> It's a different kind of tool doing a different kind of work, and that makes a clean apples-to-apples comparison to earlier models difficult.
They claim it’s a different kind of tool and then describe using it the same way you’d use any other model. This really felt way worse than the average Cloudflare blog and really just rehashed the Mythos announcement which had already called out the key parts being chaining and crafting examples.
The real question is whether it was Mythos or Opus that wrote this post.
> "Why it matters"
It doesn't, it's a corporate blog, they were rarely written in one-author's voice anyway, but it's interesting to see that even large organisations are outsourcing their blogs to LLMs.
When writing is too heavily LLM-assisted, it does actually cease to be substantive, because it becomes impossible to know which parts of it represent actual claims which the author believes as stated and which are interpolations.
I agree with the complaint, I just disagree with this somehow obviating the need to engage with the underlying substance (where it exists)
And obviously it's a problem that it's so much cheaper to produce writing without underlying substance, but I think when one of the leading Internet security/infrastructure companies is writing about the leading cybersecurity model, it's excessively flippant to say the writing on top is "the real question"
Sentence constructions like this definitely scream AI: "That's a reasonable bias for an exploratory tool. It's a ruinous one for a triage queue..."
I will upgrade the "why it matters" to "and now AI output is part of the training data". A day is coming when the punched-up AI verbiage will be the norm and hard to distinguish unless you're from the previous generation. Sort of in the way that I miss some aspects of Usenet.
That's a scary thought, llm's training on llm output. People trained by default of ubiquity to think and read llm output produce their own llm-esque writing.
Seems stifling. We'll need someway to reward human creativity and out-of-bounds thinking before our greatest corpus of human intellect is a bounded by whenever and whatever was trained on.
Writing and later the printing press have already considerably stifled human expressiveness. Language used to be noch more fragmented and diverse before mass media (or the Bible in every household). In my grandmother’s time you would have difficulty understanding people from three villages down the road.
Human creativity is not only not being rewarded, but people are increasingly talking like consuming too few tokens is something that's actively used against them.
So is it that humans are inherently creative, machines could never do what we do? Or is it that humans will only replicate our training data, and so we have to ensure that machines don't bound our training data? Or are you going meta and gently pointing out the absurdity? (I hope it's this one!)
> The loudest reaction to Mythos Preview from other security leaders has been about speed - scan faster, patch faster, compress the response cycle. More than one team we have spoken with is now operating under a two-hour SLA from CVE release to patch in production [...] If regression testing takes a day, you cannot get to a two-hour SLA without skipping it, and the bugs you ship when you skip regression testing tend to be worse than the bugs you were trying to patch.
Over time, I wonder if these models will be able to generate more secure code by default by doing this kind of exploitability testing before ever merging their code.
Claude Code's harness is remarkable for many use cases, particularly with 1M context sizes. But it's also limited when the scale of code or data to read becomes close to that, or exceeds it. The idea that a cluster of actors can work on a shared, structured set of context snippets, and have guidance around what is relevant to them, is an incredibly useful model outside of cybersecurity as well.
That's great and all but how severe were the most severe vulnerabilities found? I imagine they don't want to talk about it, but that's really the most interesting and important bit.
Most of their new products are AI tools that nobody uses, so I guess they’ll keep posting slop. And recently, they’ve fired so many people that they probably don’t have good writers anymore.
As much as I’d like to share in the skepticism, the very beginning of the article states it very plainly — this is a step function.
Lots of people feel that Mythos is a psyops campaign, but I don’t really understand the skepticism. Most of it seems to stem from the general distrust of things that aren’t publicly available.
A few Anthropic employees have described Mythos as a general purpose model improvement, but that claim has yet to be widely backed up so that’s the only place I’m remaining skeptical.
For the domain of security research, I’m willing to buy the narrative.
In his interview on the Hard Fork podcast, Palo Alto Networks’ CEO described the capability change from Opus to Mythos being more about availability; evidently it runs in a very compute-intensive, always-on mode. Unclear if the base model is significantly different, but Arora ascribed the difference mostly to that change.
I get that you want to address them or whatever before releasing info but I keep seeing these claims with barely any data and I’m like…how do you expect people to not be skeptical?
I mean hell if you’re a security professional you’re literally paid to be skeptical.
It's nice to see them address the instrumentation side of this.
I expressed some concerns along the same lines in the thread about the Mythos evaluation curl did a few days ago, which sounded a lot like the "passing in the repo and telling it go!" type workflow described in this as dramatically less effective.
Disappointed that the post is very slim on details beyond this however. No hard numbers. Not comparatively, not in isolation. Would have arguably been kinda the point.
Interesting for teams looking to implement ai into their deployment process.
I don't think guardrails are useful long term. Assuming we don't see the end of open near-frontier models, it is folly to try to keep models from doing exploit generation. The solution needs to be all software projects writing code under the assumption that hackers will be running LLMs against their code in search of exploits and write secure code accordingly.
I think the curl folks finding it underwhelming is more of a testament to their code being subjected to a lot of tests/attacks/auditing over the past years compared to many other codebases. It's not going to find magically insurmounable exploits on it's own and "pwn teh w0rld".
At the same time, there is so much shitty non-memory safe code out there (C/C++ mainly) or logically weak code (much of it vibe-coded or otherwise by inexperienced devs) that will be easy pickings for anyone pointing Mythos at those codebases/services and eventually lead to chaos since the cost of an customized exploit has gone from days to months of expensive researcher time to some token spending.
Now if they noticed that they could find exploit chains easily in a lot of popular software, some embargo and hardening to give popular OSS packages time to not be exploitable by default does help people (and the NSA that probably has a preview).
The amount of code that is absolute trash in F500 could drown the world.
Static scanners are ok at find a few particular types of issues, and really bad at more abstract issues. Also having rules where you must pass static analysis has to be followed up with actually making sure your code monkeys aren't writing bullshit that confuses the scanner and lets it pass while doing nothing for security (or adding nice logic traps).
Most external security firms looking at code are more useless than a zero with the circle rubbed out. Had a fun example from a while back where the team that wrote the code inserted an intentional security flaw to be sure they were catching anything. Problem is they were giving access to the entire git history so these stood out. The moment they just gave flat code the security teams ability to find flaws disappeared.
LLM models seem to have a pretty good grasp on finding flaws in code like this once you can get the issue to stay in context and execution time. When I hear things like Mythos getting much longer time to work on the problem then at least to me it makes a lot more sense on the number of issues it's picking up.
While it is true that C/C++ are prone to bugs when used by careless programmers, Cloudflare also said:
"We saw consistently more false positives from projects written in memory-unsafe languages."
So while there may be a greater probability to find bugs in C/C++ projects, there is also a greater probability that there will be more work that must be done by humans to verify that real bugs have been found.
There will be no mea culpa from folks insinuating Mythos is a marketing stunt. Nor will there be every time AI capabilities repeatedly blast through the naive expectations.
What does this mean?
> It's a different kind of tool doing a different kind of work, and that makes a clean apples-to-apples comparison to earlier models difficult.
They claim it’s a different kind of tool and then describe using it the same way you’d use any other model. This really felt way worse than the average Cloudflare blog and really just rehashed the Mythos announcement which had already called out the key parts being chaining and crafting examples.
I was expecting some more concrete numbers and surprises. It just seems like a balanced promotion article probably written using LLM itself.
In the last few days I was recommending to read the insights from XBOW [1], it's a competitor but it adds more information to the discussion.
[1] https://xbow.com/blog/mythos-offensive-security-xbow-evaluat...
The real question is whether it was Mythos or Opus that wrote this post.
> "Why it matters"
It doesn't, it's a corporate blog, they were rarely written in one-author's voice anyway, but it's interesting to see that even large organisations are outsourcing their blogs to LLMs.
This looks more like it was edited by AI rather than fully written by it. Or they are using a really good humaniser for the second pass.
It's fascinating seeing people think that if you're snarky enough about something, the substance of that thing actually ceases to be substantive.
It's like staring down the barrel of a gun and taking the time to make quips about the type of paper the gun advertisement was printed on.
When writing is too heavily LLM-assisted, it does actually cease to be substantive, because it becomes impossible to know which parts of it represent actual claims which the author believes as stated and which are interpolations.
All of them represent claims which the author believes as stated, otherwise the author wouldn't put their name on them.
Eh, I still read all of it, but it grates that everything everywhere all the time now is written by one person.
I agree with the complaint, I just disagree with this somehow obviating the need to engage with the underlying substance (where it exists)
And obviously it's a problem that it's so much cheaper to produce writing without underlying substance, but I think when one of the leading Internet security/infrastructure companies is writing about the leading cybersecurity model, it's excessively flippant to say the writing on top is "the real question"
Cloudflare blogs have been excellent for many years, long before transformers arrived.
Sentence constructions like this definitely scream AI: "That's a reasonable bias for an exploratory tool. It's a ruinous one for a triage queue..."
I will upgrade the "why it matters" to "and now AI output is part of the training data". A day is coming when the punched-up AI verbiage will be the norm and hard to distinguish unless you're from the previous generation. Sort of in the way that I miss some aspects of Usenet.
That's a scary thought, llm's training on llm output. People trained by default of ubiquity to think and read llm output produce their own llm-esque writing.
Seems stifling. We'll need someway to reward human creativity and out-of-bounds thinking before our greatest corpus of human intellect is a bounded by whenever and whatever was trained on.
Writing and later the printing press have already considerably stifled human expressiveness. Language used to be noch more fragmented and diverse before mass media (or the Bible in every household). In my grandmother’s time you would have difficulty understanding people from three villages down the road.
Human creativity is not only not being rewarded, but people are increasingly talking like consuming too few tokens is something that's actively used against them.
So is it that humans are inherently creative, machines could never do what we do? Or is it that humans will only replicate our training data, and so we have to ensure that machines don't bound our training data? Or are you going meta and gently pointing out the absurdity? (I hope it's this one!)
I had a dude in a conversation non-ironically use "load-bearing."
I could only follow up with, "that is a genuine insight."
Not a single person visibly flinched in pain.
Should that be surprising? Larger orgs are the ones more naturally associated with mediocrity and are most likely to want to reduce human labor hours.
> The loudest reaction to Mythos Preview from other security leaders has been about speed - scan faster, patch faster, compress the response cycle. More than one team we have spoken with is now operating under a two-hour SLA from CVE release to patch in production [...] If regression testing takes a day, you cannot get to a two-hour SLA without skipping it, and the bugs you ship when you skip regression testing tend to be worse than the bugs you were trying to patch.
Over time, I wonder if these models will be able to generate more secure code by default by doing this kind of exploitability testing before ever merging their code.
Or they don’t, and they* sell access to Mythos and successors through their services company or network of partners and charge a premium.
* they, I mean all foundation models providers, as OpenAI seems to go in the same direction
This is worth a read specifically for this section and the ones following it, re: custom vs. agentic-coding harnesses. https://blog.cloudflare.com/cyber-frontier-models/#why-point...
Claude Code's harness is remarkable for many use cases, particularly with 1M context sizes. But it's also limited when the scale of code or data to read becomes close to that, or exceeds it. The idea that a cluster of actors can work on a shared, structured set of context snippets, and have guidance around what is relevant to them, is an incredibly useful model outside of cybersecurity as well.
That's great and all but how severe were the most severe vulnerabilities found? I imagine they don't want to talk about it, but that's really the most interesting and important bit.
Most of their new products are AI tools that nobody uses, so I guess they’ll keep posting slop. And recently, they’ve fired so many people that they probably don’t have good writers anymore.
As much as I’d like to share in the skepticism, the very beginning of the article states it very plainly — this is a step function.
Lots of people feel that Mythos is a psyops campaign, but I don’t really understand the skepticism. Most of it seems to stem from the general distrust of things that aren’t publicly available.
A few Anthropic employees have described Mythos as a general purpose model improvement, but that claim has yet to be widely backed up so that’s the only place I’m remaining skeptical.
For the domain of security research, I’m willing to buy the narrative.
In his interview on the Hard Fork podcast, Palo Alto Networks’ CEO described the capability change from Opus to Mythos being more about availability; evidently it runs in a very compute-intensive, always-on mode. Unclear if the base model is significantly different, but Arora ascribed the difference mostly to that change.
Claiming something doesn't make it true.
great, but why don't you share real data on how many security vuln it found ? how many were reals, how many weren't ?
Yeah I’m waiting for this as well.
I get that you want to address them or whatever before releasing info but I keep seeing these claims with barely any data and I’m like…how do you expect people to not be skeptical?
I mean hell if you’re a security professional you’re literally paid to be skeptical.
Beside the poorly written post, the vulnerability discovery workflow might actually give good results
It's nice to see them address the instrumentation side of this.
I expressed some concerns along the same lines in the thread about the Mythos evaluation curl did a few days ago, which sounded a lot like the "passing in the repo and telling it go!" type workflow described in this as dramatically less effective.
Disappointed that the post is very slim on details beyond this however. No hard numbers. Not comparatively, not in isolation. Would have arguably been kinda the point.
Interesting for teams looking to implement ai into their deployment process.
I don't think guardrails are useful long term. Assuming we don't see the end of open near-frontier models, it is folly to try to keep models from doing exploit generation. The solution needs to be all software projects writing code under the assumption that hackers will be running LLMs against their code in search of exploits and write secure code accordingly.
I can't wait to be told that Cloudflare is now part of "The Mythos FUD" campaign.
2 things can be true at the same time.
I think the curl folks finding it underwhelming is more of a testament to their code being subjected to a lot of tests/attacks/auditing over the past years compared to many other codebases. It's not going to find magically insurmounable exploits on it's own and "pwn teh w0rld".
At the same time, there is so much shitty non-memory safe code out there (C/C++ mainly) or logically weak code (much of it vibe-coded or otherwise by inexperienced devs) that will be easy pickings for anyone pointing Mythos at those codebases/services and eventually lead to chaos since the cost of an customized exploit has gone from days to months of expensive researcher time to some token spending.
Now if they noticed that they could find exploit chains easily in a lot of popular software, some embargo and hardening to give popular OSS packages time to not be exploitable by default does help people (and the NSA that probably has a preview).
The amount of code that is absolute trash in F500 could drown the world.
Static scanners are ok at find a few particular types of issues, and really bad at more abstract issues. Also having rules where you must pass static analysis has to be followed up with actually making sure your code monkeys aren't writing bullshit that confuses the scanner and lets it pass while doing nothing for security (or adding nice logic traps).
Most external security firms looking at code are more useless than a zero with the circle rubbed out. Had a fun example from a while back where the team that wrote the code inserted an intentional security flaw to be sure they were catching anything. Problem is they were giving access to the entire git history so these stood out. The moment they just gave flat code the security teams ability to find flaws disappeared.
LLM models seem to have a pretty good grasp on finding flaws in code like this once you can get the issue to stay in context and execution time. When I hear things like Mythos getting much longer time to work on the problem then at least to me it makes a lot more sense on the number of issues it's picking up.
While it is true that C/C++ are prone to bugs when used by careless programmers, Cloudflare also said:
"We saw consistently more false positives from projects written in memory-unsafe languages."
So while there may be a greater probability to find bugs in C/C++ projects, there is also a greater probability that there will be more work that must be done by humans to verify that real bugs have been found.
AI boosters are so, so easy to find.
Technically speaking CloudFlare is at its core, a security vulnerability itself. World's largest MITM
There will be no mea culpa from folks insinuating Mythos is a marketing stunt. Nor will there be every time AI capabilities repeatedly blast through the naive expectations.