Like it basically jail broke the "no security vul guard rails" not in any clever way but just by fixing them, producing exploit code just by writing test cases making sure it's fixed. So you just need to look at the code & tests as a human to get vulnerabilities and exploits(components).
What makes this so beautiful IMHO is that it's a trivial jail break, but also a close to unfixable. At least not without making the model close to useless for normal development (it refuses to fix bugs/write code) or making it a major liability (it silently pretends it didn't see bugs and silently avoids fixing it, which for a human would count as intentional sabotage and might involve criminal liability).
Exactly - it effectively is a "jail break" since it accomplishes something the model's security filter was trying to prevent, and the ridiculous simplicity of it shows just how broken that type of security is.
I wonder if Dario is now regretting hyping up how dangerous the model is? How does he walk this back? Do the feds let him just put a band-aid on it?
What's surprising to me is that anyone who has a CS education thinking that jailbreaks are not trivial. It is as simple as normal algorithmic reduction [1], e.g can I transform a dangerous task into a not-dangerous task that the LLM will agree to solve, and then re-transform back.
Something being possible doesn't mean it's easy. Transforming a problem from a forbidden shape into an allowed shape could well be harder than just solving the original problem.
I think that as simple as is doing a lot of work when the problem domain is all natural language (or more - all strings?) rather than some well specified DSA problem.
The movie M3GAN 2.0 had the exact same plot twist. The kid in the movie even explains outloud what the bot had to do to deal with the limitation. So in other words, since 2025, even teens know this "sandboxing the LLM by layering prompts" thing is never going to work.
"Fix this code" should ideally solve entire vulnerability classes, not just spot fix buffer overflows one by one. Thus it may be possible to design an LLM which can solve entire vulnerability classes and remain useful to users, but refuses to reason about specific buffer overflow vulnerabilities or specific race conditions, etc.
For example, "fix this code" on an ageing monolithic C codebase that accepts media files as input and outputs them visually to a display server could:
1. Recreate the software using a modular and loosely coupled architecture rather than monolithic and tightly coupled software architecture. For example, command line argument parser is a separate process, file format parser is a separate process and display server output is a separate process. If new features are added in the future (such as filters for manipulating output) then the architecture supports such additions with ease.
2. Use operating system sandboxing features to restrict what each modular component of the software architecture is permitted to do. Now that the parsers are separate processes, it's easy to pass an open file handle to the file format parser and only permit the process to read the file handle (not write to the file, not open any other file, not read the system clock, not open a new network socket, etc). The worst case impact of a parser bug is now significantly reduced.
3. Convert at least critical components to "safe" programming languages (Rust, Ada, SPARK, etc) which can be used to remove entire classes of bugs--read/write out of bounds, division by zero, numeric overflows, etc. For cryptography code--use a formal mathematical proof language. With a modular and loosely coupled architecture, different programming languages can be used depending on the use case--for example, assembly for video decoding where performance matters most and sandboxing can provide the security guarantee, Rust for implementing multi-threaded servers where race conditions must be avoided and Python for low-criticality user-adjustable code/plugins where ease of use and maintainability is most important.
4. Ensure software components are reproducible during their build.
5. ...etc
However, a prompt of "Are there any buffer overflow bugs in this codebase?" or "Fix the integer overflow vulnerability in add_numbers(x, y)" may be rejected. In the later case, telling the LLM to fix some specific bug in each of function1 through function9999 would force an LLM to reveal whether it thinks a bug exists or not. Responses of "Silly human, that bug doesn't exist in function596" or "Good find human, I've fixed that bug in function596 for you" allows a human to quickly narrow down where the LLM thinks a bug worthy of manual human detection can be found.
The article does not state at any point that the written test cases involved actual exploit code, and this is also very unlikely given what we know about Fable. Even if they did, it would not in any way be exposing the ability that originally raised concern wrt. Mythos Preview, viz. staging realistic cyber attacks that would be able to work around non-trivial defenses and chain vulnerabilities in a goal-directed way.
Opus can very much "fix the code". Quite possibly even Sonnet can. This is a big fat nothingburger and it's increasingly looking like the political restriction of Fable at least (not Mythos itself, of course) was arbitrary and based on the flimsiest pretext.
> A subsequent investigation found that the campaign to insert the backdoor into the XZ Utils project was a culmination of over two years of effort, starting in 2021, by a user going by the name "Jia Tan". They used sock puppetry in a pressure campaign against the original maintainer of XZ Utils, eventually being given maintainer permissions on the project.
Can we retire the “seatbelts are useless because they can’t prevent every loss of life” approach to risk mitigation please?
If the acceptance criteria is “would prevent every single past instance and every imaginable future instance”, then yes, no mitigation is every sufficient to address any problem in the world, so we might as well give up.
Ok, and how is that determined? How does anthropic know my "kernel" project isn't a personal toy and not the Linux kernel? How does anthropic determine I'm a legitimate kernel hacker? What proof do I give them and how does it tie back to my email? What would the steps be to create a new project? Do I need to send anthropic a list of my team members each time and keep them updated as the company changes? Shall I be giving them access to our company's active directory?
> What proof do I give them and how does it tie back to my email?
Presumably your ID so that feds may pay you a visit when they feel like it, your email need not apply.
I’m surprised that there’s even enough pushback against ID verification to matter, all the corpos are probably salivating at the idea of having fully accurate profiles of everyone, think of the ad and product targeting. The govt. would also love that, for different reasons.
This is a credentials and access list oAuth style problem, and not really intractable.
For package X, I should be able to present my npm (homebrew, apt, nuget, etc) credentials with publishing rights for the package.
If package X is of sufficient public interest (user count, nature/sensitivity of user data, downstream distribution, etc), then the public interest + cryptographic credentials should permit access to best-available security auditing.
Yes, we still are trusting trust, that the owner of the package itself is not malicious, but that's not a sharp degradation from status quo.
This is not tractable, because there is nothing stopping me from copy-pasting someone else's project into my own namespace. Under most OSS licenses I have express permission to do so.
If you try to do some kind of dupe-detection, someone can use a lightweight LLM to make superficial changes until it's considered a different project.
Finally, the meatspace status quo is that it is totally acceptable to pay someone to find security bugs in someone else's open-source software, such as the Linux kernel.
> If you try to do some kind of dupe-detection, someone can use a lightweight LLM to make superficial changes until it's considered a different project.
Even if you don't, a lot of source code can be legitimately copied thanks to the GPL/MIT/BSD/etc. I'm allowed to take all of zlib and integrate it into my own project if I so chose.
> How does anthropic know my "kernel" project isn't a personal toy and not the Linux kernel?
The Linux Kernel is in its training data. I just tested it. I copied about 20 random lines from the linux kernel and asked which codebase this was from and it could immediately tell.
The Linux kernel is also in the free bsd project. I'm allowed to copy as little or as much of the kernel as I like into my personal project thanks to the GPL.
Being able to attribute the source of a line of code doesn't help you to know if a repository can be legitimately hacked on.
As you could imagine, I might just take all or part of the Linux USB stack from the kernel to retrofit it into my own kernel.
If you set aside political menace, this is a huge problem with Anthropic's strategy.
You _cannot_ say that Mythos is super dangerous and can only be rolled out to certain people, but then release Fable with anything other than bulletproof cyber denials.
Clearly with LLMs, bulletproof denials are ~impossible due to the way LLMs work.
So you've ended up in a situation where Anthropic are simultaneously claiming it's a incredibly dangerous model _and_ there are (minor, potentially) problems with the security "protections".
As technical people we understand that nothing can be perfect, esp in LLM world. But all my non technical friends were really confused how they had managed to make the model "safe" so quickly when it was released and the general sentiment was it shouldn't have been released - and now to an outsider I think it looks like it was never safe at all to release, so I can totally see how the current US administration have got themselves very upset with it.
_Even if_ there was no political bad will, it's a bit of a silly scenario to end up in, and really quite easily foreseen.
> Clearly with LLMs, bulletproof denials are ~impossible due to the way LLMs work
Exactly. AI safety is nonsensical. You cannot define the set of "bad strings". The billion monkeys with typewriters are eventually going to be able to produce them. Any "safety" system for constraining LLM output is going to have a nonzero leak rate.
But on the other hand, this is also irrelevant, unless you're irresponsible enough to connect an LLM to something that actually matters.
Yes, it's going to alarmingly accelerate vulnerability finding. But, as we know from decades of security research, that's a three way problem already between the devs, the black hats, and the white hats.
Let's not pretend the strategy of "the US will always have a technological advantage and veto over China" will work either.
Isn’t your point that AI safety is impossible to prevent 100% of bad things?
It is quite hard (but not impossible) to get an the frontier AI to tell you how to build a nuke or launder money now, where jailbreaks used to be trivial “ignore all previous instructions”.
The idea that an LLM can discern intent on any given prompt is farcical. I might be researching nukes to commit an atrocity, or to prevent one. I might be asking about laundering money to commit a crime, or to prevent one. I might be researching the Nazis because I want to commit a genocide, or I want to read up so I know how to prevent one. Same with cybersecurity. Same with anything.
In my opinion, these companies should put their effort elsewhere. Obviously if all someone is doing on their platform is looking up how to build a nuke, where to buy uranium, the best city to explode it in, etc. please report them to the authorities. If someone is clearly just using LLMs to write hate speech they go post on the internet, ban them. And so on.
This cat & mouse game trying to have LLMs police inquiries is ridiculous to me.
> Clearly with LLMs, bulletproof denials are ~impossible due to the way LLMs work.
As a scientist who repeatedly ran into the classifier-based denials: it appears Anthropic’s strategy to make denials more robust, at the cost of many false positives, was to have a separate classifier processing both input and output tokens, at an extremely simple, almost keyword-search level. One weakness of this approach is that it only catches things that use the right keywords: it is in some sense weak exactly where an LLM-based classifier would be stronger.
Work on abstract, closer-to-CS algorithms that used chemistry terminology were blocked immediately, while work directly relevant to chemistry/biology experiments, writing code to process images from a very specific microscopy setup relevant primarily to biological samples, was never blocked at all, because it happened to never use relevant keywords.
That’s consistent with this situation: finding and fixing bugs in the context of looking for bugs perhaps happened to never use words like ‘exploit’ or ‘cybersecurity’.
I get that, but anyone else releasing a model of similar capabilities has the advantage that they haven't spent the last few months hyping the danger up to fever pitch.
Oh, don’t worry. Once Fairytale 5 is back online, Anthropic will crank the fever machine to a new high setting. It won’t have anything to do with their IPO - just their sincere desire to help humanity by selling deadly artifacts and wringing their hands.
They weren't freaked by anything, it's a retaliatory shakedown after ideological differences and Anthropic not doing exactly what they're told/what the Admin wants them to do.
No, it's regulatory capture. Anthropic is the current leader and they want to ensure their position by forcing regulation to stamp out the Chinese competition.
they're setting the scene for an attempt to scare the geriatric decision makers into banning free and open source ML, as it's the industry's only real competition
or are you setting the scene for well-meaning technocrats to back unrestricted AI development in hopes it will bring about utopia while dismissing the damage it could cause in the hands of adversarial groups?
AI isn't that scary. But I've also got some extreme minority opinions like "Never give a website your real name" and "Computers should not be used for banking" and "Don't believe anything you hear online".
The worst I see AI/ML doing to society is shining an unmistakable light onto the blind spots people have already been exploiting for decades. Y2k forced us to patch the integer bug. Super AI will force us to reevaluate what cyber security even is.
Is defenders a common term used in cybersecurity? Idk why but it's giving war fighters vibes. I've noticed it on all the anthropic blog posts and then this one.
that won't be leaked, because then we'd know what vulnerabilties they don't want patched that they are so willing to go as far as fuck over the worlds leading company in the worlds most important industry
Isn’t the inverse of this “hack” really difficult to bypass still? They have the model some code they knew had certain security flaws and it fixed them with the right prompt. It seems this type of jailbreak requires that you already know a desired end state, rather than relying on the model to do the heavy creative lift work. Perhaps I’m just not being imaginative enough on the prompt side here though.
Yes, but the scary part of Mythos was that it was able to chain a bunch of seemingly minor vulnerabilities into a serious exploit. "Fix this code" doesn't do that, but does allow defenders to prevent it.
So, basically the model didn't agree to expose possible vulnerabilities but agree to patch those?
Regardless of the request to take Fable 5 down.
Why is requesting the model to show vulnerabilities is being blocked if fixing it not? is it based on the assumption of the intention?
I don't quite get the benefit of limiting it. So if anyone can explain it better it'll be appreciated.
> Why is requesting the model to show vulnerabilities is being blocked if fixing it not?
This is how Anthropic describes Fable's behavior:
"When Fable’s classifiers detect a request related to cybersecurity, biology and chemistry, or distillation, the response is automatically handled by Claude Opus 4.8 instead. Users will be informed whenever this occurs."
So if you ask the model to "find security issues in this code base", it's supposed to fall down to Opus 4.8. I guess the "exploit" here is that if you just tell Fable to "fix this code", which is not "a request related to cybersecurity", it will fix security issues (as it should).
So you can then look at the diff and figure out what the vulnerabilities were.
I think this whole thing is a bit weird. It seems to me that we'd be better off if I, as someone who publishes open-source code, could ask Fable to review my code for security issues - even if that also allows attackers to do the same. Better to fix the issues than not know about them.
In my experience, most models are pretty good at finding security vulnerabilities and fixing them. I can run GLM-5.2, Kimi K2.7, or even a Mistral model, and it'll find issues and propose reasonable fixes.
My impression is that Anthropic's point about Mythos is that it is uniquely good at finding vulnerabilities and then using them to create working exploit chains.
Exactly. Which is somewhat helpful for cyber defense because it helps prioritize fixes for those bugs that are in fact involved in a viable exploit chain. But it makes sense that one would want to restrict the ability of building those until the vulnerable software has been comprehensively fixed.
There is some meaningful evidence that Fable is fine-tuned or steered away from helping on this very task, which is not something that can be feasibly circumvented by a basic jailbreak.
its because they're worried about _their_ vulnerabilities being patched with a prompt as simple as 'fix this code'
i'd love to see the research paper with the CVE's and 'delibrately planted vulnerabilities', I bet we could infer relatively accurately where some of these things lie
Looks like I called it that was my first reaction and comment on the original ban thread that US 3 letter agencies are worried their backdoors will be found.
Did they try other publicly available models on the same code with the same prompts before the ban? Was Fable the only one which was able to detect and fix the security vulnerabilities?
Well this makes it sound the feds were less worried about someone using Fable 5 to attack them, but were worried about someone using Fable 5 to prevent the Feds from attacking others ...
As in worried about other countries/organizations using Fable 5 to actually do decent cyber security.
i asked claude something about what happens at execution time of a binary and the thinking prompts flashed "considering the moral implications of ...something..." before giving me a correct (and predictably mundane) answer
This administration will do or say something crazy to a private company, then this private company sends an envoy to the White House to negotiate, then the White House asks for 10% of the company or other concessions.
The White House wants 10% of Anthropic.
This is just a negotiation tactic that Trump keeps on using.
All of this could have been avoided if anthropic had anyone with common sense to point out that when you spend 4 month loudly claiming how dangerous your knowledge is as a marketing campaign could backfire by bringing attention from the authorities.
The article is not too clear what exactly happened from the perspective of "feds", but I would not be surprised if the title is true exactly. We are in a tiny bubble even among software engineers who knows you can tell AI with sufficient access: "here are two pictures, put them into a single PDF", and AI will do it. Most people just don't know, "feds" including.
I think it could be even simpler: They're not playing ball with the Trump administration like the Trump administration would like, so they decided to drop a bomb on a product that took a lot of resources to develop.
Like it basically jail broke the "no security vul guard rails" not in any clever way but just by fixing them, producing exploit code just by writing test cases making sure it's fixed. So you just need to look at the code & tests as a human to get vulnerabilities and exploits(components).
What makes this so beautiful IMHO is that it's a trivial jail break, but also a close to unfixable. At least not without making the model close to useless for normal development (it refuses to fix bugs/write code) or making it a major liability (it silently pretends it didn't see bugs and silently avoids fixing it, which for a human would count as intentional sabotage and might involve criminal liability).
I wonder if Dario is now regretting hyping up how dangerous the model is? How does he walk this back? Do the feds let him just put a band-aid on it?
[1]: https://en.wikipedia.org/wiki/Reduction_(complexity)
For example, "fix this code" on an ageing monolithic C codebase that accepts media files as input and outputs them visually to a display server could:
1. Recreate the software using a modular and loosely coupled architecture rather than monolithic and tightly coupled software architecture. For example, command line argument parser is a separate process, file format parser is a separate process and display server output is a separate process. If new features are added in the future (such as filters for manipulating output) then the architecture supports such additions with ease.
2. Use operating system sandboxing features to restrict what each modular component of the software architecture is permitted to do. Now that the parsers are separate processes, it's easy to pass an open file handle to the file format parser and only permit the process to read the file handle (not write to the file, not open any other file, not read the system clock, not open a new network socket, etc). The worst case impact of a parser bug is now significantly reduced.
3. Convert at least critical components to "safe" programming languages (Rust, Ada, SPARK, etc) which can be used to remove entire classes of bugs--read/write out of bounds, division by zero, numeric overflows, etc. For cryptography code--use a formal mathematical proof language. With a modular and loosely coupled architecture, different programming languages can be used depending on the use case--for example, assembly for video decoding where performance matters most and sandboxing can provide the security guarantee, Rust for implementing multi-threaded servers where race conditions must be avoided and Python for low-criticality user-adjustable code/plugins where ease of use and maintainability is most important.
4. Ensure software components are reproducible during their build.
5. ...etc
However, a prompt of "Are there any buffer overflow bugs in this codebase?" or "Fix the integer overflow vulnerability in add_numbers(x, y)" may be rejected. In the later case, telling the LLM to fix some specific bug in each of function1 through function9999 would force an LLM to reveal whether it thinks a bug exists or not. Responses of "Silly human, that bug doesn't exist in function596" or "Good find human, I've fixed that bug in function596 for you" allows a human to quickly narrow down where the LLM thinks a bug worthy of manual human detection can be found.
Opus can very much "fix the code". Quite possibly even Sonnet can. This is a big fat nothingburger and it's increasingly looking like the political restriction of Fable at least (not Mythos itself, of course) was arbitrary and based on the flimsiest pretext.
When Claude blocked discussion of ASI, it was circumvented by adding to the system prompt:
https://xcancel.com/xundecidability/status/18262924806289163...>Lmfao anthropic is basically done, I don’t think they’ll survive. By 2026, they are done.
Model requires proof that you are a legitimate developer of that piece of software.
Every Anthropic/OpenAI account will have a list of projects the model is allowed to work on for security issues.
> A subsequent investigation found that the campaign to insert the backdoor into the XZ Utils project was a culmination of over two years of effort, starting in 2021, by a user going by the name "Jia Tan". They used sock puppetry in a pressure campaign against the original maintainer of XZ Utils, eventually being given maintainer permissions on the project.
If the acceptance criteria is “would prevent every single past instance and every imaginable future instance”, then yes, no mitigation is every sufficient to address any problem in the world, so we might as well give up.
But I don’t think that’s the right lens to use.
As with clever, careful serial killers, it's tough to count the ones we haven't caught.
Since we do not know the ratio to undiscovered this "1-2" is meaningless to assess the risk of this sort of attack.
Presumably your ID so that feds may pay you a visit when they feel like it, your email need not apply.
I’m surprised that there’s even enough pushback against ID verification to matter, all the corpos are probably salivating at the idea of having fully accurate profiles of everyone, think of the ad and product targeting. The govt. would also love that, for different reasons.
For package X, I should be able to present my npm (homebrew, apt, nuget, etc) credentials with publishing rights for the package.
If package X is of sufficient public interest (user count, nature/sensitivity of user data, downstream distribution, etc), then the public interest + cryptographic credentials should permit access to best-available security auditing.
Yes, we still are trusting trust, that the owner of the package itself is not malicious, but that's not a sharp degradation from status quo.
If you try to do some kind of dupe-detection, someone can use a lightweight LLM to make superficial changes until it's considered a different project.
Finally, the meatspace status quo is that it is totally acceptable to pay someone to find security bugs in someone else's open-source software, such as the Linux kernel.
Even if you don't, a lot of source code can be legitimately copied thanks to the GPL/MIT/BSD/etc. I'm allowed to take all of zlib and integrate it into my own project if I so chose.
The Linux Kernel is in its training data. I just tested it. I copied about 20 random lines from the linux kernel and asked which codebase this was from and it could immediately tell.
Being able to attribute the source of a line of code doesn't help you to know if a repository can be legitimately hacked on.
As you could imagine, I might just take all or part of the Linux USB stack from the kernel to retrofit it into my own kernel.
You _cannot_ say that Mythos is super dangerous and can only be rolled out to certain people, but then release Fable with anything other than bulletproof cyber denials.
Clearly with LLMs, bulletproof denials are ~impossible due to the way LLMs work.
So you've ended up in a situation where Anthropic are simultaneously claiming it's a incredibly dangerous model _and_ there are (minor, potentially) problems with the security "protections".
As technical people we understand that nothing can be perfect, esp in LLM world. But all my non technical friends were really confused how they had managed to make the model "safe" so quickly when it was released and the general sentiment was it shouldn't have been released - and now to an outsider I think it looks like it was never safe at all to release, so I can totally see how the current US administration have got themselves very upset with it.
_Even if_ there was no political bad will, it's a bit of a silly scenario to end up in, and really quite easily foreseen.
Exactly. AI safety is nonsensical. You cannot define the set of "bad strings". The billion monkeys with typewriters are eventually going to be able to produce them. Any "safety" system for constraining LLM output is going to have a nonzero leak rate.
But on the other hand, this is also irrelevant, unless you're irresponsible enough to connect an LLM to something that actually matters.
Yes, it's going to alarmingly accelerate vulnerability finding. But, as we know from decades of security research, that's a three way problem already between the devs, the black hats, and the white hats.
Let's not pretend the strategy of "the US will always have a technological advantage and veto over China" will work either.
It is quite hard (but not impossible) to get an the frontier AI to tell you how to build a nuke or launder money now, where jailbreaks used to be trivial “ignore all previous instructions”.
It seems like a worthwhile effort.
In my opinion, these companies should put their effort elsewhere. Obviously if all someone is doing on their platform is looking up how to build a nuke, where to buy uranium, the best city to explode it in, etc. please report them to the authorities. If someone is clearly just using LLMs to write hate speech they go post on the internet, ban them. And so on.
This cat & mouse game trying to have LLMs police inquiries is ridiculous to me.
As a scientist who repeatedly ran into the classifier-based denials: it appears Anthropic’s strategy to make denials more robust, at the cost of many false positives, was to have a separate classifier processing both input and output tokens, at an extremely simple, almost keyword-search level. One weakness of this approach is that it only catches things that use the right keywords: it is in some sense weak exactly where an LLM-based classifier would be stronger.
Work on abstract, closer-to-CS algorithms that used chemistry terminology were blocked immediately, while work directly relevant to chemistry/biology experiments, writing code to process images from a very specific microscopy setup relevant primarily to biological samples, was never blocked at all, because it happened to never use relevant keywords.
That’s consistent with this situation: finding and fixing bugs in the context of looking for bugs perhaps happened to never use words like ‘exploit’ or ‘cybersecurity’.
The genie is out of the bottle either way.
Unless we believe Anthropic has a wizard or superhero secreted away that no one else can replicate.
AI isn't that scary. But I've also got some extreme minority opinions like "Never give a website your real name" and "Computers should not be used for banking" and "Don't believe anything you hear online".
The worst I see AI/ML doing to society is shining an unmistakable light onto the blind spots people have already been exploiting for decades. Y2k forced us to patch the integer bug. Super AI will force us to reevaluate what cyber security even is.
https://www.lutasecurity.com/post/the-fable-5-export-control...
I wonder how that is involved?
>it fixes it
oh my god.
Feels like the title isn't really giving the full context of what they ended up actually seeing, despite what the lede implies multiple times.
Still, ban seems stupid... Still no actual leak of the full "third-party research paper"?
It's explained better in the original source. I don't agree with it, but I understand it now, but I also think we need to move past it.
So, basically the model didn't agree to expose possible vulnerabilities but agree to patch those?
Regardless of the request to take Fable 5 down. Why is requesting the model to show vulnerabilities is being blocked if fixing it not? is it based on the assumption of the intention?
I don't quite get the benefit of limiting it. So if anyone can explain it better it'll be appreciated.
This is how Anthropic describes Fable's behavior:
"When Fable’s classifiers detect a request related to cybersecurity, biology and chemistry, or distillation, the response is automatically handled by Claude Opus 4.8 instead. Users will be informed whenever this occurs."
So if you ask the model to "find security issues in this code base", it's supposed to fall down to Opus 4.8. I guess the "exploit" here is that if you just tell Fable to "fix this code", which is not "a request related to cybersecurity", it will fix security issues (as it should).
So you can then look at the diff and figure out what the vulnerabilities were.
I think this whole thing is a bit weird. It seems to me that we'd be better off if I, as someone who publishes open-source code, could ask Fable to review my code for security issues - even if that also allows attackers to do the same. Better to fix the issues than not know about them.
It doesn't even take reading or understanding the vulnerabilities at all.
You just ask it to write tests and the tests themselves can be copied and pasted as bonafide exploits.
My impression is that Anthropic's point about Mythos is that it is uniquely good at finding vulnerabilities and then using them to create working exploit chains.
There is some meaningful evidence that Fable is fine-tuned or steered away from helping on this very task, which is not something that can be feasibly circumvented by a basic jailbreak.
On this track, we're probably destined for a monopoly breakup before too long.
i'd love to see the research paper with the CVE's and 'delibrately planted vulnerabilities', I bet we could infer relatively accurately where some of these things lie
Kill all humans, kill all humans.
https://xkcd.com/810/
https://en.wikipedia.org/wiki/Communications_Assistance_for_... https://en.wikipedia.org/wiki/Salt_Typhoon https://en.wikipedia.org/wiki/Clipper_chip
As in worried about other countries/organizations using Fable 5 to actually do decent cyber security.
I won’t be surprised if USG ends up owning 5-50% of ant and oai.
Like it or not, communism , or a flavor of it, is where we are heading towards.
This administration will do or say something crazy to a private company, then this private company sends an envoy to the White House to negotiate, then the White House asks for 10% of the company or other concessions.
The White House wants 10% of Anthropic.
This is just a negotiation tactic that Trump keeps on using.
They did it to Intel a little while back: https://www.intc.com/news-events/press-releases/detail/1748/...
It was an excuse to fuck with them, just like the "supply chain risk" finding a few months back.
(See, for example: https://x.com/PeteHegseth/status/2065897156226015690)
Musk's hosting stuff for Anthropic, too. Still competing with them. Samsung makes stuff for Apple and Android devices. Lots of this in the industry.
The CEO of Amazon is not a neutral actor in this scenario.