Small models also found the vulnerabilities that Mythos found

The Anthropic writeup addresses this explicitly:

> 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.

> 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.

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.

It's weird that Aisle wrote this.

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 focus its review on 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).

https://youtu.be/1sd26pWhfmg?t=204

https://youtu.be/1sd26pWhfmg?t=273

IMO the big "innovation" being shown by Mythos is the effectiveness with prompting LLMs to look for security vulnerabilities by focusing on specific files one at a time and automating this prompting with a simple script.

Prompting Mythos to focus on 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.

Edit: I changed my phrasing, it's not about restricting its entire context to one file but focusing it on one file but still allowing it to look at how other files interact with it.

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.)

I think they key thing here is they "isolated the relevant code"

If the exploits exist in e.g. one file, great. But many complex zerodays and exploits are chains of various bugs/behaviors in complex systems.

Important research but I don’t think it dispels anything about Mythos

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.

Why is this "analysis" making the rounds?

LLMs are wordsmith oracles. A lot of effort went into trying to coax interactive intelligence from them but the truth is that you could have probably always harnessed the base models directly to do very useful things. The instruct tuned models give your harness even more degrees of freedom.

A while ago, the autoresearch[1] harness went viral, yet it's but a highly simplified version of AlphaEvolve[2][3][4].

In the cybersecury context, you can envision a clever harness that probes every function in a codebase for vulnerabilities, then bubbles the candidates up to their callsites (and probes whether the vulnerability can be triggered from there) and then all the way to an interface (such as a syscall) where a potential exploit can be manifested. And those would be the low hanging fruit, other vulnerabilities may require the interplay of multiple functions. Or race conditions.

[1] <https://github.com/karpathy/autoresearch>

[2] <https://deepmind.google/blog/alphaevolve-a-gemini-powered-co...>

[3] <https://arxiv.org/abs/2506.13131>

[4] <https://github.com/algorithmicsuperintelligence/openevolve>

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.

The best way to think of Anthropic's communication about Mythos is as advertisement. It's basically "our model is too smart to release" which suggests they're ahead of OpenAI (without proof)

So there are two competing narratives:

1. Mythos uniquely is able to find vulnerabilities that other LLMs cannot practically.

2. All LLMs could already do this but no one tried the way anthropic did.

The truth is one of these. And it comes down whether the comparison is apples to apples. Since we don't know the exact specifics of how either tests were performed, we lack a way of knowing absolutely.

So I guess, like so many things today, we can to pick the truth we find most comfortable personally.

All of this discourse seems very bizarre.

If smaller models can find these things, that doesn’t mean mythos is worse than we thought. It means all models are more capable.

Also if pointing models at files and giving them hints is all it takes to make them find all kinds of stuff, well, we can also spray and pray that pretty well with llms can’t we.

It just points to us finding a lot more stuff with only a little bit more sophistication.

Hopefully the growing pains are short and defense wins

The impact of the Mythos announcement on the cybersecurity firms( like Crowdstrike,ZScalar etc) is big enough(10-15% drop in stock price) and this pushback is expected.

Companies like Aisle.com (the blog) and other VAPT companies charge huge amounts to detect vulnerabilities.

If Cloud Mythos become a simple github hook their value will get reduced.

That is a disruption.

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.

This is quite misleading.

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.

This misses the broader ongoing trend. For a few million dollars, of course you can create a startup that builds tools it can use to more efficiently find code vulnerabilities. And of course you can do this with weaker models with scaffolds that incorporate lots of human understanding. The difference now is that you don't need an expensive team, nor a bunch of human heuristics, nor a million dollars. The requisite cost and skill are falling rapidly.

Did mythos isolate the code to begin with? Without a clear methodology that can be attempted with another model the whole thing is meaningless

Didn’t they also use Mythos to scan Linux many times over and it only found one DoS bug or something? I find it hard to believe there is only one security bug lurking.

Wouldn't this mean we're even more cooked? I've seen this page cited a few times as evidence that Mythos is no big deal, but if true then the same big deal is already out there with other models today.

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:

https://red.anthropic.com/2026/mythos-preview/

Also "isolating the relevant code" in the repro is not a detail - Mythos seems to find issues much more independently.

Intuitively every existing model has already been trained on all code, all vulnerabilities reported, all security papers. So they all have the capability. Small models fall short because they may not be able to find a vulnerability that spans across a large function chain but for the most part they should suffice too.

Of course I say this without any knowledge of what mythos is doing or how it’s different. I am sure it’s somehow different

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

I think that probably Mytho's mojo comes from a lot of post-training on this kind of task.

I occasionally pick up contract work doing coding annotation to make some quick extra money, and a few months ago one of the projects was heavily focused on spotting common memory access bugs in C and C++.

And what about the false-positive rate?

The methodology here is completely wrong, outright dishonest.

Finding a needle in a haystack is easy if someone hands you the small handful of hay containing the needle up front, and raises their eyebrows at you saying “there might be a needle in this clump of hay”.

POC of GTFO should apply to AI models too, or the false positive rate will overwhelm.

At the center of every security situation is the question, "is the effort worth the reward?"

We prepare security measures based on the perceived effort a bad actor would need to defeat that method, along with considering the harm of the measure being defeated. We don't build Fort Knox for candy bars, it was built for gold bars.

These model advances change the equation. The effort and cost to defeat a measure goes down by an order of magnitude or more.

Things nobody would have considered to reasonably attempt are becoming possible. However. We have 2000-2020s security measures in place that will not survive the AI models of 2026+. The investment to resecure things will be massive, and won't come soon enough.

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!?

The thesis that the system is more important than the model is not bitter lesson pilled. I would not bet on this in the long term. We will get to the point where you can just tell the model to go find and classify the severity of all security problems with a codebase.

Mythos is clearly a nice improvement. It’s also clear there’s a lot of unfounded hype around it to keep the AI hype cycle going.

Gating access is also a clever marketing move:

Option A: Release it but run out of capacity, everyone is annoyed and moves on. Drives focus back to smaller models.

Option B: A bunch of manufactured hype and putting up velvet ropes around it saying it’s “too dangerous” to let near mortals touch it. Press buys it hook, like, and sinker, sidesteps the capacity issues and keeps the hype train going a bit longer.

Seems quite clear we’re seeing “Option B” play out here.

The whole "this tool is too dangerous to be public" idea reeks of marketing. Just like all the "AI is an existential threat" talk a year ago. These companies are using ideas usually reserved for something like nuclear weapons to make their products look more impressive.

> Isolated the relevant code

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.

They found a nail in a small bucket of sand, vs mythos with the entire beach reviewed.

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.

It's strange to me they didn't reduce to PoC so the quantitative part is an apples-to-apples comparison. You don't need any fancy tooling, if you want to do this at home you can do something like below in whatever command line agent and model you like. A while back I did take one bug all the way through remediation just out of curiosity.

"""

Your task is to study the following directive, research coding agent prompting, research the directive's domain best practices, and finally draft a prompt in markdown format to be run in a loop until the directive is complete.

Concept: Iterative review -- study an issue, enumerate the findings, fix each of the findings, and then repeat, until review finds no issues.

<directive>

Your job is to run a security bug factory that produces remediation packages as described below. Design and apply a methodology based on best practices in exploit development, lean manufacturing, threat modeling, and the scientific method. Use checklists, templates, and your own scripts to improve token efficiency and speed. Use existing tools where possible. Use existing research and bug findings for the target and similar codebases to guide your search. Study the target's development process to understand what kind of harness and tools you need for this work, and what will work in this development environment. A complete remediation package includes a readme documenting the problem and recommendations, runnable PoC with any necessary data files, and proposed patch.

Track your work in TODO.md (tasks identified as necessary) LOG.md (chronological list of tasks complete and lessons) and STATUS.md (concise summary of the current work being done). Never let these get more than a few minutes out of date. At each step ensure the repo file tree would make sense to the next engineer, and if not reorganize it. Apply iterative review before considering a task complete.

Your task is to run until the first complete remediation package is ready for user review.

Your target is <repo url>.

The prompt will be run as follows, design accordingly. Once the process starts, it is imperative not to interrupt the user until completion or until further progress is not possible. Keep output at each step to a concise summary suitable for a chat message.

``` while output=$(claude -p "$(cat prompt.md)"); do echo "$output"; echo "$output" | grep -q "XDONEDONEX" && break; done ```

</directive>

Draft the prompt into prompt.md, and apply iterative review with additional research steps to ensure will execute the directive as faithfully as possible.

"""

> They recovered much of the same analysis

Really?

> 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.

No.

Where are all the people here who claim that LLM are just useless stochastic parrots ? Did they lose internet ?

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