Greatly appreciated the candor. I've included a few slides into text that i thought were eye-opening to me:
From his Kernel Recipes 2026 slide on Mythos
```
Mythos's 79 vulnerabilities:
24 - no detail at all "something crashed"
14 - not a bug at all
3 - totally made up data
15 - already fixed in latest release
- 11 by others
- 4 by anthropic
20 - fixes were needed
- 7 "assume a malicious filesystem image"
- 2 "assume you can inject a malicious network packet into the middle of the stack"
- 2 "NOMMU"
- 6 sctp networking issues for untrusted devices
- 2 ipv6 minor network issues
- 1 gpu driver for local malicious user
```
GHK called this "10 'real' bugfixes", which to me sounds like there's a wild hype machine around these companies and uncritical parroting of every press release they make that falls apart when you engage the affected real experts.
We’ve seen this in a few open source repos we voluntarily manage security on. They’re not massive repos, but big enough they get attention from security researchers.
Most of the real low hanging fruit was picked up by humans years ago. When doing automated scanning, the majority of stuff is overly-verbose nonsense which takes hours of expert human labour to understand, test, and discard.
Reading through a Claude generated false positive is absolutely excruciating, because it is absolutely determined that what it’s found is justified. Often you’ll receive very long accompanying “proof of concept” code which demonstrates absolutely wild scenarios. It’s especially frustrating when you’re volunteering your time for a project, and a well-meaning contributor submits the report without the technical nous to understand why you’re rejecting it.
It's great to hear about $topic from someone no-nonsense and in-the-know like Greg KH. You can verify all of this too - since, well Linux kernel. (As opposed to what Microsoft or Apple claims to fix as far as LLM finds.)
Mythos may not be great today but it is not far fetched to imagine bug discovery, analysis and fixes can be made much quicker, accurate and even newly possible with specialized models trained on say Linux kernel specifics - with codemap/coding standards/threat models, good and bad coding patterns, tools to validate etc. an LLM can be much more relentless than humans and if it has the help to be accurate it will be worth the electricity burned. Oh and another model trained on triage data to validate the first one's findings would be good.
(I think Microsoft is doing this internally - different models trained internally alongside Mythos - there was some talk about it on the tubes, don't recall where exactly.)
From his Kernel Recipes 2026 slide on Mythos
```
```GHK called this "10 'real' bugfixes", which to me sounds like there's a wild hype machine around these companies and uncritical parroting of every press release they make that falls apart when you engage the affected real experts.
Most of the real low hanging fruit was picked up by humans years ago. When doing automated scanning, the majority of stuff is overly-verbose nonsense which takes hours of expert human labour to understand, test, and discard.
Reading through a Claude generated false positive is absolutely excruciating, because it is absolutely determined that what it’s found is justified. Often you’ll receive very long accompanying “proof of concept” code which demonstrates absolutely wild scenarios. It’s especially frustrating when you’re volunteering your time for a project, and a well-meaning contributor submits the report without the technical nous to understand why you’re rejecting it.
Mythos may not be great today but it is not far fetched to imagine bug discovery, analysis and fixes can be made much quicker, accurate and even newly possible with specialized models trained on say Linux kernel specifics - with codemap/coding standards/threat models, good and bad coding patterns, tools to validate etc. an LLM can be much more relentless than humans and if it has the help to be accurate it will be worth the electricity burned. Oh and another model trained on triage data to validate the first one's findings would be good.
(I think Microsoft is doing this internally - different models trained internally alongside Mythos - there was some talk about it on the tubes, don't recall where exactly.)