We have a bounty program. If you can find security bugs in Firefox, please let us pay you for them. You don't need to provide a fix; a testcase that crashes in an interesting way is often enough to qualify.
I never got direct access to Mythos, so all I know is what I've seen from the quality of the bugs being produced. I also haven't been involved at the prompting end.
So the best answer I can give is: I dunno, maybe it's possible to find bugs like this using Opus, but if so, where are they? Did nobody think to try "please find the bug in this code" pre-Mythos? I've done enough auditing with Opus to be convinced that it can be a good assistant to somebody who already knows what they're doing, but in practice the big wave of AI-discovered bugs started with Mythos.
I'm sure lots of people have assumed they could send a publicly available model bug hunting and find things. I have not noticed a huge amount of success. We've had some very nice correctness bugs reported, but skimming through the list of security bugs I've fixed recently, the AI-related ones all seem to be Mythos.
My best guess is that Mythos is just enough better along just enough axes that its hit rate on finding potential bugs and filtering out the real ones from the hallucinations is good enough to matter. Like, there's no obvious qualitative difference between 3.6kg of uranium-232 and 3.8 kg of uranium-232, just a small quantitative increase. But if you form both of them into spheres, only one of them has reached critical mass. Can you do something clever to reach critical mass with 3.6kg of uranium? Maybe! But needing to do something clever is a non-trivial barrier in itself.
Yeah, the test with the patch also adds comments. The human reviewer had extra context available.
I did get Opus to do an audit for similar problems elsewhere, to supplement the investigations that we were already doing by hand. It initially thought it found something, but when asked to produce a testcase, it thought for 20 minutes and admitted defeat. I suspect that the difference between Opus and Mythos is in small edges like this: if Mythos is smart enough to spot why Opus's discovery didn't work a little bit faster, and it can waste less time chasing down red herrings, then it's more likely to find a real bug within the limits of a context window. It's not that Opus completely lacks some capability, it's that it has trouble chaining all the pieces together consistently.
If I had to guess, I'd say that AI is better at finding TOCTOU bugs than fuzzing because it starts by looking at the code and trying to find problems with it, which naturally leads it to experiment with questions like "is there any way to make this assumption false?", whereas fuzzing is more brute force. Fuzzing can explore way more possible states, but AI is better at picking good ones.
In this particular sense, AI tends to find bugs that are closer to what we'd see from a human researcher reading the code. Fuzz bugs are often more "here's a seemingly innocuous sequence of statements that randomly happen to collide three corner cases in an unexpected way".
Outside of SpiderMonkey, my understanding is that many of the best vulnerabilities were in code that is difficult to fuzz effectively for whatever reason.
The same is also true of a good security researcher, and has been for a long time. The question is mostly whether it takes long enough to come up with a testcase that we've managed to ship the fix to all affected releases, and given people some time to update. (And maybe LLMs do change the calculus there! We'll have to wait and see.)
I work on SpiderMonkey, so I mostly looked at the JS bugs. It was a smorgasbord of various things. Broadly speaking I'd say the most impressive bugs were TOCTOU issues, where we checked something and later acted on it, and the testcase found a clever way to invalidate the result of the check in between.
There doesn't have to be a huge qualitative discontinuity between Opus and Mythos. It's just that Mythos has reached a threshold where it's finally smart enough that putting it in a loop and asking it to find bugs is suddenly really effective. Especially at the beginning, Mozilla wasn't doing anything particularly clever with prompts. Mythos is just smart enough that the hit rate on obvious prompts is high enough to matter. (Maybe you can get similar performance out of Opus 4.6 with really smart prompts, but AFAICT nobody had managed it until Mythos.)
In general, I would say that our use of "vulnerability" lines up with what jerrythegerbil calls "potential vulnerability". (In cases with a POC, we would likely use the word "exploit".) Our goal is to keep Firefox secure. Once it's clear that a particular bug might be exploitable, it's usually not worth a lot of engineering effort to investigate further; we just fix it. We spend a little while eyeballing things for the purpose of sorting into sec-high, sec-moderate, etc, and to help triage incoming bugs, but if there's any real question, we assume the worst and move on.
So were all 271 bugs exploitable? Absolutely not. But they were all security bugs according to the normal standards that we've been applying for years.
(Partial exception: there were some bugs that might normally have been opened up, but were kept hidden because Mythos wasn't public information yet. But those bugs would have been marked sec-other, and not included in the count.)
So if you think we're guilty of inflating the number of "real" vulnerabilities found by Mythos, bear in mind that we've also been consistently inflating the baseline. The spike in the Firefox Security Fixes by Month graph is very, very real:
https://hacks.mozilla.org/2026/05/behind-the-scenes-hardenin...
One clear use case for GC in Rust is for implementing other languages (eg writing a JS engine). When people ask why SpiderMonkey hasn't been rewritten in Rust, one of the main technical blockers I generally bring up is that safe, ergonomic, performant GC in Rust still appears to be a major research project. ("It would be a whole lot of work" is another, less technical problem.)
For a variety of reasons I don't think this particular approach is a good fit for a JS engine, but it's still very good to see people chipping away at the design space.
Copy-and-patch is a technique for reducing the amount of effort it takes to write a JIT by leaning on an existing AOT compiler's code generator. Instead of generating machine code yourself, you can get LLVM (or another compiler) to generate a small snippet of code for each operation in your internal IR. Then codegen is simply a matter of copying the precompiled snippet and patching up the references.
The more resources are poured into a JIT, the less it is likely to use copy-and-patch. You get more control/flexibility doing codegen yourself.
Yeah, SM will compile functions with try/catch/finally, but we don't support unwinding directly into optimized code, so the catch block itself will not be optimized.
I don't know how JSC handles it, but in SM `eval` has significant negative effects on surrounding code. (We also decline to optimize functions containing `with` statements, but that's less because it's impossible and more because nobody uses them.)
This isn't about languages; it's about hardware. Should hardware be "higher-level" to support higher level languages? The author says no (and I am inclined to agree with him).
The prose isn't polished enough to be AI. AI generation is unlikely to produce missing spaces like "...which are not readable to humans.SDB uses eBPF ...", or grammatical inaccuracies like "Ensuring Fully Correctness".
As for the data race thing, it seems to me that there's a pretty clear distinction between rbspy's approach (as described in reference 1) and this blog post. rbspy is walking the native stack, which occasionally fails. SDB seems to be looking at Ruby's internals instead, and has some sort of generation-number design to identify cases where there was a data race.
Beyond that, this post just absolutely sounds like what somebody would write if they were trying to describe in prose why they think their multi-threaded code is correct, especially the "Scanning Stacks without the GVL" section.
Taking a quick look at the JSC code, the main difference is that CacheIR is more pervasive and load-bearing. Even monomorphic cases go through CacheIR.
The main justification for CacheIR isn't that it enables us to do optimizations that can't be done in other ways. It's just a convenient unifying framework.
https://www.mozilla.org/en-US/security/client-bug-bounty/