91–100 of 345 posts
If you're not already applying static analysis and linters to your codebase (and I know many of you aren't), ask yourself why you would bother to apply an expensive LLM tool? Not to say these things won't catch vulnerabilities static tools cannot, I think they can, it's just we already have the capability to automatically catch a large surface area of common vulns, and have chosen not to, often for expense reasons. I…
Earlier quoted context omitted.
I don't know why you're getting downvoted. This is exactly what was reported by curl's creator under the section "Five findings became one": https://daniel.haxx.se/blog/2026/05/11/mythos-finds-a-curl-v...
And yet [1]: > Not even half-way through this #curl release cycle we are already at 11 confirmed vulnerabilities - and there are three left in the queue to assess and new reports keep arriving at a pace of more than one/day. > 11 CVEs announced in a single release is our record from 2016 after the first-ever security audit (by Cure 53). > This is the most intense period in #curl that I can remember ever been through.…
If you read his own top comment on that LinkedIn post he clarifies:
“The simple reason is: the (AI powered) tools are this good now. And people use these tools against curl source code.They find lots of new problems no one detected before. And none of these new ones used Mythos. Focusing on Mythos is a distraction - there are plenty of good models, and people who can figure out how to get those models and tools to find things.”
You can get a taste of this today yourself with Codex Security. I turned it on just as an experiment and in less than a week it has now become essential to all of us. I was shocked how accurate it is, how many security issues it found in existing code, how it continually finds them as we commit, and how NO ONE is immune from making these mistakes. I'd say it is about 90% accurate for us. Often even the "Low" findings…
How do you avoid this pitfall?
> For example, at one of our Glasswing partner banks, Mythos Preview helped to detect and prevent a fraudulent $1.5 million wire transfer after a threat actor compromised a customer’s email account and made spoof phone calls.
For some reason I am not able to relate to the concreteness of either of these.
First half of the page was occupied with a image, not sure if it was relevant in any ways other than setting up security scare. The size of code base, number of tokens, $ involved seem to be out of scope of the update for some reason. Personally I am getting skeptical about all these optics at this point, just some money printing scheme at high level.
People predict that in 50 years, no human will be driving a car, and people will be shocked that we let humans drive cars manually. Coding may be the same. So many vulnerabilities in code written by very competent programmers. Manually building large, complex systems without major bugs or security vulnerabilities seems to be a nearly impossible challenge.
You can get a taste of this today yourself with Codex Security. I turned it on just as an experiment and in less than a week it has now become essential to all of us. I was shocked how accurate it is, how many security issues it found in existing code, how it continually finds them as we commit, and how NO ONE is immune from making these mistakes. I'd say it is about 90% accurate for us. Often even the "Low" findings…
One issue I've seen with LLM's is adding superfluous code in the name of "safety" and confidently generating a bunch of stuff that was useful in years gone by, but now handled correctly by the standard lib. I'm of the opinion that less is more when it comes to code, and find the trend this is introducing quite frustrating. How do you avoid this pitfall?
The report on findings is very interesting: 1451 acknowledged findings out of 23k candidates(~6%, not high but neither low). But I didn't find the most important information (or maybe I missed it): how much did it cost to find 1451 security bugs?
Claude Mythos Preview will be available to participants at $25/$125 per million input/output tokens
...
Anthropic is committing up to $100M in usage credits for Mythos Preview
How much of this is RL’ing a good coding model on every CVE ever?
There has been a lot of cynicism around mythos, that it's just the usual public models without guardrails, etc. etc. but this: > 1,752 of those high- or critical-rated vulnerabilities have now been carefully assessed by one of six independent security research firms, or in a small number of cases by ourselves. Of these, 90.6% (1,587) have proved to be valid true positives, and 62.4% (1,094) were confirmed as either h…
sigh I remember the GPT-2 days - when it was the first time OpenAI restricted access to the models citing "humanity is not ready for it". The model was good at writing poetry or something.
Since then, I don't remember a single model announcement from OAI/ANT that didn't use similar wording.
The so-called leak of model announcement was marketing, it being dangerous is marketing, the world not being ready for it is marketing. And yes, the ones that were given access to saying "oh wow", believe or not, is also marketing.
It's all marketing. You can get the same results from any of the top-5/10 models that are generally available already.
Mythos is Anthropic's way to sell the new idea, because the previous one has democratized.