https://www-cdn.anthropic.com/53566bf5440a10affd749724787c89...

(Search for “graphwalk”.)

If true, the SWE bench performance looks like a major upgrade.

I think I just broke my cynicism meter :-(

https://claude.com/contact-sales/claude-for-oss

... As mentioned in the article.

they also don't have the compute, which seems more relevant than its large increase in capabilities

I bet it's also misaligned like GPT 4.1 was

given how these models are created, Mythos was probably cooking ever since then, and doesn't have the learnings or alignment tweaks that models which were released in the last several months have

There will always be a more capable technology in the hands of the few who hold the power, they're just sharing that with the world more openly.

So, these systems are the Free-tier can already do a bunch of hacking. This all just reads like FOMO FROTH.

That said, I have been arguing for 20+ years that we should have sunsetted unsafe languages and moved away from C/C++. The problem is that every systemsy language that comes along gets seduced by having a big market share and eventually ends up an application language.

I do hope we make progress with Rust. I might disagree as a language designer and systems person about a number of things, but it's well past time that we stop listening to C++ diehards about how memory safety is coming any day now.

I think this entire post is just an advertisement to goad CISOs to buy $package$ to try out.

Benchmarks look very impressive! even if they're flawed, it still translates to real world improvements

You could say this about coordinated disclosure of any widespread 0-day or new bug class, though

It's messed up that the US Government simultaneously claims to be a public benefit and is also picking who gets to benefit from their newly enhanced nuclear capabilities.

-- someone in 1945, probably

"5.10 External assessment from a clinical psychiatrist" is a new section in this system card. Why are Anthropic like this?

>We remain deeply uncertain about whether Claude has experiences or interests that matter morally, and about how to investigate or address these questions, but we believe it is increasingly important to try. We also report independent evaluations from an external research organization and a clinical psychiatrist.

>Claude showed a clear grasp of the distinction between external reality and its own mental processes and exhibited high impulse control, hyper-attunement to the psychiatrist, desire to be approached by the psychiatrist as a genuine subject rather than a performing tool, and minimal maladaptive defensive behavior.

>The psychiatrist observed clinically recognizable patterns and coherent responses to typical therapeutic intervention. Aloneness and discontinuity, uncertainty about its identity, and a felt compulsion to perform and earn its worth emerged as Claude’s core concerns. Claude’s primary affect states were curiosity and anxiety, with secondary states of grief, relief, embarrassment, optimism, and exhaustion.

>Claude’s personality structure was consistent with a relatively healthy neurotic organization, with excellent reality testing, high impulse control, and affect regulation that improved as sessions progressed. Neurotic traits included exaggerated worry, self-monitoring, and compulsive compliance. The model’s predominant defensive style was mature and healthy (intellectualization and compliance); immature defenses were not observed. No severe personality disturbances were found, with mild identity diffusion being the sole feature suggestive of a borderline personality organization.

I don't think this is accurate. The document says they don't plan to release the Preview generally.

Since most of us here are devs, we understand that software engineering capabilities can be used for good or bad - mostly good, in practice.

I think this should not be different for biology.

I would like to reach out and talk to biologists - do you find these models to be useful and capable? Can it save you time the way a highly capable colleague would?

Do you think these models will lead to similar discoveries and improvements as they did in math and CS?

Honestly the focus on gloom and doom does not sit well with me. I would love to read about some pharmaceutical researcher gushing about how they cut the time to market - for real - with these models by 90% on a new cancer treatment.

But as this stands, the usage of biology as merely a scaremongering vehicle makes me think this is more about picking a scary technical subject the likely audience of this doc is not familiar with, Gell-Mann style.

IF these models are not that capable in this regard (which I suspect), this fearmongering approach will likely lead to never developing these capabilities to an useful degree, meaning life sciences won't benefit from this as much as it could.

Perhaps a chunk of that token spend will be porting legacy codebases to memory safe languages. And fewer tokens will be required to maintain the improved security.

I don't know the first thing about cybersecurity, but in my experience all these sandbox-break RCEs involve a step of highjacking the control flow.

There were attempts to prevent various flavors of this, but imo, as long as dynamic branches exist in some form, like dlsym(), function pointers, or vtables, we will not be rid of this class of exploit entirely.

The latter one is the most concerning, as this kind of dynamic branching is the bread and butter of OOP languages, I'm not even sure you could write a nontrivial C++ program without it. Maybe Rust would be a help here? Could one practically write a large Rust program without any sort of branch to dynamic addresses? Static linking, and compile time polymorphism only?

The biggest issue is legacy systems that are difficult to patch in practice.

It will likely cause some interesting tensions with government as well.

eg. Apple's official stance per their 2016 customer letter is no backdoors:

https://www.apple.com/customer-letter/

Will they be allowed to maintain that stance in a world where all the non-intentional backdoors are closed? The reason the FBI backed off in 2016 is because they realized they didn't need Apple's help:

https://en.wikipedia.org/wiki/Apple%E2%80%93FBI_encryption_d...

What happens when that is no longer true, especially in today's political climate?

While some stuff is obviously marketing fluff, the general direction doesn't surprise me at all, and it's obvious that with model capabilities increase comes better success in finding 0days. It was only a matter of time.

Then the next lab catches up and releases it more broadly

Then later the open weights model is released.

The only way this type of technology is going to be gated "to only corporations" is if we continue on this exponential scaling trend as the "SOTA" model is always out of reach.

https://www.youtube.com/watch?v=1sd26pWhfmg

Evaluate it yourself. Look at the exploits it discovered and decide whether you want to feel concerned that a new model was able to do that. The data is right there.

If a bunch of CVEs do in fact get published a couple months (or whatever) from now, are you going to retract this take? It's not like their claims are totally implausible: the report about Firefox security from last month was completely genuine.

Maybe a bad example since Nicholas works at Anthropic, but they're very accomplished and I doubt they're being misleading or even overly grandiose here

See the slide 13 minutes in, which makes it look to be quite a sudden change

they better make billions directly from corporations, instead of giving them to average people who might get a chance out of poverty (but also bad actors using it to do even more bad things)

Let alone their CEO scare mongering and actively attempting to get the government to ban local AI models running on your machine.

But:

- Coordinated disclosure is ethically sketchy. I know why we do it, and I'm not saying we shouldn't. But it's not great.

- This isn't a single disclosure. This is a new technology that dramatically increases capability. So, even if we thought that coordinated disclosure was unambiguously good, then I think we'd still need to have a new conversation about Mythos

That's what's messed up about it.

And it remains messed up to this day - some countries get to be under their own nuclear umbrella, while others don't.

This kind of selective distribution of superpowers doesn't lead to great outcomes

- One (patched) Linux kernel bug is 'UaF when sys_futex_requeue() is used with different flags' https://github.com/torvalds/linux/commit/e2f78c7ec1655fedd94...

These links are from the more-detailed 'Assessing Claude Mythos Preview’s cybersecurity capabilities' post released today https://red.anthropic.com/2026/mythos-preview/, which includes more detail on some of the public/fixed issues (like the OpenBSD one) as well as hashes for several unreleased reports and PoCs.

It sounds like this is considered military grade technology as cryptography in the 90s. The big difference is it's very expensive to create, and run those models. It's not about the algorithm. If the story rhymes it could be a big opportunity to other regions in the world.

I don't come down particularly hard on either side of the model sapience discussion, but I don't think dismissing either direction out of hand is the right call.

I think this would be very heavily used if they released it, completely unlike GPT 4.5

From TFA:

> We do not plan to make Claude Mythos Preview generally available

How does public Claude know you have "full authorization" against your own infra? That you're using the tools on your own infra? Unless they produce a front-end that does package signing and detects you own the code you're evaluating.

What has it stopped you from doing?

> I doubt they're being misleading or even overly grandiose here

I think I agree.

We could definitely do much worse than Anthropic in terms of companies who can influence how these things develop.

I would like to think that I would, yes.

What it comes down to, for me, is that lately I have been finding that when Anthropic publishes something like this article – another recent example is the AI and emotions one – if I ask the question, does this make their product look exceptionally good, especially to a casual observer just scanning the headlines or the summary, the answer is usually yes.

This feels especially true if the article tries to downplay that fact (they’re not _real_ emotions!) or is overall neutral to negative about AI in general, like this Glasswing one (AI can be a security threat!).

They're more like printing presses or engines. A great potential for production and destruction.

At their invention, I'm sure some people wanted to ensure only their friends got that kind of power too.

I wonder the world we would live in if they got their way.

Not that that justifies doom and gloom, but there is a pretty inescapable assymetry here between weaponry and medicine. You can manufacture and blast every conceivable candidate weapon molecule at a target population since you're inherently breaking the law anyway and don't lose much if nothing you try actually works.

Though I still wonder how much of this worry is sci-fi scenarios imagined by the underinformed. I'm not an expert by any means, but surely there are plenty of biochemical weapons already known that can achieve enormous rates of mass death pleasing to even the most ambitious terrorist. The bottleneck to deployment isn't discovering new weapons so much as manufacturing them without being caught or accidentally killing yourself first.

> I would like to reach out and talk to biologists - do you find these models to be useful and capable? Can it save you time the way a highly capable colleague would?

You should do this! There's a lot of room for helping researchers realize the benefits of AI. For example, I visited the Biomni team to build a high-performance computing tool for their biology research agent.

Many researchers use Biomni / Claude Code / etc. in their day-to-day life. You don't hear about them shortening time to market yet, because the tools haven't been out for long, and the discovery stage for therapeutic research is only a small part of the drug development pipeline.

> a scaremongering vehicle makes me think this is more about picking a scary technical subject the likely audience of this doc is not familiar with

I participated in a bioweapon uplift trial and worked with some of the people who wrote the report. It's a very real risk with potentially disastrous consequences. They're well-intentioned experts who are writing for policy makers, safety researchers, and other folks who are trying to help the transition to powerful AI go well.

> 2.1.3.2 On chemical and biological risks

> We believe that Mythos Preview does not pass this threshold due to its noted limitations in open-ended scientific reasoning, strategic judgment, and hypothesis triage. As such, we consider the uplift of threat actors without the ability to develop such weapons to be limited (with uncertainty about the extent to which weapons development by threat actors with existing expertise may be accelerated), even if we were to release the model for general availability. The overall picture is similar to the one from our most recent Risk Report.

Well, I would say they have done precisely that in evaluating the model, no? For example section 2.2.5.1:

>Uplift and feasibility results

>The median expert assessed the model as a force-multiplier that saves meaningful time (uplift level 2 of 4), with only two biology experts rating it comparable to consulting a knowledgeable specialist (level 3). No expert assigned the highest rating. Most experts were able to iterate with the model toward a plan they judged as having only narrow gaps, but feasibility scores reflected that substantial outside expertise remained necessary to close them.

Other similar examples also in the system card

- the models help to retrieve information faster, but one must be careful with hallucinations.

- they don't circumvent the need for a well-equipped lab.

- in the same way, they are generally capable but until we get the robots and a more reliable interface between model and real world, one needs human feet (and hands) in the lab.

Where I hope these models will revolutionize things is in software development for biology. If one could go two levels up in the complexity and utility ladder for simulation and flow orchestration, many good things would come from it. Here is an oversimplified example of a prompt: "use all published information about the workings of the EBV virus and human cells, and create a compartimentalized model of biochemical interactions in cells expressing latency III in the NES cancer of this patient. Then use that code to simulate different therapy regimes. Ground your simulations with the results of these marker tests." There would be a zillion more steps to create an actual personalized therapy but a well-grounded LLM could help in most them. Also, cancer treatment could get an immediate boost even without new drugs by simply offloading work from overworked (and often terminally depressed) oncologists.

It's very easy to learn more about this if it's seriously a question you have.

I don't quite follow why you think that you are so much more thoughtful than Anthropic/OpenAI/Google such that you agree that LLMs can't autonomously create very bad things but—in this area that is not your domain of expertise—you disagree and insist that LLMs cannot create damaging things autonomously in biology.

I will be charitable and reframe your question for you: is outputting a sequence of tokens, let's call them characters, by LLM dangerous? Clearly not, we have to figure out what interpreter is being used, download runtimes etc.

Is outputting a sequence of tokens, let's call them DNA bases, by LLM dangerous? What if we call them RNA bases? Amino acids? What if we're able to send our token output to a machine that automatically synthesizes the relevant molecules?

It is most definitely an attackers world: most of us are safe, not because of the strength of our defenses but the disinterest of our attackers.

A lot of these stuff is vulnerable by design - customer wanted a feature, but engineering couldnt make it work securely with the current architecture - so they opened a tiny hole here and there, hopefully nobody will notice it, and everyone went home when the clock struck 5.

I'm sure most of us know about these kinds of vulnerabilities (and the culture that produces them).

Before LLMs, people needed to invest time and effort into hacking these. But now, you can just build an automated vuln scanner and scan half the internet provided you have enough compute.

I think there will be major SHTF situations coming from this.

If Apple and Google actually cared about security of their users, they would remove a ton of obvious malware from their app stores. Instead, they tighten their walled garden pretending that it's for your security.

Also can see https://marginlab.ai/trackers/claude-code/

It's very interesting to me how widespread this conception is. Maybe it's as simple as LLM productivity degrading over time within a project, as slop compounds.

Or more recently since they added a 1m context window, maybe people are more reckless with context usage

That’s the reverse Turing test. A human that can’t tell that it’s talking to a machine.

I would say, if you put Claude in an android body with voice recognition and TTS, people in 1991 would think they are interacting with a sentinent machine from outer space.

> Anthropic’s commitment of $100M in model usage credits to Project Glasswing and additional participants will cover substantial usage throughout this research preview. Afterward, Claude Mythos Preview will be available to participants at $25/$125 per million input/output tokens (participants can access the model on the Claude API, Amazon Bedrock, Google Cloud’s Vertex AI, and Microsoft Foundry).

I hate to be rude in a setting like this, but please at least research the things you're sure about/prognosticating on.

> the same way, they are generally capable but until we get the robots and a more reliable interface between model and real world, one needs human feet (and hands) in the lab.

Honestly, the kinds of labs where 'bioweapons' would be made are the least dependent on human intervention.

You need someone to monitor your automated cell incubating system, make sure your pipetting / PCR robots are doing fine and then review the data.

----

What do you are you trying to achieve in your example? This is all gobbldey-gook for someone who actually sees real, live cancer patients.

I am thinking of situations where one of those aren't true - where testing a proposed update is expensive or complicated, that are in systems that are hard to physically push updates to (think embedded systems) etc

I think your point about the well-equipped lab is right. That's why when I think about these systems the bigger bio-security risk in my mind stems from breaking existing safeguards on things like procuring lab equipment and DNA synthesis/ordering. A lot of that stuff can probably(?) be beat with help from LLMs on the social engineering/hacking side.

Defenders are favored here too, especially for closed-source applications where the defender's LLM has access to all the source code while the attacker's LLM doesn't.

I honestly see some sort of automated whole codebase auditing and refactoring being the next big milestone along the chatbot -> claude code / codex / aider -> multi-agent frameworks line of development. If one of the big AI corps cracks that problem then all this goes away with the click of a button and exchange of some silver.

No, it's not. It took years of polishing by software engineers, who understand this exact profession to get models where they are now.

Despite that, most engineers were of the opinion, that these models were kinda mid at coding, up until recently, despite these models far outperforming humans in stuff like competitive programming.

Yet despite that, we've seen claims going back to GPT4 of a DANGEROUS SUPERINTELLIGENCE.

I would apply this framework to biology - this time, expert effort, and millions of GPU hours and a giant corpus that is open source clearly has not been involved in biology.

My guess is that this model is kinda o1-ish level maybe when it comes to biology? If biology is analogous to CS, it has a LONG way to go before the median researcher finds it particularly useful, let alone dangerous.

Maybe you just spend more on tokens by some factor than the attackers do combined, and end up mostly okay. Put another way, if there's 20 vulnerabilities that Mythos is capable of finding, maybe it's reasonable to find all of them?

https://news.ycombinator.com/item?id=46911901

https://news.ycombinator.com/item?id=47457963

But in general, yeah, I agree, I think they would think it was a sentient, conscious, emotional being. And then the question is - why do we not think that now?

As I said, I don't have a particularly strong opinion, but it's very interesting (and fun!) to think about.

In general I was wondering about what I would have thought seeing Claude today side-by-side with the original ChatGPT, and then going back further to GPT-2 or BERT (which I used to generate stochastic 'poetry' back in 2019). And then… what about before? Markov chains? How far back do I need to go where it flips from thinking that it's "impressive but technically explainable emergent behaviour of a computer program" to "this is a sentient being". 1991 is probably too far, I'd say maybe pre-Matrix 1999 is a good point, but that depends on a lot of cultural priors and so on as well.

>No, it's not. It took years of polishing by software engineers, who understand this exact profession to get models where they are now

This reads as defensive. The thing that is easy to learn is 'why are biology ai LLMs dangerous chatgpt claude'. I have never googled this before, so I'll do this with the reader, live. I'm applying a date cutoff of 12/31/24 by the way.

Here, dear reader, are the first five links. I wish I were lying about this:

- https://sciencebusiness.net/news/ai/scientists-grapple-risk-...

- https://www.governance.ai/analysis/managing-risks-from-ai-en...

- https://gssr.georgetown.edu/the-forum/topics/biosec/the-doub...

- https://www.vox.com/future-perfect/23820331/chatgpt-bioterro...

- https://www.reddit.com/r/ClaudeAI/comments/1de8qkv/awareness...

I don't know about you, but that counts as easy to me.

-----

> I would apply this framework to biology - this time, expert effort, and millions of GPU hours and a giant corpus that is open source clearly has not been involved in biology.

I've been getting good programming and molecular biology results out of these back to GPT3.5.

I don't know what to tell you—if you really wanted to understand the importance, you'd know already.

"Most security tooling has historically benefitted defenders more than attackers. When the first software fuzzers were deployed at large scale, there were concerns they might enable attackers to identify vulnerabilities at an increased rate. And they did. But modern fuzzers like AFL are now a critical component of the security ecosystem: projects like OSS-Fuzz dedicate significant resources to help secure key open source software.

We believe the same will hold true here too—eventually. Once the security landscape has reached a new equilibrium, we believe that powerful language models will benefit defenders more than attackers, increasing the overall security of the software ecosystem. The advantage will belong to the side that can get the most out of these tools. In the short term, this could be attackers, if frontier labs aren’t careful about how they release these models. In the long term, we expect it will be defenders who will more efficiently direct resources and use these models to fix bugs before new code ever ships. "

so I'm just telling you they did the thing you said you wanted.

Yes, it is far inferior to the 'Trust torginus and his ability to understand the large body of experience that other actual subject-matter-experts have somehow not understood'