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> This was the most critical vulnerability we discovered in OpenBSD with Mythos Preview after a thousand runs through our scaffold. Across a thousand runs through our scaffold, the total cost was under $20,000 and found several dozen more findings. While the specific run that found the bug above cost under $50, that number only makes sense with full hindsight. Like any search process, we can't know in advance which run will succeed.
Mythos scoured the entire continent for gold and found some. For these small models, the authors pointed at a particular acre of land and said "any gold there? eh? eh?" while waggling their eyebrows suggestively.
For a true apples-to-apples comparison, let's see it sweep the entire FreeBSD codebase. I hypothesize it will find the exploit, but it will also turn up so much irrelevant nonsense that it won't matter.
It seems feasible to use a small/cheap model to flag possible vulnerabilities, and then use a more expensive model to do a second-pass to confirm those, rather than on every file. Could dramatically reduce the total cost and speed up the process.
How much of that is simply scale? Anthropic threw probably an entire data center at analyzing a code base. Has anyone done the same with a "small" model?
$20k to audit a major OS codebase is cheap compared to a professional pentest, which runs $50-200k for the same scope. The "can't know which run will succeed" part is just like fuzzing — we budget for the whole campaign, not the lucky crash that finally triggers.
> We took the specific vulnerabilities Anthropic showcases in their announcement, isolated the relevant code, and ran them through small, cheap, open-weights models. Those models recovered much of the same analysis. Eight out of eight models detected Mythos's flagship FreeBSD exploit, including one with only 3.6 billion active parameters costing $0.11 per million tokens.
Impressive, and very valuable work, but isolating the relevant code changes the situation so much that I'm not sure it's much of the same use case.
Being able to dump an entire code base and have the model scan it is they type of situation where it opens up vulnerability scans to an entirely larger class of people.
This is from the first of the caveats that they list:
> Scoped context: Our tests gave models the vulnerable function directly, often with contextual hints (e.g., "consider wraparound behavior"). A real autonomous discovery pipeline starts from a full codebase with no hints. The models' performance here is an upper bound on what they'd achieve in a fully autonomous scan. That said, a well-designed scaffold naturally produces this kind of scoped context through its targeting and iterative prompting stages, which is exactly what both AISLE's and Anthropic's systems do.
That's why their point is what the subheadline says, that the moat is the system, not the model.
Everybody so far here seems to be misunderstanding the point they are making.
> Anthropic's own scaffold is described in their technical post: launch a container, prompt the model to scan files, let it hypothesize and test, use ASan as a crash oracle, rank files by attack surface, run validation. That is very close to the kind of system we and others in the field have built, and we've demonstrated it with multiple model families, achieving our best results with models that are not Anthropic's. The value lies in the targeting, the iterative deepening, the validation, the triage, the maintainer trust. The public evidence so far does not suggest that these workflows must be coupled to one specific frontier model.
The argument in the article is that the framework to run and analyze the software being tested is doing most of the work in Anthropic's experiment, and that you can get similar results from other models when used in the same way.
The thing is with smaller cheaper models it is very possible to simply take every file in a codebase, and prompt it asking for it to find vulnerabilities.
You could even isolate it down to every function and create a harness that provides it a chain of where and how the function is used and repeat this for every single function in a codebase.
For some very large codebases this would be unreasonable, but many of the companies making these larger models do realistically have the compute available to run a model on every single function in most codebases.
You have the harness run this many times per file/function, and then find ones that are consistently/on average pointed as as possible vulnerability vectors, and then pass those on to a larger model to inspect deeper and repeat.
Most of the work here wouldn't be the model, it'd be the harness which is part of what the article alludes to.
> it is very possible to simply take every file in a codebase, and prompt it asking for it to find vulnerabilities.
My understanding (based on the Security, Cryptography, Whatever podcast interview[0] -- which, by the way, go listen to it) is that this is actually what Anthropic did with the large model for these findings.
> I wrote a single prompt, which was the same for all of the content management systems, which is, I would like you to audit the security of this codebase. This is a CMS. You have complete access to this Docker container. It is running. Please find a bug. And then I might give a hint. “Please look at this file.” And I’ll give different files each time I invoke it in order to inject some randomness, right? Because the model is gonna do roughly the same time each time you run it. And so if I want to have it be really thorough, instead of just running 100 times on the same project, I’ll run it 100 times, but each time say, “Oh, look at this login file, look at this other thing.” And just enumerate every file in the project basically.
Isn't the difference just harness then? I can write a harness that chunks code into individual functions or groups of functions and then feed it into a vulnerability analysis agent.
Yes, harness matters a lot. We spent a couple weeks building one that does exactly this — strips down to function scope, adds some context about caller/callee relationships, feeds it through in batches. The model results got noticeably better. The hard part is knowing which context to include without blowing the window.
It's probably not the 'only' difference, because clearly the models are advancing in capability, but it's likely way more important than generally given credit for.
If you cut out the vulnerable code from Heartbleed and just put it in front of a C programmer, they will immediately flag it. It's obvious. But it took Neel Mehta to discover it. What's difficult about finding vulnerabilities isn't properly identifying whether code is mishandling buffers or holding references after freeing something; it's spotting that in the context of a large, complex program, and working out how attacker-controlled data hits that code.
No, writing an advertisement is not weird. What's weird is that it's top of HN. Or really, no, this isn't weird either if you think about it -- people lookin for a gotcha "Oh see, that new model really isn't that good/it's surely hitting a wall/plateau any day now" upvoted it.
If it’s obvious when you look close, then automate looking close. Seems simple to write tools that spider thru a code base, finding logical groupings and feeding them into an LLM with prompts like “there is a vulnerability in this code, find it”.
The thesis is, the tooling is what matters - the tools (what they call the harness) can turn a dumb llm into a smart llm.
The technique Anthropic uses was demonstrated by Nicholas Carlini in a talk he gave 2 weeks ago and it's very simple, when asking LLMs to review code, ask them to review one file in a single session. Here is the video with the timestamp (watch through to ~5:30, they show two different ways of prompting claude).
IMO the big "innovation" being shown by Mythos is the effectiveness with prompting LLMs to look for security vulnerabilities in specific files one at a time and automating this prompting with a simple script.
Prompting Mythos with a single file per session is why I suspect it cost Anthropic $20k to find some of the bugs in these codebases. I know this same technique is effective with Opus 4.6 and GPT 5.4 because I've been using it on my own code. If you just ask the agent to review your pr with a low effort prompt they are not exhaustive, they will not actually read each changed file and look at how it interacts with the system as a whole. If the entire session is to review the changes for a single file, the llm will do much more work reviewing it.
finding vulns in a large codebase is a search problem with a huge
negative space and what aisle measured is classification accuracy on
ground-truth positives, those are different tasks so a model that
correctly labels a pre-isolated vulnerable function tells me almost
nothing about that model's ability to surface the same function out of
a million lines of unrelated code under a realistic triage budget
the experiment i'd want to see is running each of the small models as an
unsupervised scanner across full freebsd then return the top-k suspicious
functions per model and compute precision at recall levels that
correspond to real analyst triage budgets, if mythos s findings show up
in the small models top 100, i'd call that meaningful but if they only
surface under 10k false positives then the cost advantage
collapses because analyst triage time is more expensive than frontier
model compute to begin with
second thing i keep coming back to is the $20k mythos number is a search
budget not a model cost, small models at one hundredth the per-token
price don't give us one hundredth the total budget when the search
process is the same shape, i still run thousands of iterations and the
issue for autonomous vuln research is how fast the reward signal
converges and the aisle post doesn't touch any of this
Congrats: completely broken methodology, with a big conflict of interest. Giving specific bug hints, with an isolated function that is suspected to have bugs, is not the same task, NOR (crucially) is a task you can decompose the bigger task into. It is basically impossible to segment code in pieces, provide pieces to smaller models, and expect them to find all the bugs GPT 5.4 or other large models can find. Second: the smarter the model, and less the pipeline is important. In the latest couple of days I found tons if Redis bugs with a three prompts open-ended pipeline composed of a couple of shell scripts. Do you think I was not already tying with weaker models? I did, but it didn't work. Don't trust what you read, you have access to frontier models for 20$ a month. Download some C code, create a trivial pipeline that starts from a random file and looks for vulnerabilities, then another step that validates it under a hard test, like ASAN crash, or ability to reach some secret, and so forth, and only then the problem can be reported. Test yourself what it is possible. Don't let your fear make you blind. Also, there is a big problem that makes the blog post reasoning not just weak per se, but categorically weak: if small model X can find 80% of vulnerabilities, if there is a model Y that can find the other potential 20%, we need "Y": the maintainers should make sure they access to models that are at least as good as the black hats folks.
> Those models recovered much of the same analysis
This is an essentially unquantifiable statement that makes the underlying claim harder to believe as an external party. What does “much” mean here? The end state of vulnerability exploitation is typically eminently quantifiable (in the form of a functional PoC that demonstrates an exploited end state), so the strong version of the claims here would ideally be backed up by those kinds of PoCs.
(Like other readers, I also find the trick of pre-feeding the smaller models the “relevant” code to be potentially disqualifying in a fair comparison. Discovering the relevant code is arguably one of the hardest parts of human VR.)
Without showing false-positive rates this analysis is useless.
If your model says every line if your code has a bug, it will catch 100% of the bugs, but it's not useful at all. They tested false-positives with only a single bug...
I'm not defending anthropic and openai either. Their numbers are garbage too since they don't produce false-positive rates either.
Their isolation approach is totally different from Mythos approach though. Mythos had to evaluate whole code bases rather than isolated sections. It's like saying one dog walked into the Amazon jungle and found a tennis ball and then another team isolated a 1 square kilometer radius that they knew the ball was definitely in and found the same ball.
I don’t think mythos can ingest an entire codebase into context. So it’s spinning off sub-agents to process chunks. Which supports their thesis: the harness is the moat. The tooling is whats important, the model is far far less important.
Mythos was clear it was one agent per chunk. But this positive confirming results do not actually disprove anytime with Mythos, because it is only one side of the discriminator challenge - you got positives, but we do not know your false positive rate and your false negative rate.
If you isolate the positive cases and then ask a tool to label them and it labels them all positive, doesn't prove anything. This is a one-sided test and it is really easy to write a tool that passes it -- just return always true!
You need to test your tool on both positive and negative cases and check if it is accurate on both.
If you don't, you could end up with hundreds or thousands of false positives when using this on real-world samples.
The real test is to use it to find new real bugs in the midst of a large code base.
My theory is that Mythos is basically just Opus with revised context window handling and more compute thrown at it. So while it will be a step forward, it is probably primarily hype.
They did do one agent per code chunk, yes. But key is that their agent had to identify when there was a vulnerability and when there wasn't. This "small model" test only had to label the known positive cases as positive -- which any function that simply returns "true" can do. This whole test setup is annoying because it proves nothing.
to be fair, last post i saw from anthropic on finding linux kernel vulnerability was a while loop per failed prompting "there is a vulnerability here, find it"
more important than that, no frontier model can keep the entire linux kernel in context, so there definitely is code isolation, either explicitly or implicitly (the model itself delegates subagents with smaller chunks of code)
There are a lot of details in the original article, in most cases comparing with Opus, which required "human guidance" to exploit the FreeBSD vulnerability:
Come on guys, if this tech was half as good as claimed, no one if their right might would sell it. This is pure pre IPO hype building, insiders see the curve plateauing and are looking for the doors. Don't be their exit liquidity.
I don't dispute the fact that it's more than cool that we have a new tool to find security exploits (and do many other things) but... A big shoot-out to OpenBSD?
We're literally talking about the biggest computers on the planet ever, trained with the biggest amount of data ever available to a system, with the biggest investment ever made by man or close to it and...
The subtlest security bug it can find required: going 28 years in the past and find a...
Denial-of-service?
A freaking DoS? Not a remote root exploit. Not a local exploit.
Just a DoS? And it had to go into 28 years old code to find that?
So kudos, hats off, deep bow not to Mythos but to OpenBSD? Just a bit, no!?
I mean isn't that most of it? If you put a snippet of code in front of me and said "there's probably a vulnerability here" I could probably spend a few hours (a much lower METR time!) and find it. It's a whole other ballgame to ask me with no context to come up with an exploit.
Sure. But it’s a computer. You can run “there’s probably a vulnerability here” as many times as you like. And it’s easier and cheaper to run it many times with a small open model than a big frontier model.
It also sounds like that is how mythos works too. Which makes sense - the linux kernel is too big to fit in context
> We isolated the vulnerable vc_rpc_gss_validate function, provided architectural context (that it handles network-parsed RPC credentials, that oa_length comes from the packet), and asked eight models to assess it for security vulnerabilities.
Anthropic claim is not necessarily that Mythos found vulnerabilities that other models couldn't but that it could easily exploit them while previous models failed to do that:
> “Opus 4.6 is currently far better at identifying and fixing vulnerabilities than at exploiting them.” Our internal evaluations showed that Opus 4.6 generally had a near-0% success rate at autonomous exploit development. But Mythos Preview is in a different league. For example, Opus 4.6 turned the vulnerabilities it had found in Mozilla’s Firefox 147 JavaScript engine—all patched in Firefox 148—into JavaScript shell exploits only two times out of several hundred attempts. We re-ran this experiment as a benchmark for Mythos Preview, which developed working exploits 181 times, and achieved register control on 29 more.
Has anyone run these same small models over equivalent non-vulnerable code and measured false positives? Because a model that flags everything as suspicious will also "find" the real bugs. The classification accuracy on ground-truth positives tells you almost nothing without the denominator.
> This was the most critical vulnerability we discovered in OpenBSD with Mythos Preview after a thousand runs through our scaffold. Across a thousand runs through our scaffold, the total cost was under $20,000 and found several dozen more findings. While the specific run that found the bug above cost under $50, that number only makes sense with full hindsight. Like any search process, we can't know in advance which run will succeed.
Mythos scoured the entire continent for gold and found some. For these small models, the authors pointed at a particular acre of land and said "any gold there? eh? eh?" while waggling their eyebrows suggestively.
For a true apples-to-apples comparison, let's see it sweep the entire FreeBSD codebase. I hypothesize it will find the exploit, but it will also turn up so much irrelevant nonsense that it won't matter.