch every security hole in your codebase were basically busted long before LLMs.
I don't know about Mythos but the chart understates the capability of the current frontier models. GPT and Claude models available today are capable of Web app exploits, C2, and persistence in well under 10M tokens if you build a good harness.
The benchmark might be a good apples-to-apples comparison but it is not showing capability in an absolute sense.
My first thought seeing the title: "always has been"
It looks like it, but it isn't. It's the work itself that's valued in software security, not the amount of it you managed to do. The economics are fundamentally different.
Put more simply: to keep your system secure, you need to be fixing vulnerabilities faster than they're being discovered. The token count is irrelevant.
Moreover: this shift is happening because the automated work is outpacing humans for the same outcome. If you could get the same results by hand, they'd count! A sev:crit is a sev:crit is a sev:crit.
All of the recent news read like something that could happen in a cyberpunk novel - AIs that defend vs AIs that do the attacks.
I think were are already here. I wrote something about this, if you are interested: https://go.cbk.ai/security-agents-need-a-thinner-harness
I've said for decades that, in principle, cybersecurity is advantage defender. The defender has to leave a hole. The attackers have to find it. We just live in a world with so many holes that dedicated attackers rarely end up bottlenecked on finding holes, so in practice it ends up advantage attacker.
There is at least a possibility that a code base can be secured by a (practically) finite number of tokens until there is no more holes in it, for reasonable amounts of money.
This also reminds me of what I wrote here: https://jerf.org/iri/post/2026/what_value_code_in_ai_era/ There's still value in code tested by the real world, and in an era of "free code" that may become even more true than it is now, rather than the initially-intuitive less valuable. There is no amount of testing you can do that will be equivalent to being in the real world, AI-empowered attackers and all.
> Classical software engineering would have you believe that dependencies are good (we’re building pyramids from bricks)
Would it? I’m old school but I’ve never trusted these massive dependency chains.
That’s a nit.
We’re going to have to write more secure software, not just spend more.
If you have a limited budget of tokens as a defender, maybe the best thing to spend them on is not red teaming, but formalizing proofs of your code's security. Then the number of tokens required roughly scales with the amount and complexity of your code, instead of scaling with the number of tokens an attacker is willing to spend.
(It's true that formalization can still have bugs in the definition of "secure" and doesn't work for everything, which means defenders will still probably have to allocate some of their token budget to red teaming.)
Cybersecurity has always been proof of work. Fuck, most of software development is proof of work by that logic. Thats why many attacks originate from countries were the cost of living is a fraction of the COL in the United States. They can throw more people at the problem because its cheaper to do so.
But I don't really get the hype, we can fix all the vulnerabilities in the world but people are still going to pick up parking-lot-USBs and enter their credentials into phishing sites.
Security always had “defender’s dilemma” (an attacker needs to find one thing, but defender needs to fix everything) problem, nothing is new in terms of AI’s impact just application of different resources and units.
> Cybersecurity looks like proof of work now
Imo, cybersecurity looks like formally verified systems now.
You can't spend more tokens to find vulnerabilities if there are no vulnerabilities.
I'm starting to think that Opus and Mythos are the same model (or collection of models) whereas Mythos has better backend workflows than Opus 4.6. I have not used Mythos, but at work I have a 5 figure monthly token budget to find vulnerabilities in closed-source code. I'm interested in mythos and will use it when it's available, but for now I'm trying to reverse engineer how I can get the same output with Opus 4.6 and the answer to me is more tokens.
Does this mean all code written before Mythos is a liability?
Although not an escape from the "who can spend the most on tokens" arms race, there is also the possibility to make reverse engineering and executable analysis more difficult. This increases the attacker's token spend if nothing else. I wonder if dev teams will take an interest.
Better to write good, high-quality, properly architected and tested software in the first place of course.
Edited for typo.
>You don’t get points for being clever. You win by paying more.
Really depends how consistently the LLMs are putting new novel vulnerabilities back in your production code for the other LLMs to discover.
I'm curious to see if formally verified software will get more popular. I'm somewhat doubtful, since getting programmers to learn formally math is hard (rightfully so, but still sad). But, if LLMs could take over the drudgery of writing proofs in a lot of the cases, there might be something there.
By using these services, you're also exfiltrating your entire codebase to them, so you have to continuously use the best cyber capabilities providers offer in case a data breach allows somebody to obtain your codebase and an attacker uses a better vulnerability detector than what you were using.
If you run this long enough presumably it will find every exploit and you patch them all and run it again to find exploits in your patches until there simply... Are no exploits?
we did a lot of thinking around this topic. and distilled it into a new way to dynamically evaluate the security posture of an AI system (which can apply for any system for that matter). we wrote some thoughts on this here: https://fabraix.com/blog/adversarial-cost-to-exploit
> You don’t get points for being clever. You win by paying more.
And yet... Wireguard was written by one guy while OpenVPN is written by a big team. One code base is orders of magnitude bigger than the other. Which should I bet LLMs will find more cybersecurity problems with? My vote is on OpenVPN despite it being the less clever and "more money thrown at" solution.
So yes, I do think you get points for being clever, assuming you are competent. If you are clever enough to build a solution that's much smaller/simpler than your competition, you can also get away with spending less on cybersecurity audits (be they LLM tokens or not).
> to harden a system you need to spend more tokens discovering exploits than attackers will spend exploiting them.
If we take this at face value, it's not that different than how a great deal of executive teams believe cybersecurity has worked up to today. "If we spend more on our engineering and infosec teams, we are less likely to get compromised".
The only big difference I can see is timescale. If LLMs can find vulnerabilities and exploit them this easily (and I do take that with a grain of salt, because benchmarks are benchmarks), then you may lose your ass in minutes instead of after one dedicated cyber-explorer's monster energy fueled, 7-week traversal of your infrastructure.
I am still far more concerned about social engineering than LLMs finding and exploiting secret back doors in most software.
people biting into what companies say about their own products had always been the frustration in cyber. now more than ever.
nothing is better or worse, basically as its always been.
if you think otherwise, stop ignoring the past.
Everything eventually turns into Bitcoin. That’s what I plan to see in the future years and decades.
Please. Are we going to rely now in Anthropic et al to secure our systems? Wasn’t enough to rely on them to build our systems? What’s next? To rely on them for monitoring and observability? What else? Design and mockups?
Everything eventually turns into Bitcoin. That’s what I plan to see in the future years and decades. Satoshi just saw it first.
Dijkstra would shake his head at our folly.
In other news, token seller says tokens should be bought
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Trusted software will be so expensive that it will effectively kill startups for infrastructure, unless they can prove they spent millions of dollars hardening their software.
I predict the software ecosystem will change in two folds: internal software behind a firewall will become ever cheaper, but anything external facing will become exponential more expensive due to hacking concern.