This isn't surprising. What is not mentioned is that Claude Code also found one thousand false positive bugs, which developers spent three months to rule out.
I have so many bugs in the Linux kernel that I can’t
report because I haven’t validated them yet… I’m not going
to send [the Linux kernel maintainers] potential slop,
but this means I now have several hundred crashes that they
haven’t seen because I haven’t had time to check them.
—Nicholas Carlini, speaking at [un]prompted 2026
Not "hidden", but probably more like "no one bothered to look".
declares a 1024-byte owner ID, which is an unusually long but legal value for the owner ID.
When I'm designing protocols or writing code with variable-length elements, "what is the valid range of lengths?" is always at the front of my mind.
it uses a memory buffer that’s only 112 bytes. The denial message includes the owner ID, which can be up to 1024 bytes, bringing the total size of the message to 1056 bytes. The kernel writes 1056 bytes into a 112-byte buffer
This is something a lot of static analysers can easily find. Of course asking an LLM to "inspect all fixed-size buffers" may give you a bunch of hallucinations too, but could be a good starting point for further inspection.
> This is something a lot of static analysers can easily find.
And yet they didn't (either noone ran them, or they didn't find it, or they did find it but it was buried in hundreds of false positives) for 20+ years...
I find it funny that every time someone does something cool with LLMs, there's a bunch of takes like this: it was trivial, it's just not important, my dad could have done that in his sleep.
Remember Heartbleed in OpenSSL? That long predated LLMs, but same story: some bozo forgot how long something should/could be, and no one else bothered to check either.
this is always overlooked. AI stories sound like "with right attitude, you too can win 10M $ in lottery, like this man just did"
Running LLM on 1000 functions produces 10000 reports (these numbers are accurate because I just generated them) — of course only the lottery winners who pulled the actually correct report from the bag will write an article in Evening Post
https://www.youtube.com/watch?v=1sd26pWhfmg is the presentation itself. The prompts are trivial; the bug (and others) looks real and well-explained - I'm still skeptical but this looks a lot more real/useful than anything a year ago even suggested was possible...
they are and they will be, then they won't after the market crashes, the bubble bursts and the companies bankrupts. possibly taking down major portion of the global economy with them.
Tokens are insanely cheap at the moment.
Through OpenRouter a message to Sonnet costs about $0.001 cents or using Devstral 2512 it's about $0.0001.
An extended coding session/feature expansion will cost me about $5 in credits.
Split up your codebase so you don't have to feed all of it into the LLM at once and it's a very reasonable.
It cost me ~$750 to find a tricky privilege escalation bug in a complex codebase where I knew the rough specs but didn't have the exploit. There are certainly still many other bugs like that in the codebase, and it would cost $100k-$1MM to explore the rest of the system that deeply with models at or above the capability of Opus 4.6.
It's definitely possible to do a basic pass for much less (I do this with autopen.dev), but it is still very expensive to exhaustively find the harder vulnerabilities.
That might be a problem for the labs (although I don't think it is) but it's not a problem for end-users. There is enough pressure from top labs competing with each other, and even more pressure from open models that should keep prices at a reasonable price point going further.
In order to justify higher prices the SotA needs to have way higher capabilities than the competition (hence justifying the price) and at the same time the competition needs to be way below a certain threshold. Once that threshold becomes "good enough for task x", the higher price doesn't make sense anymore.
While there is some provider retention today, it will be harder to have once everyone offers kinda sorta the same capabilities. Changing an API provider might even be transparent for most users and they wouldn't care.
If you want to have an idea about token prices today you can check the median for serving open models on openrouter or similar platforms. You'll get a "napkin math" estimate for what it costs to serve a model of a certain size today. As long as models don't go oom higher than today's largest models, API pricing seems in line with a modest profit (so it shouldn't be subsidised, and it should drop with tech progress). Another benefit for open models is that once they're released, that capability remains there. The models can't get "worse".
Not really. I'm fully taking advantage of these low prices while they last. Eventually the AI companies will run start running out of funny money and start charging what the models actually cost to run, then I just switch over to using the self hosted models more often and utilize the online ones for the projects that need the extra resources.
Currently there's no reason for why I shouldn't use Claude Sonnet to write one time bash scripts, once it starts costing me a dollar to do so I'm going to change my behavior.
> Currently there's no reason for why I shouldn't use Claude Sonnet to write one time bash scripts, once it starts costing me a dollar to do so I'm going to change my behavior.
This just isn't going to happen, we have open weights models which we can roughly calculate how much they cost to run that are on the level of Sonnet _right now_. The best open weights models used to be 2 generations behind, then they were 1 generation behind, now they're on par with the mid-tier frontier models. You can choose among many different Kimi K2.5 providers. If you believe that every single one of those is running at 50% subsidies, be my guest.
I also have this feeling. But do you ever doubt it. that when the time comes we will be like the boiled frog? Where its "just so convenient" or that the reality of setting up a local ai is just a worse experience for a large upfront cost?
Inference cost has dropped 300x in 3 years, no reason to think this won't keep happening with improvements on models, agent architecture and hardware.
Also, too many people are fixated with American models when Chinese ones deliver similar quality often at fraction of a cost.
From my tests, "personality" of an LLM, it's tendency to stick to prompts and not derail far outweights the low % digit of delta in benchmark performance.
Not to mention, different LLMs perform better at different tasks, and they are all particularly sensible to prompts and instructions.
Code review is the real deal for these models. This area seems largely underappreciated to me. Especially for things like C++, where static analysis tools have traditionally generated too many false positives to be useful, the LLMs seem especially good. I'm no black hat but have found similarly old bugs at my own place. Even if shit is hallucinated half the time, it still pays off when it finds that really nasty bug.
Instead, people seem to be infatuated with vibe coding technical debt at scale.
>Instead, people seem to be infatuated with vibe coding technical debt at scale.
Don't blame them. That is what AI marketing pushes. And people are sheep to marketing..
I understand why AI companies don't want to promote it. Because they understand that the LCD/Majority of their client base won't see code review as a critical part of their business. If LLMs are marketed as best suited for code review, then they probably cannot justify the investments that they are getting...
> Nicholas has found hundreds more potential bugs in the Linux kernel, but the bottleneck to fixing them is the manual step of humans sorting through all of Claude’s findings
No, the problem is sorting out thousands of false positives from claude code's reports. 5 out of 1000+ reports to be valid is statistically worse than running a fuzzer on the codebase.
> 5 out of 1000+ reports to be valid is statistically worse than running a fuzzer on the codebase.
Carlini said "hundreds" of crashes, not 1000+.
It's not that only 5 were true positives and the rest were false positives. 5 were true positives and Carlini doesn't have bandwidth to review the rest. Presumably he's reviewed more than 5 and some were not worth reporting, but we don't know what that number is. It's almost certainly not hundreds.
Keep in mind that Carlini's not a dedicated security engineer for Linux. He's seeing what's possible with LLMs and his team is simultaneously exploring the Linux kernel, Firefox,[0] GhostScript, OpenSC,[1] and probably lots of others that they can't disclose because they're not yet fixed.
> On the kernel security list we've seen a huge bump of reports. We were between 2 and 3 per week maybe two years ago, then reached probably 10 a week over the last year with the only difference being only AI slop, and now since the beginning of the year we're around 5-10 per day depending on the days (fridays and tuesdays seem the worst). Now most of these reports are correct, to the point that we had to bring in more maintainers to help us. ... Also it's interesting to keep thinking that these bugs are within reach from criminals so they deserve to get fixed.
It was Opus 4.6 (the model). You could discover this with some other coding agent harness.
The other thing that bugs me and frankly I don't have the time to try it out myself, is that they did not compare to see if the same bug would have been found with GPT 5.4 or perhaps even an open source model.
Without that, and for the reasons I posted above, while I am sure this is not the intention, the post reads like an ad for claude code.
I don't understand this critique. Carlini did use Claude Code directly. Claude Code used the Claude Opus 4.6 model, but I don't know why you'd consider it inaccurate to say Claude Code found it.
GPT 5.4 might be capable of finding it as well, but the article never made any claims about whether non-Anthropic models could find it.
If I wrote about achieving 10k QPS with a Go server, is the article misleading unless I enumerate every other technology that could have achieved the same thing?
No the title is correct and you are misreading or didn't read. It was found with Claude code, that's the quote. This isn't a model eval, it's an Anthropic employee talking about Claude code. So comparing to other models isn't a thing to reasonably expect.
This isn't surprising. What is not mentioned is that Claude Code also found one thousand false positive bugs, which developers spent three months to rule out.
> What is not mentioned is that Claude Code also found one thousand false positive bugs, which developers spent three months to rule out.
Source? I haven't seen this anywhere.
In my experience, false positive rate on vulnerabilities with Claude Opus 4.6 is well below 20%.
In TFA:
Those aren't false positives; they're results he hasn't yet inspected.
I wrote a longer reply here: https://news.ycombinator.com/item?id=47638062
In my experience, the issue has been likelihood of exploitation or issue severity. Claude gets it wrong almost all the time.
A threat model matters and some risks are accepted. Good luck convincing an LLM of that fact
Couldn't you just make it write a PoC?
Yes, guy is full of shit
On the other hand, some bugs take three months to find. So this still seems like a win.
You didn't read the article did you?
You didn't read the article did you?
He explicitly talks about not sending the maintainers slop, learn how to read.
Not "hidden", but probably more like "no one bothered to look".
declares a 1024-byte owner ID, which is an unusually long but legal value for the owner ID.
When I'm designing protocols or writing code with variable-length elements, "what is the valid range of lengths?" is always at the front of my mind.
it uses a memory buffer that’s only 112 bytes. The denial message includes the owner ID, which can be up to 1024 bytes, bringing the total size of the message to 1056 bytes. The kernel writes 1056 bytes into a 112-byte buffer
This is something a lot of static analysers can easily find. Of course asking an LLM to "inspect all fixed-size buffers" may give you a bunch of hallucinations too, but could be a good starting point for further inspection.
> This is something a lot of static analysers can easily find.
And yet they didn't (either noone ran them, or they didn't find it, or they did find it but it was buried in hundreds of false positives) for 20+ years...
I find it funny that every time someone does something cool with LLMs, there's a bunch of takes like this: it was trivial, it's just not important, my dad could have done that in his sleep.
Remember Heartbleed in OpenSSL? That long predated LLMs, but same story: some bozo forgot how long something should/could be, and no one else bothered to check either.
Related work from our security lab:
Stream of vulnerabilities discovered using security agents (23 so far this year): https://securitylab.github.com/ai-agents/
Taskflow harness to run (on your own terms): https://github.blog/security/how-to-scan-for-vulnerabilities...
"Guy working at company making product, says that the newer version of the product is better"
Huh, who would've expected this.
> "given enough eyeballs, all bugs are shallow"
Time to update that:
"given 1 million tokens context window, all bugs are shallow"
Already happend: https://arxiv.org/abs/2407.08708
more like some bugs are shallow and others are pieced together false-positives from an automated tool reliable in its unreliability.
..and three months to review the false positives
this is always overlooked. AI stories sound like "with right attitude, you too can win 10M $ in lottery, like this man just did"
Running LLM on 1000 functions produces 10000 reports (these numbers are accurate because I just generated them) — of course only the lottery winners who pulled the actually correct report from the bag will write an article in Evening Post
An explanation of the Claude Opus 4.6 linux kernel security findings as presented by Nicholas Carlini at unpromptedcon.
https://www.youtube.com/watch?v=1sd26pWhfmg is the presentation itself. The prompts are trivial; the bug (and others) looks real and well-explained - I'm still skeptical but this looks a lot more real/useful than anything a year ago even suggested was possible...
This does sound great, but the cost of tokens will prevent most companies from using agents to secure their code.
Tokens aren't more expensive than highly trained meatbags today. There's no way they'll be more expensive "tomorrow"...
they are and they will be, then they won't after the market crashes, the bubble bursts and the companies bankrupts. possibly taking down major portion of the global economy with them.
Tokens are insanely cheap at the moment. Through OpenRouter a message to Sonnet costs about $0.001 cents or using Devstral 2512 it's about $0.0001. An extended coding session/feature expansion will cost me about $5 in credits. Split up your codebase so you don't have to feed all of it into the LLM at once and it's a very reasonable.
It cost me ~$750 to find a tricky privilege escalation bug in a complex codebase where I knew the rough specs but didn't have the exploit. There are certainly still many other bugs like that in the codebase, and it would cost $100k-$1MM to explore the rest of the system that deeply with models at or above the capability of Opus 4.6.
It's definitely possible to do a basic pass for much less (I do this with autopen.dev), but it is still very expensive to exhaustively find the harder vulnerabilities.
You’d have to ignore the massive investor ROI expectations or somehow have no capability to look past “at the moment”.
That might be a problem for the labs (although I don't think it is) but it's not a problem for end-users. There is enough pressure from top labs competing with each other, and even more pressure from open models that should keep prices at a reasonable price point going further.
In order to justify higher prices the SotA needs to have way higher capabilities than the competition (hence justifying the price) and at the same time the competition needs to be way below a certain threshold. Once that threshold becomes "good enough for task x", the higher price doesn't make sense anymore.
While there is some provider retention today, it will be harder to have once everyone offers kinda sorta the same capabilities. Changing an API provider might even be transparent for most users and they wouldn't care.
If you want to have an idea about token prices today you can check the median for serving open models on openrouter or similar platforms. You'll get a "napkin math" estimate for what it costs to serve a model of a certain size today. As long as models don't go oom higher than today's largest models, API pricing seems in line with a modest profit (so it shouldn't be subsidised, and it should drop with tech progress). Another benefit for open models is that once they're released, that capability remains there. The models can't get "worse".
Not really. I'm fully taking advantage of these low prices while they last. Eventually the AI companies will run start running out of funny money and start charging what the models actually cost to run, then I just switch over to using the self hosted models more often and utilize the online ones for the projects that need the extra resources. Currently there's no reason for why I shouldn't use Claude Sonnet to write one time bash scripts, once it starts costing me a dollar to do so I'm going to change my behavior.
> Currently there's no reason for why I shouldn't use Claude Sonnet to write one time bash scripts, once it starts costing me a dollar to do so I'm going to change my behavior.
This just isn't going to happen, we have open weights models which we can roughly calculate how much they cost to run that are on the level of Sonnet _right now_. The best open weights models used to be 2 generations behind, then they were 1 generation behind, now they're on par with the mid-tier frontier models. You can choose among many different Kimi K2.5 providers. If you believe that every single one of those is running at 50% subsidies, be my guest.
I also have this feeling. But do you ever doubt it. that when the time comes we will be like the boiled frog? Where its "just so convenient" or that the reality of setting up a local ai is just a worse experience for a large upfront cost?
worse. he's already boiled. probably paying way more than that one dollar per bash script with all the subscriptions he already has.
Yeah, the $20 I paid to OpenRouter about 4 months ago really cost me an arm and a leg, not sure where I'll get my next meal if I'm to be honest.
>$0.001 cents
$0.001 (1/10 of a cent) or 0.001 cents (1/1000 of a cent, or $0.00001)?
I don't buy it.
Inference cost has dropped 300x in 3 years, no reason to think this won't keep happening with improvements on models, agent architecture and hardware.
Also, too many people are fixated with American models when Chinese ones deliver similar quality often at fraction of a cost.
From my tests, "personality" of an LLM, it's tendency to stick to prompts and not derail far outweights the low % digit of delta in benchmark performance.
Not to mention, different LLMs perform better at different tasks, and they are all particularly sensible to prompts and instructions.
But on the other hand, Claude might introduce more vulnerability than it discovered.
Code review is the real deal for these models. This area seems largely underappreciated to me. Especially for things like C++, where static analysis tools have traditionally generated too many false positives to be useful, the LLMs seem especially good. I'm no black hat but have found similarly old bugs at my own place. Even if shit is hallucinated half the time, it still pays off when it finds that really nasty bug.
Instead, people seem to be infatuated with vibe coding technical debt at scale.
> Code review is the real deal for these models.
Yea, that is what I have been saying as well...
>Instead, people seem to be infatuated with vibe coding technical debt at scale.
Don't blame them. That is what AI marketing pushes. And people are sheep to marketing..
I understand why AI companies don't want to promote it. Because they understand that the LCD/Majority of their client base won't see code review as a critical part of their business. If LLMs are marketed as best suited for code review, then they probably cannot justify the investments that they are getting...
Guys please read the article before commenting...
> Nicholas has found hundreds more potential bugs in the Linux kernel, but the bottleneck to fixing them is the manual step of humans sorting through all of Claude’s findings
No, the problem is sorting out thousands of false positives from claude code's reports. 5 out of 1000+ reports to be valid is statistically worse than running a fuzzer on the codebase.
Just sayin'
> 5 out of 1000+ reports to be valid is statistically worse than running a fuzzer on the codebase.
Carlini said "hundreds" of crashes, not 1000+.
It's not that only 5 were true positives and the rest were false positives. 5 were true positives and Carlini doesn't have bandwidth to review the rest. Presumably he's reviewed more than 5 and some were not worth reporting, but we don't know what that number is. It's almost certainly not hundreds.
Keep in mind that Carlini's not a dedicated security engineer for Linux. He's seeing what's possible with LLMs and his team is simultaneously exploring the Linux kernel, Firefox,[0] GhostScript, OpenSC,[1] and probably lots of others that they can't disclose because they're not yet fixed.
[0] https://www.anthropic.com/news/mozilla-firefox-security
[1] https://red.anthropic.com/2026/zero-days/
> On the kernel security list we've seen a huge bump of reports. We were between 2 and 3 per week maybe two years ago, then reached probably 10 a week over the last year with the only difference being only AI slop, and now since the beginning of the year we're around 5-10 per day depending on the days (fridays and tuesdays seem the worst). Now most of these reports are correct, to the point that we had to bring in more maintainers to help us. ... Also it's interesting to keep thinking that these bugs are within reach from criminals so they deserve to get fixed.
https://lwn.net/Articles/1065620/
The title is a little misleading.
It was Opus 4.6 (the model). You could discover this with some other coding agent harness.
The other thing that bugs me and frankly I don't have the time to try it out myself, is that they did not compare to see if the same bug would have been found with GPT 5.4 or perhaps even an open source model.
Without that, and for the reasons I posted above, while I am sure this is not the intention, the post reads like an ad for claude code.
OP here.
I don't understand this critique. Carlini did use Claude Code directly. Claude Code used the Claude Opus 4.6 model, but I don't know why you'd consider it inaccurate to say Claude Code found it.
GPT 5.4 might be capable of finding it as well, but the article never made any claims about whether non-Anthropic models could find it.
If I wrote about achieving 10k QPS with a Go server, is the article misleading unless I enumerate every other technology that could have achieved the same thing?
No the title is correct and you are misreading or didn't read. It was found with Claude code, that's the quote. This isn't a model eval, it's an Anthropic employee talking about Claude code. So comparing to other models isn't a thing to reasonably expect.