There's a lot of discourse on Twitter about people using LLMs to solve CTF challenges.
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There's a lot of discourse on Twitter about people using LLMs to solve CTF challenges. I used to write CTF challenges in a past life, so I threw a couple of my hardest ones at it.
We're screwed.
At least with text-file style challenges ("source code provided" etc), Claude Opus solves them quickly. For the "simpler" of the two, it just very quickly ran through the steps to solve it. For the more "ridiculous" challenge, it took a long while, and in fact as I type this it's still burning tokens "verifying" the flag even though it very obviously found the flag and it knows it (it's leetspeak and it identified that and that it's plausible). LLMs are, indeed, still completely unintelligent, because no human would waste time verifying a flag and second-guessing itself when it very obviously is correct. (Also you could just run it...)
But that doesn't matter, because it found it.
The thing is, CTF challenges aren't about inventing the next great invention or having a rare spark of genius. CTF challenges are about learning things by doing. You're supposed to enjoy the process. The whole point of a well-designed CTF challenge is that anyone, given enough time and effort and self-improvement and learning, can solve it. The goal isn't actually to get the flag, otherwise you'd just ask another team for the flag (which is against the rules of course). The goal is to get the flag by yourself. If you ask an LLM to get the flag for you, you aren't doing that.
(Continued)
So it's not surprising that an LLM can solve them, because it automates the process. That just takes all the fun and all the learning out of it, completely defeating the purpose.
I'm sure you could still come up with challenges that LLMs can't solve, but they would necessarily be harder, because LLMs are going to oneshot any of the "baby" starter challenges you could possibly come up with. So you either get rid of the "baby" challenges entirely (which means less experienced teams can't compete at all), or you accept that people will solve them with LLMs. But neither of those actually works.
Since CTF competitions are pretty much by definition timed, speed is an advantage. That means a team that does not use LLMs will not win, so teams must use LLMs. This applies to both new and experienced teams. But: A newbie team using LLMs will not learn. Because the whole point is learning by doing, and you're not doing anything. And so will not become experienced.
So this is going to devolve into CTFs being a battle of teams using LLMs to fight for the top spots, where everyone who doesn't want to use an LLM is excluded, and where less experienced teams stop improving and getting better, because they're outsourcing the work to LLMs and not learning as a result.
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So it's not surprising that an LLM can solve them, because it automates the process. That just takes all the fun and all the learning out of it, completely defeating the purpose.
I'm sure you could still come up with challenges that LLMs can't solve, but they would necessarily be harder, because LLMs are going to oneshot any of the "baby" starter challenges you could possibly come up with. So you either get rid of the "baby" challenges entirely (which means less experienced teams can't compete at all), or you accept that people will solve them with LLMs. But neither of those actually works.
Since CTF competitions are pretty much by definition timed, speed is an advantage. That means a team that does not use LLMs will not win, so teams must use LLMs. This applies to both new and experienced teams. But: A newbie team using LLMs will not learn. Because the whole point is learning by doing, and you're not doing anything. And so will not become experienced.
So this is going to devolve into CTFs being a battle of teams using LLMs to fight for the top spots, where everyone who doesn't want to use an LLM is excluded, and where less experienced teams stop improving and getting better, because they're outsourcing the work to LLMs and not learning as a result.
This is, quite frankly, the same problem LLM agents are causing in software engineering and such, just way worse. Because with CTFs, there is no "quality metric". Once you get the flag you get the flag. It doesn't matter if your approach was ridiculous or you completely misunderstood the problem or "winged it" in the worst way possible or the solver is a spaghetti ball of technical debt. It doesn't matter if Claude made a dozen reasoning errors in its chain that no human would (which it did). Every time it gets it wrong it just tries again, and it can try again orders of magnitude faster than a human, so it doesn't matter.
I don't have a solution for this. You can't ban LLMs, people will use them regardless. You could try interviewing teams one on one after the challenge to see if they actually have a coherent story and clearly did the work, but even then you could conceivably cheat using an LLM and then wait it out a bit to make the time spent plausible, study the reasoning chain, and convince someone that you did the work. It's like LLMs in academics, but much worse due to the time constraints and explicitly competitive nature of CTFs.
LLMs broke CTFs.
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This is, quite frankly, the same problem LLM agents are causing in software engineering and such, just way worse. Because with CTFs, there is no "quality metric". Once you get the flag you get the flag. It doesn't matter if your approach was ridiculous or you completely misunderstood the problem or "winged it" in the worst way possible or the solver is a spaghetti ball of technical debt. It doesn't matter if Claude made a dozen reasoning errors in its chain that no human would (which it did). Every time it gets it wrong it just tries again, and it can try again orders of magnitude faster than a human, so it doesn't matter.
I don't have a solution for this. You can't ban LLMs, people will use them regardless. You could try interviewing teams one on one after the challenge to see if they actually have a coherent story and clearly did the work, but even then you could conceivably cheat using an LLM and then wait it out a bit to make the time spent plausible, study the reasoning chain, and convince someone that you did the work. It's like LLMs in academics, but much worse due to the time constraints and explicitly competitive nature of CTFs.
LLMs broke CTFs.
@lina LLMs broke the whole damn industry. We have a new "full stack" guy on our team who's there because his our boss' son and he was put there as a junior full stack to "learn".
He's using Claude or copilot or whatever the fuck, I don't differentiate them by interface, but some LLM, constantly, to solve ridiculously easy tickets that I used to welcome as a learning opportunity when I was his expertise. He will never learn.
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This is, quite frankly, the same problem LLM agents are causing in software engineering and such, just way worse. Because with CTFs, there is no "quality metric". Once you get the flag you get the flag. It doesn't matter if your approach was ridiculous or you completely misunderstood the problem or "winged it" in the worst way possible or the solver is a spaghetti ball of technical debt. It doesn't matter if Claude made a dozen reasoning errors in its chain that no human would (which it did). Every time it gets it wrong it just tries again, and it can try again orders of magnitude faster than a human, so it doesn't matter.
I don't have a solution for this. You can't ban LLMs, people will use them regardless. You could try interviewing teams one on one after the challenge to see if they actually have a coherent story and clearly did the work, but even then you could conceivably cheat using an LLM and then wait it out a bit to make the time spent plausible, study the reasoning chain, and convince someone that you did the work. It's like LLMs in academics, but much worse due to the time constraints and explicitly competitive nature of CTFs.
LLMs broke CTFs.
@lina This saddens me to hear. We host an industrial control system themed CTF and LLMs haven't quite gotten to the point of being useful to solve challenges yet. But I can totally see them catching up.
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R relay@relay.infosec.exchange shared this topic
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@lina LLMs broke the whole damn industry. We have a new "full stack" guy on our team who's there because his our boss' son and he was put there as a junior full stack to "learn".
He's using Claude or copilot or whatever the fuck, I don't differentiate them by interface, but some LLM, constantly, to solve ridiculously easy tickets that I used to welcome as a learning opportunity when I was his expertise. He will never learn.
@lina Yet it's me who will be let go eventually, because I can't "solve" tickets as fast as a guy with no experience, all of the ambition and fuck ton of Claude tokens does.
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@lina Yet it's me who will be let go eventually, because I can't "solve" tickets as fast as a guy with no experience, all of the ambition and fuck ton of Claude tokens does.
@Deiru@gensokyo.social @lina@vt.social the corporations will implode from thier over reliance of immature AI
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This is, quite frankly, the same problem LLM agents are causing in software engineering and such, just way worse. Because with CTFs, there is no "quality metric". Once you get the flag you get the flag. It doesn't matter if your approach was ridiculous or you completely misunderstood the problem or "winged it" in the worst way possible or the solver is a spaghetti ball of technical debt. It doesn't matter if Claude made a dozen reasoning errors in its chain that no human would (which it did). Every time it gets it wrong it just tries again, and it can try again orders of magnitude faster than a human, so it doesn't matter.
I don't have a solution for this. You can't ban LLMs, people will use them regardless. You could try interviewing teams one on one after the challenge to see if they actually have a coherent story and clearly did the work, but even then you could conceivably cheat using an LLM and then wait it out a bit to make the time spent plausible, study the reasoning chain, and convince someone that you did the work. It's like LLMs in academics, but much worse due to the time constraints and explicitly competitive nature of CTFs.
LLMs broke CTFs.
And honestly, reading the Claude output, it's just ridiculous. It clearly has no idea what it's doing and it's just pattern-matching. Once it found the flag it spent 7 pages of reasoning and four more scripts trying to verify it, and failed to actually find what went wrong. It just concluded after all that time wasted that sometimes it gets the right answer and sometimes the wrong answer and so probably the flag that looks like a flag is the flag. It can't debug its own code to find out what actually went wrong, it just decided to brute force try again a different way.
It's just a pattern-matching machine. But it turns out if you brute force pattern-match enough times in enough steps inside a reasoning loop, you eventually stumble upon the answer, even if you have no idea how.
Humans can "wing it" and pattern-match too, but it's a gamble. If you pattern-match wrong and go down the wrong path, you just wasted a bunch of time and someone else wins. Competitive CTFs are all about walking the line between going as fast as possible and being very careful so you don't have to revisit, debug, and redo a bunch of your work. LLMs completely screw that up by brute forcing the process faster than humans.
This sucks.
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And honestly, reading the Claude output, it's just ridiculous. It clearly has no idea what it's doing and it's just pattern-matching. Once it found the flag it spent 7 pages of reasoning and four more scripts trying to verify it, and failed to actually find what went wrong. It just concluded after all that time wasted that sometimes it gets the right answer and sometimes the wrong answer and so probably the flag that looks like a flag is the flag. It can't debug its own code to find out what actually went wrong, it just decided to brute force try again a different way.
It's just a pattern-matching machine. But it turns out if you brute force pattern-match enough times in enough steps inside a reasoning loop, you eventually stumble upon the answer, even if you have no idea how.
Humans can "wing it" and pattern-match too, but it's a gamble. If you pattern-match wrong and go down the wrong path, you just wasted a bunch of time and someone else wins. Competitive CTFs are all about walking the line between going as fast as possible and being very careful so you don't have to revisit, debug, and redo a bunch of your work. LLMs completely screw that up by brute forcing the process faster than humans.
This sucks.
I might still do a monthly challenge or something in the future so people who want to have fun and learn can have fun and learn. That's still okay.
But CTFs as discrete competitions with winners are dead.
A CTF competition is basically gameified homework.
LLMs broke the game. Now all that's left is self study.
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I might still do a monthly challenge or something in the future so people who want to have fun and learn can have fun and learn. That's still okay.
But CTFs as discrete competitions with winners are dead.
A CTF competition is basically gameified homework.
LLMs broke the game. Now all that's left is self study.
@lina For in-person CTF competitions, would it be possible to do like programming competitions (specifically thinking of ACM ICPC) and disallow Internet access entirely? That would at least limit GenAI use to local models, which I suspect will remain uncompetitive at this sort of task for a very long time (due to the nearly inherent context size limitations).
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So it's not surprising that an LLM can solve them, because it automates the process. That just takes all the fun and all the learning out of it, completely defeating the purpose.
I'm sure you could still come up with challenges that LLMs can't solve, but they would necessarily be harder, because LLMs are going to oneshot any of the "baby" starter challenges you could possibly come up with. So you either get rid of the "baby" challenges entirely (which means less experienced teams can't compete at all), or you accept that people will solve them with LLMs. But neither of those actually works.
Since CTF competitions are pretty much by definition timed, speed is an advantage. That means a team that does not use LLMs will not win, so teams must use LLMs. This applies to both new and experienced teams. But: A newbie team using LLMs will not learn. Because the whole point is learning by doing, and you're not doing anything. And so will not become experienced.
So this is going to devolve into CTFs being a battle of teams using LLMs to fight for the top spots, where everyone who doesn't want to use an LLM is excluded, and where less experienced teams stop improving and getting better, because they're outsourcing the work to LLMs and not learning as a result.
@lina@vt.social
That is (yet another) sad development to hear about.
I just hope CTFs will be made/hosted regardless. I have never cared too much about the time and more about finishing them and it'd be a shame if that option of learning were to disappear or get way less accessible. -
@lina For in-person CTF competitions, would it be possible to do like programming competitions (specifically thinking of ACM ICPC) and disallow Internet access entirely? That would at least limit GenAI use to local models, which I suspect will remain uncompetitive at this sort of task for a very long time (due to the nearly inherent context size limitations).
@MrDOS Maybe, but in-person CTFs are themselves biased towards more privileged people and more advanced teams. We need online CTFs for the pipeline to work...
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@MrDOS Maybe, but in-person CTFs are themselves biased towards more privileged people and more advanced teams. We need online CTFs for the pipeline to work...
@lina Yeah, of course – and in reverse, I'm sure online competitive programming competitions are staring down the barrel of the same problem!
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This is, quite frankly, the same problem LLM agents are causing in software engineering and such, just way worse. Because with CTFs, there is no "quality metric". Once you get the flag you get the flag. It doesn't matter if your approach was ridiculous or you completely misunderstood the problem or "winged it" in the worst way possible or the solver is a spaghetti ball of technical debt. It doesn't matter if Claude made a dozen reasoning errors in its chain that no human would (which it did). Every time it gets it wrong it just tries again, and it can try again orders of magnitude faster than a human, so it doesn't matter.
I don't have a solution for this. You can't ban LLMs, people will use them regardless. You could try interviewing teams one on one after the challenge to see if they actually have a coherent story and clearly did the work, but even then you could conceivably cheat using an LLM and then wait it out a bit to make the time spent plausible, study the reasoning chain, and convince someone that you did the work. It's like LLMs in academics, but much worse due to the time constraints and explicitly competitive nature of CTFs.
LLMs broke CTFs.
How does this statement differ from "DeepBlue broke chess"? Cheat engines are similarly impossible to deterministically detect in online competition, yet the game is more popular than ever.
The competition format will have to adapt, which sucks, but if the majority of participants can agree that LLMs are cheats, then the community should be able to adapt & self-police like any other game community where cheats are easily accessible. Unless I'm missing something special about CTFs?
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How does this statement differ from "DeepBlue broke chess"? Cheat engines are similarly impossible to deterministically detect in online competition, yet the game is more popular than ever.
The competition format will have to adapt, which sucks, but if the majority of participants can agree that LLMs are cheats, then the community should be able to adapt & self-police like any other game community where cheats are easily accessible. Unless I'm missing something special about CTFs?
@nathan It's worse because it's not a linear game like chess. You aren't competing move-wise, you are going down your own path where there is no interaction between teams. There's no way to detect that in online competition, even heuristically. There's no realtime monitoring. There isn't any condensed format that describes "what you did". At most you could stream yourself to some kind of video escrow system, but then who is going to watch those? And if you make them public after the competition, you are giving away your tools to everyone. And you could still have an LLM on the side on another machine and parallel construct the whole thing plausibly.
Sure you could do in-person only, but that would only work for the top tiers and who is going to want to learn and grow online when a huge number of people are going to be cheating online?
It's the same with any kind of game. Sure cheating is barely a concern in-person, but people hate cheaters online, and companies still try hard to detect cheaters. And detecting cheaters for a CTF is nigh impossible.
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There's a lot of discourse on Twitter about people using LLMs to solve CTF challenges. I used to write CTF challenges in a past life, so I threw a couple of my hardest ones at it.
We're screwed.
At least with text-file style challenges ("source code provided" etc), Claude Opus solves them quickly. For the "simpler" of the two, it just very quickly ran through the steps to solve it. For the more "ridiculous" challenge, it took a long while, and in fact as I type this it's still burning tokens "verifying" the flag even though it very obviously found the flag and it knows it (it's leetspeak and it identified that and that it's plausible). LLMs are, indeed, still completely unintelligent, because no human would waste time verifying a flag and second-guessing itself when it very obviously is correct. (Also you could just run it...)
But that doesn't matter, because it found it.
The thing is, CTF challenges aren't about inventing the next great invention or having a rare spark of genius. CTF challenges are about learning things by doing. You're supposed to enjoy the process. The whole point of a well-designed CTF challenge is that anyone, given enough time and effort and self-improvement and learning, can solve it. The goal isn't actually to get the flag, otherwise you'd just ask another team for the flag (which is against the rules of course). The goal is to get the flag by yourself. If you ask an LLM to get the flag for you, you aren't doing that.
(Continued)
@lina I feel exactly the same about academia. I dunno about anyone else, but I genuinely enjoyed learning new things in school, building new ways of thinking and new skills. Sharpening my mind was the best feeling in the world back then, when I was young and still had all that neuroplasticity. It's mystifying that anyone would rather subsume their thinking to the chatbot than build new skills.
But then again, lots of young adults in the US are ushered into university and told that it's their only option, even if they don't particularly care for the subjects they're ostensibly supposed to be learning. I was privileged enough that I didn't have to work through college, for instance.
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This is, quite frankly, the same problem LLM agents are causing in software engineering and such, just way worse. Because with CTFs, there is no "quality metric". Once you get the flag you get the flag. It doesn't matter if your approach was ridiculous or you completely misunderstood the problem or "winged it" in the worst way possible or the solver is a spaghetti ball of technical debt. It doesn't matter if Claude made a dozen reasoning errors in its chain that no human would (which it did). Every time it gets it wrong it just tries again, and it can try again orders of magnitude faster than a human, so it doesn't matter.
I don't have a solution for this. You can't ban LLMs, people will use them regardless. You could try interviewing teams one on one after the challenge to see if they actually have a coherent story and clearly did the work, but even then you could conceivably cheat using an LLM and then wait it out a bit to make the time spent plausible, study the reasoning chain, and convince someone that you did the work. It's like LLMs in academics, but much worse due to the time constraints and explicitly competitive nature of CTFs.
LLMs broke CTFs.
@lina I mean, yes, but I don't know if complete pessimism is warranted. It's definitely broken a lot of public CTFs but I think society will find a way, and maybe it's not even the worst thing.
Forever ago, when I was in uni, a few colleagues and I would do this thing every semester where we'd do one of the nominally individual projects together, ahead of time. Technically, it was cheating, but we did it specifically so we could go "off the rails" and try things that were not in the guide that our TAs handed out to us.
For instance, the "rite of passage" for every 3rd year student in my generation was a transformer design. It was something you'd work on, on and off, for the whole semester (we're talking big three-phase transformers for power distribution here so there was definitely *a lot* to work on).
They'd give us a sort of step-by-step guide to walk us through the whole process (start here, compute this quantity, check it against this standard table etc.) and you'd consult with the TAs along the semester. It was definitely interesting, if tedious at times, but tediousness was the lesser problem.
The bigger deal was that these guides weren't updated very often -- because the associated industrial standards don't get updated that often.
So what we did was that those of us who actually wanted to be there in the first place got together and we tried to experiment with various things not in the guide. Different isolation materials that we'd just read about, different cooling methods and so on. Not that we could show those to the TAs (can't blame them but most of them weren't very interested), and we didn't always have a lot of time or access to all the data we needed (we were students and had student budgets to contend with -- we couldn't buy standards, for example, and this was before libgen).
The cool thing about it was that it removed any kind of metrics pressure from this process. We weren't going to be ranked by anyone, there were no arsehole TAs to cater towards and no obtuse professors whose personal preferences in the formatting of our reports who had to be placated.
We also didn't have to show our results to anyone who wasn't primarily interested in mentoring us. We worked *really* quickly because we had graded assignments to finish first and clung to whatever had remained of our social lives by the third year of an engineering degree, so "deadlines" were super tight.
That quickly removed any incentive to cheat. When there was no way around it (tl;dr outdated guides sometimes didn't work in the context we used them, I have some fun stories about that) we totally cheated on the "real" assignments -- but never on these ones. This was technically cheating, too -- in the process of working out these differences we'd obviously discuss how we'd gone through the "real" assignments, share results and so on -- but since we all had different design targets (tl;dr same transformer designs but with different target parameters, so you couldn't just copy your colleague's work) it wasn't really a big deal.
With no incentive to cheat and nothing to get ahead of other than the limits of our own knowledge and engineering abilities, we often found ourselves doing things we normally wouldn't do for our regular assignments. We couldn't try things out in a lab, so if we doubted our analytical results for some particular configuration, we'd compare it against general EM field numerical simulations. If we didn't have a good simulation package for what we were after, we'd try to work out different analytical solutions for related quantities and see if we got similar results were similar.
We ended up learning a lot more than we did from the "real" assignments, mostly because our priorities were different. With real assignment, your main objective was inevitably to get a high grade, and keeping the TAs and the prof happy were as critical as tracking the decimal point.
Whereas with our "social" assignments, our main objectives were 1. to learn new things and 2. to get something that looked like a workable design that was an improvement over the "real" one in some aspect of our choice (better efficiency, reduced size, less coolant, whatever). If you "cheated" your way through it, #1 was obviously not happening and you were never really sure of #2, so no one was motivated to do it.
I think this is what we're eventually going to converge towards in other spaces, too: CTFs organised in smaller circles, with fewer external metrics and motivators, and an emphasis on cooperation, shifting the "competition" towards external factors than competition among teams/team members.
When CTF scores matter because they could potentially get you ahead in the race for an intership, every twenty year-old will eventually give in to cheating -- if only because it's the only way to stay in the race with people who do it because it's the only way they *can* do it. But if you take out the cheese, it's not much of a rat race anymore.
I'm old enough to have seen this happen to hackathons to some degree. At first, after hackathons had grown into their "competitive" form from their "let's hack shit together" roots, everyone was super enthusiastic and people every age jumped in. After a while, when prep became intensive enough that the only way to a prize was to implement 90% of what you meant to do beforehand (e.g. in a library) and then show up on the day of the hackathon and just piece the frontend together, everyone who was in it primarily for the thrill of focused building noped out.
Did that stop hackathons? Not at all, it just "split" things into:
- Corporate-funded hackathons which almost no one attends after they finish school -- where people rarely produce anything of value, and it's fine, because everyone understands that's not what they're there for. The "cheese" wasn't explicitly removed here, it's just at some point almost everyone recognised it's unattainable and the amount of hoops you have to jump through in order to attain it just isn't worth it when you're programming professionally
- "Real" hackathons, where people get together to work on a real project together, and the only competition is maybe the how-much-wasabi-you-can-eat-without-crying competition when everyone goes out for sushi the next day. -
There's a lot of discourse on Twitter about people using LLMs to solve CTF challenges. I used to write CTF challenges in a past life, so I threw a couple of my hardest ones at it.
We're screwed.
At least with text-file style challenges ("source code provided" etc), Claude Opus solves them quickly. For the "simpler" of the two, it just very quickly ran through the steps to solve it. For the more "ridiculous" challenge, it took a long while, and in fact as I type this it's still burning tokens "verifying" the flag even though it very obviously found the flag and it knows it (it's leetspeak and it identified that and that it's plausible). LLMs are, indeed, still completely unintelligent, because no human would waste time verifying a flag and second-guessing itself when it very obviously is correct. (Also you could just run it...)
But that doesn't matter, because it found it.
The thing is, CTF challenges aren't about inventing the next great invention or having a rare spark of genius. CTF challenges are about learning things by doing. You're supposed to enjoy the process. The whole point of a well-designed CTF challenge is that anyone, given enough time and effort and self-improvement and learning, can solve it. The goal isn't actually to get the flag, otherwise you'd just ask another team for the flag (which is against the rules of course). The goal is to get the flag by yourself. If you ask an LLM to get the flag for you, you aren't doing that.
(Continued)
@lina most of the CTF include 6-7 challenges to be solved in 4 hours.
Those CTFs expect you to know a typical set of forensync tools managed by an external guy/gal/entity which is somewhat known to be able to do it in time.
It stops being funny when you stop learning by doing and starts being a "kill'em all" competition.
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This is, quite frankly, the same problem LLM agents are causing in software engineering and such, just way worse. Because with CTFs, there is no "quality metric". Once you get the flag you get the flag. It doesn't matter if your approach was ridiculous or you completely misunderstood the problem or "winged it" in the worst way possible or the solver is a spaghetti ball of technical debt. It doesn't matter if Claude made a dozen reasoning errors in its chain that no human would (which it did). Every time it gets it wrong it just tries again, and it can try again orders of magnitude faster than a human, so it doesn't matter.
I don't have a solution for this. You can't ban LLMs, people will use them regardless. You could try interviewing teams one on one after the challenge to see if they actually have a coherent story and clearly did the work, but even then you could conceivably cheat using an LLM and then wait it out a bit to make the time spent plausible, study the reasoning chain, and convince someone that you did the work. It's like LLMs in academics, but much worse due to the time constraints and explicitly competitive nature of CTFs.
LLMs broke CTFs.
@lina Programming competitions are banning LLMs, see e.g. https://info.atcoder.jp/entry/llm-rules-en. How are CTFs any different?
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@lina most of the CTF include 6-7 challenges to be solved in 4 hours.
Those CTFs expect you to know a typical set of forensync tools managed by an external guy/gal/entity which is somewhat known to be able to do it in time.
It stops being funny when you stop learning by doing and starts being a "kill'em all" competition.
@echedellelr The ones I've worked on are less about "forensic tooling" and more about diverse (reverse)engineering challenges. They also usually run for a couple days and ~16 chals.
It evens out the playing field because pre-prepared tooling doesn't help you as much, since the challenges tend to be quite novel. I much prefer those to "write a ROP chain and exploit this service" or "crack this password" (not requiring an inordinate amount of compute, no more than 1hr of CPU time on a contemporary PC, is also a hard level design rule). There's usually one or two more typical infosec ones but they aren't the majority.
One example is a CrackMe challenge that was written in Verilog (implementing a custom CPU to run the actual crackme binary).
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I might still do a monthly challenge or something in the future so people who want to have fun and learn can have fun and learn. That's still okay.
But CTFs as discrete competitions with winners are dead.
A CTF competition is basically gameified homework.
LLMs broke the game. Now all that's left is self study.
@lina thank you for this excellent thread
