How Claude Mythos found a 15-year-old bug in Mozilla Firefox | Brian Grinstead

**Brian Grinstead** (0:00)
Firefox has tens of thousands of source code files and tens of millions of lines of code. It's not possible to say one shot, go find all the potential bugs in this project. It's way too much context for the model.

**Claire Vo** (0:12)
I think people really underappreciate the relentless tedium that an agent will go through.

**Brian Grinstead** (0:17)
Anybody who's done this kind of what I call archaeology, it's really hard to do, and this is something that the coding agents are great at. I asked Claude Code, go figure out semantically when this bug was introduced. I was like watching it do git commands, I didn't even know existed.

**Claire Vo** (0:30)
And the ability to take an agent and give it a very constrained problem in surface area and say exhaust every attempt at this is really powerful. Again, not because human intelligence couldn't identify similar issues, but actually are like cognitive energy declines over time in a way that agents don't.

**Brian Grinstead** (0:51)
Our goal is not to have a bunch of bugs that are hard to find. Our goal is to have zero bugs. And so I think that these tools, as us and other defenders are starting to apply them, actually get us closer to that world.

**Claire Vo** (1:06)
Welcome back to How I AI. I'm Claire Vo, Product Leader and AI Obsessive, here on a mission to help you build better with these new tools. Today, I have Brian Grinstead, Distinguished Engineer at Mozilla Firefox, who's going to take us behind the scenes with their experience with Anthropic's new, but not yet fully released, Model Mythos. And how they solved almost 500 security bugs by rolling their own harness, which isn't as complicated as you think. Let's get to it. This episode is brought to you by Work OS. AI has already changed how we work. Tools are helping teams write better code, analyze customer data, and even handle support tickets automatically. But there's a catch. These tools only work well when they have deep access to company systems. Your co-pilot needs to see your entire code base. Your chat bot needs to search across internal docs. And for enterprise buyers, that raises serious security concerns. That's why these apps face intense IT scrutiny from day one. To pass, they need secure authentication, access controls, audit logs, the whole suite of enterprise features.
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Brian, welcome to How I AI. I'm really excited about this episode because I think you have one of the most impactful stories in AI engineering right now. So tell me first, let's take a step back. For people, what is the scope of the product you're working on and how challenging is what you pulled off?

**Brian Grinstead** (3:03)
Yeah, thanks for having me. So I work on Firefox, which is a production web browser. It's very large and very complex.
We have to render the entire web. The site was built in 2000 or just pushed up yesterday. So there's all sorts of performance, security, and complexity that we deal with. So we recently in the last few months have been using new agentic scanning techniques to find a deluge of security bugs inside of the product, as have many teams as we've started to get better harnesses, models, and techniques for actually getting those bugs fixed.

**Claire Vo** (3:43)
Yeah.
What caused me to reach out and ask you to be on the podcast was this chart. Everybody saw this maybe on X or on the timeline, where the Firefox security bug fixes by month just spiked in multiples in April. And the headline of a lot of posts around this was Mythos, the definitely real model that other people have access to, just not us normies, has unlocked an incredible amount of discovery and fixes on edge case or complex security bugs. But I think the story behind the story is actually a little bit different. Can you tell us about kind of how you got here and how much of this was model and how much of this was work you did internally?

**Brian Grinstead** (4:28)
Yeah, that's right. So I think like a lot of open source projects through 2025, we had been dealing with almost like unwanted AI bug reports. And you know the shape. You've seen it. If you see you get a doc and you can just tell it's from an AI, you know, it looks very nice and professional, but you would get halfway through and the engineer is looking at this and saying that's wrong. Right. And so there is a sort of asymmetric cost on project maintainers to receive a thing. It's cheap to just paste in some C++ code into a chatbot and get back something that's wrong. But there was no way to actually verify whether that was true. And that all changed like I would say in February of 2026 And I think a big part of that story is the harnesses themselves.