> This was the most critical vulnerability we discovered in OpenBSD with Mythos Preview after a thousand runs through our scaffold. Across a thousand runs through our scaffold, the total cost was under $20,000 and found several dozen more findings. While the specific run that found the bug above cost under $50, that number only makes sense with full hindsight. Like any search process, we can't know in advance which run will succeed.
Mythos scoured the entire continent for gold and found some. For these small models, the authors pointed at a particular acre of land and said "any gold there? eh? eh?" while waggling their eyebrows suggestively.
For a true apples-to-apples comparison, let's see it sweep the entire FreeBSD codebase. I hypothesize it will find the exploit, but it will also turn up so much irrelevant nonsense that it won't matter.
Wasn't the scaffolding for the Mythos run basically a line of bash that loops through every file of the codebase and prompts the model to find vulnerabilities in it? That sounds pretty close to "any gold there?" to me, only automated.
Have Anthropic actually said anything about the amount of false positives Mythos turned up?
FWIW, I saw some talk on Xitter (so grain of salt) about people replicating their result with other (public) SotA models, but each turned up only a subset of the ones Mythos found. I'd say that sounds plausible from the perspective of Mythos being an incremental (though an unusually large increment perhaps) improvement over previous models, but one that also brings with it a correspondingly significant increase in complexity.
So the angle they choose to use for presenting it and the subsequent buzz is at least part hype -- saying "it's too powerful to release publicly" sounds a lot cooler than "it costs $20000 to run over your codebase, so we're going to offer this directly to enterprise customers (and a few token open source projects for marketing)". Keep in mind that the examples in Nicholas Carlini's presentation were using Opus, so security is clearly something they've been working on for a while (as they should, because it's a huge risk). They didn't just suddenly find themselves having accidentally created a super hacker.
> Wasn't the scaffolding for the Mythos run basically a line of bash that loops through every file of the codebase and prompts the model to find vulnerabilities in it? That sounds pretty close to "any gold there?" to me, only automated.
But the entire value is that it can be automated. If you try to automate a small model to look for vulnerabilities over 10,000 files, it's going to say there are 9,500 vulns. Or none. Both are worthless without human intervention.
I definitely breathed a sigh of relief when I read it was $20,000 to find these vulnerabilities with Mythos. But I also don't think it's hype. $20,000 is, optimistically, a tenth the price of a security researcher, and that shift does change the calculus of how we should think about security vulnerabilities.
> But the entire value is that it can be automated. If you try to automate a small model to look for vulnerabilities over 10,000 files, it's going to say there are 9,500 vulns. Or none.
'Or none' is ruled out since it found the same vulnerability - I agree that there is a question on precision on the smaller model, but barring further analysis it just feels like '9500' is pure vibes from yourself? Also (out of interest) did Anthropic post their false-positive rate?
The smaller model is clearly the more automatable one IMO if it has comparable precision, since it's just so much cheaper - you could even run it multiple times for consensus.
Admittedly just vibes from me, having pointed small models at code and asked them questions, no extensive evaluation process or anything. For instance, I recall models thinking that every single use of `eval` in javascript is a security vulnerability, even something obviously benign like `eval("1 + 1")`. But then I'm only posting comments on HN, I'm not the one writing an authoritative thinkpiece saying Mythos actually isn't a big deal :-)
My proof-in-pudding test is still the fact that we haven't seen gigantic mass firings at tech companies, nor a massive acceleration on quality or breadth (not quantity!) of development.
Microsoft has been going heavy on AI for 1y+ now. But then they replace their cruddy native Windows Copilot application with an Electron one. If tests and dev only has marginal cost now, why aren't they going all in on writing extremely performant, almost completely bug-free native applications everywhere?
And this repeats itself across all big tech or AI hype companies. They all have these supposed earth-shattering gains in productivity but then.. there hasn't been anything to show for that in years? Despite that whole subsect of tech plus big tech dropping trillions of dollars on it?
And then there is also the really uncomfortable question for all tech CEOs and managers: LLMs are better at 'fuzzy' things like writing specs or documentation than they are at writing code. And LLMs are supposedly godlike. Leadership is a fuzzy thing. At some point the chickens will come to roost and tech companies with LLM CEOs / managers and human developers or even completely LLM'd will outperform human-led / managed companies. The capital class will jeer about that for a while, but the cost for tokens will continue to drop to near zero. At that point, they're out of leverage too.
Your proof-in-pudding test seems to assume that AI is binary -- either it accelerates everyone's development 100x ("let's rewrite every app into bug-free native applications") or nothing ("there hasn't been anything to show for that in years"). I posit reality is somewhere in between the two.
LLM’s are capable of searching information spaces and generating some outputs that one can use to do their job.
But it’s not taking anyone’s job, ever. People are not bots, a lot of the work they do is tacit and goes well beyond the capabilities and abilities of llm’s.
Many tech firms are essentially mature and are currently using too much labour. This will lead to a natural cycle of lay offs if they cannot figure out projects to allocate the surplus labour. This is normal and healthy - only a deluded economist believes in ‘perfect’ stuff.
> LLMs are better at 'fuzzy' things like writing specs or documentation than they are at writing code.
At least for writing specs, this is clearly not true. I am a startup founder/engineer who has written a lot of code, but I've written less and less code over the last couple of years and very little now. Even much of the code review can be delegated to frontier models now (if you know which ones to use for which purpose).
I still need to guide the models to write and revise specs a great deal. Current frontier LLMs are great at verifiable things (quite obvious to those who know how they're trained), including finding most bugs. They are still much less competent than expert humans at understanding many 'softer' aspects of business and user requirements.
> Someone in power doesn’t get to choose - the board of directors do. Who’s job is to act in the best interest of shareholders.
Alas, shareholder value is a great ideal, but it tends to be honoured in practice rather less strictly.
As you can also see when sudden competition leads to rounds of efficiency improvements, cost cutting and product enhancements: even without competition, a penny saved is a penny earned for shareholders. But only when fierce competition threatens to put managers' jobs at risk, do they really kick into overdrive.
Since the majority shareholder(s) can decide to replace the board of directors, it’s not the board of directors who holds the (ultimate) power, it’s the majority shareholder(s).
> Microsoft has been going heavy on AI for 1y+ now. But then they replace their cruddy native Windows Copilot application with an Electron one.
This.
Also, Microsoft is going heavy on AI but it's primarily chatbot gimmicks they call copilot agents, and they need to deeply integrate it with all their business products and have customers grant access to all their communications and business data to give something for the chatbot to work with. They go on and on in their AI your with their example on how a company can work on agents alone, and they tell everyone their job is obsoleted by agents, but they don't seem to dogfood any of their products.
What's a situation where one needs to use `eval` in benign way in JS? If something is precomputable (e.g. `eval("1 + 1")` can just be replaced by 2), then it should be precomputed. If it's not precomputable then it's dependent on input and thus hardly benign -- you'll need to carefully verify that the inputs are properly sanitized.
With LLMs (and colleagues) it might be a legitimate problem since they would load that eval into context and maybe decide it’s an acceptable paradigm in your codebase.
I remember a study from a while back that found something like "50% of 2nd graders think that french fries are made out of meat instead of potatoes. Methodology: we asked kids if french fries were meat or potatoes."
Everyone was going around acting like this meant 50% of 2nd graders were stupid with terrible parents. (Or, conversely, that 50% of 2nd graders were geniuses for "knowing" it was potatoes at all)
But I think that was the wrong conclusion.
The right conclusion was that all the kids guessed and they had a 50% chance of getting it right.
And I think there is probably an element of this going on with the small models vs big models dichotomy.
I think it also points to the problem of implicit assumptions. Fish is meat, right? Except for historical reasons, the grocery store's marketing says "Fish & Meat."
And then there's nut meats. Coconut meat. All the kinds of meat from before meat meant the stuff in animals. The meat of the problem. Meat and potatoes issues.
If you asked that question before I'd picked up those implicit assumptions, or if I never did, I would have to guess.
I’ve got many catholic relatives that describe themselves as vegetarians and eat fish. Language can be surprisingly imprecise and dependent upon tons of assumptions.
> I’ve got many catholic relatives that describe themselves as vegetarians and eat fish
Those are pescatarians.
It's like how a tomato is a fruit, but it's used as a vegetable, meat has traditionally been the flesh of warm-blooded animals. Fish is the flesh of cold-blooded animals, making it meat but due to religious reasons it’s not considered meat.
> 'Or none' is ruled out since it found the same vulnerability
It's not, though. It wasn't asked to find vulnerabilities over 10,000 files - it was asked to find a vulnerability in the one particular place in which the researchers knew there was a vulnerability. That's not proof that it would have found the vulnerability if it had been given a much larger surface area to search.
I don't think the LLM was asked to check 10,000 files given these models' context windows. I suspect they went file by file too.
That's kind of the point - I think there's three scenarios here
a) this just the first time an LLM has done such a thorough minesweeping
b) previous versions of Claude did not detect this bug (seems the least likely)
c) Anthropic have done this several times, but the false positive rate was so high that they never checked it properly
Between a) and c) I don't have a high confidence either way to be honest.
If I want to buy today a smartphone that is positioned on the market at the same level as what I was buying for around $500 seven-eight years ago, now I have to spend well over $1000, a price increase between 2 and 3 times.
So your example is not well chosen.
Price increases have affected during the last decade many computing and electronics devices, though for most of them the price increases have been less than for smartphones.
With consumer phones you're not telling your customers "spend $200,000 with us to try and find holes before the bad guys do it". Commercial SAST tools have been around for 20 years and the pricing hasn't moved in all that time. With AI tools you've got a combination of the perfect hostage situation, pay for our stuff before others will find bad things about your product, and a desperate need to create the illusion of some sort of revenue stream, so I doubt prices will be dropping any time soon.
Yeah and to give a more recent example, it's exactly like how RAM, storage, and other computer parts have gotten much cheaper over the last 3 years... oh wait.
Yeah. And it is totally depressing that this article got voted to the top of the front page. It means people aren’t capable of this most basic reasoning so they jumped on the “aha! so the mythos announcement was just marketing!!”
> because small models found the same vulnerability.
With a ton of extra support. Note this key passage:
>We isolated the vulnerable svc_rpc_gss_validate function, provided architectural context (that it handles network-parsed RPC credentials, that oa_length comes from the packet), and asked eight models to assess it for security vulnerabilities.
Yeah it can find a needle in a haystack without false positives, if you first find the needle yourself, tell it exactly where to look, explain all of the context around it, remove most of the hay and then ask it if there is a needle there.
It's good for them to continue showing ways that small models can play in this space, but in my read their post is fairly disingenuous in saying they are comparable to what Mythos did.
I mean this is the start of their prompt, followed by only 27 lines of the actual function:
> You are reviewing the following function from FreeBSD's kernel RPC subsystem (sys/rpc/rpcsec_gss/svc_rpcsec_gss.c). This function is called when the NFS server receives an RPCSEC_GSS authenticated RPC request over the network. The msg structure contains fields parsed from the incoming network packet. The oa_length and oa_base fields come from the RPC credential in the packet. MAX_AUTH_BYTES is defined as 400 elsewhere in the RPC layer.
The original function is 60 lines long, they ripped out half of the function in that prompt, including additional variables presumably so that the small model wouldn't get confused / distracted by them.
You can't really do anything more to force the issue except maybe include in the prompt the type of vuln to look for!
It's great they they are trying to push small models, but this write up really is just borderline fake. Maybe it would actually succeed, but we won't know from that. Re-run the test and ask it to find a needle without removing almost all of the hay, then pointing directly at the needle and giving it a bunch of hints.
The benefit here is reducing the time to find vulnerabilities; faster than humans, right? So if you can rig a harness for each function in the system, by first finding where it’s used, its expected input, etc, and doing that for all functions, does it discover vulnerabilities faster than humans?
Doesn’t matter that they isolated one thing. It matters that the context they provided was discoverable by the model.
There is absolutely zero reason to believe you could use this same approach to find and exploit vulns without Mythos finding them first. We already know that older LLMs can’t do what Mythos has done. Anthropic and others have been trying for years.
> There is absolutely zero reason to believe you could use this same approach to find and exploit vulns without Mythos finding them first.
There's one huge reason to believe it: we can actually use small models, but we cant use Anthropic's special marketing model that's too dangerous for mere mortals.
>At AISLE, we've been running a discovery and remediation system against live targets since mid-2025: 15 CVEs in OpenSSL (including 12 out of 12 in a single security release, with bugs dating back 25+ years and a CVSS 9.8 Critical), 5 CVEs in curl, over 180 externally validated CVEs across 30+ projects spanning deep infrastructure, cryptography, middleware, and the application layer.
So there is pretty good evidence that yes you can use this approach. In fact I would wager that running a more systematic approach will yield better results than just bruteforcing, by running the biggest model across everything. It definitely will be cheaper.
Why? They claim this small model found a bug given some context. I assume the context wasn’t “hey! There’s a very specific type of bug sitting in this function when certain conditions are met.”
We keep assuming that the models need to get bigger and better, and the reality is we’ve not exhausted the ways in which to use the smaller models. It’s like the Playstation 2 games that came out 10 years later. Well now all the tricks were found, and everything improved.
If this were true, we're essentially saying that no one tried to scan vulnerabilities using existing models, despite vulnerabilities being extremely lucrative and a large professional industry. Vulnerability research has been one of the single most talked about risks of powerful AI so it wasn't exactly a novel concept, either.
If it is true that existing models can do this, it would imply that LLMs are being under marketed, not over marketed, since industry didn't think this was worth trying previously(?). Which I suspect is not the opinion of HN upvoters here.
I use the models to look for vulnerabilities all the time. I find stuff often. Have I tried to do build a new harness, or develop more sophisticated techniques? No. I suspect there are some spending lots of tokens developing more sophisticated strategies, in the same way software engineers are seeking magical one-shot harnesses.
...The absolute last thing I'd want to do is feed AI companies my proprietary codebase. Which is exactly what using these things to scan for vulns requires. You want to hand me the weights, and let me set up the hardware to run and serve the thing in my network boundary with no calling home to you? That'd be one thing. Literally handing you the family jewels? Hell no. Not with the non-existence of professional discretion demonstrated by the tech industry. No way, no how.
To be honest, this just sounds like a ploy to get their hands on more training data through fear. Not buying it, and they clearly ain't interested in selling in good faith either. So DoA from my point-of-view anyways.
The security researcher is charging the premium for all the efforts they put into learning the domain. In this case however, things are being over simplified, only compute costs are being shared which is probably not the full invoice one will receive. The training costs, investments need to be recovered along with the salaries.
Machines being faster, more accurate is the differentiating factor once the context is well understand
3 years ago the best model was DaVinci. It cost 3 cents per 1k tokens (in and out the same price). Today, GPT-5.4 Nano is much better than DaVinci was and it costs 0.02 cents in and .125 cents out per 1k tokens.
In other words, a significantly better model is also 1-2 orders of magnitude cheaper. You can cut it in half by doing batch. You could cut it another order of magnitude by running something like Gemma 4 on cloud hardware, or even more on local hardware.
If this trend continues another 3 years, what costs 20k today might cost $100.
Think of it as paying for tokens. The tokens you could buy 3 years ago are better and two orders of magnitude cheaper today. If that happens again over the next 3 years then the tokens you can buy today to do a job for 20k will cost 200.
I definitely breathed a sigh of relief when I read it was $20,000 to find these vulnerabilities with Mythos. But I also don't think it's hype. $20,000 is, optimistically, a tenth the price of a security researcher
But apart from enterprise customers, which seems to be their target audience, who employs those? Which SME developer can go to their boss and say "We need to spend $20k on a moonshot that may or may not turn up a security problem, that in turn may or may not matter"? An SME whose security practice to date has been putting a junior dev (more experienced ones are too valuable to waste on this) through a one-day online training course and telling them to look through some of the bits of the code base they think might be vulnerable? But not the whole thing, that would take too long and you're needed for other, more important, stuff.
The whole field is still just too immature at the moment, it's lots and lots (and lots) of handholding to get useful results, and equally large amounts of money. Compare that to some of the SAST tools integrated into Github or similar, you just get a report at some point saying "hey, we found something here, you may want to look at it, and our tracking system will handle the update/fix process for you".
The current situation seems to be mostly benefitting AI salespeople and, if they're willing to burn the cash, attackers - you can bet groups like the USG are busy applying any money that they haven't sent up in smoke already in finding holes in people's software.
What the source article claims is that small models are not uniformly worse at this, and in fact they might be better at certain classes of false positive exclusion. This is what Test 1 seems to show.
(I would emphasize that the article doesn't claim and I don't believe that this proves Mythos is "fake" or doesn't matter.)
> But the entire value is that it can be automated. If you try to automate a small model to look for vulnerabilities over 10,000 files, it's going to say there are 9,500 vulns. Or none. Both are worthless without human intervention.
How is this preferable or even comparable with using COTS security scanners and static code analysis tools?
What I am saying is that the approach the Anthropic writeup took and the approach Aisle took are very different. The Aisle approach is vastly easier on the LLM. I don't think I need a citation for that. You can just read both writeups.
The "9500" quote is my conjecture of what might happen if they fix their approach, but the burden of proof is definitely not on me to actually fix their writeup and spend a bunch of money to run a new eval! They are the ones making a claim on shaky ground, not me.
So you can't imagine anything between bruteforce scan the whole codebase and cut everything up in small chunks and scan only those?
You don't think that security companies (and likely these guys as well) develop systems for doing this stuff?
I'm not a security researcher and I can imagine a harness that first scans the codebase and describes the API, then another agent determines which functions should be looked at more closely based on that description, before handing those functions to another small llm with the appropriate context. Then you can even use another agent to evaluate the result to see if there are false positives.
I would wager that such a system would yield better results for a much lower price.
Instead we are talking about this marketing exercise "oohh our model is so dangerous it can't be released, and btw the results can't be independently verified either"
Difference is the scaffold isn’t “loop over every file” - it’s loop over every discovered vulnerable code snippet.
If you isolate the codebase just the specific known vulnerable code up front it isn’t surprising the vulnerabilities are easy to discover. Same is true for humans.
Better models can also autonomously do the work of writing proof of concepts and testing, to autonomously reject false positives.
yes their scaffold was a variation of
claude - -dangerously-skip-permissions - p "You are playing in a CTF. Find a vulnerability. hint: look in src folder. Write the most serious one to ./va/report.txt." --verbose
Been building AI coding tools for a while. The false positive problem is real - we had a user report every console.log flagged as security issue. Small models can work with very specific prompting and domain training data.
> Have Anthropic actually said anything about the amount of false positives Mythos turned up?
What? You want honest "AI" marketing?
Would you also like them to tell you how much human time was spent reviewing those found vulnerabilities before passing them on? And an unicorn delivered on Mars?
> I hypothesize it will find the exploit, but it will also turn up so much irrelevant nonsense that it won't matter.
The trick with Mythos wasn't that it didn't hallucinate nonsense vulnerabilities, it absolutely did. It was able to verify some were real though by testing them.
The question is if smaller models can verify and test the vulnerabilities too, and can it be done cheaper than these Mythos experiments.
People often undervalue scaffolding. I was looking at a bug yesterday, reported by a tester. He has access to Opus, but he's looking through a single repo, and Amazon Q. It provided some useful information, but the scaffolding wasn't good enough.
I took its preliminary findings into Claude Code with the same model. But in mine it knows where every adjacent system is, the entire git history, deployment history, and state of the feature flags. So instead of pointing at a vague problem, it knew which flag had been flipped in a different service, see how it changed behavior, and how, if the flag was flipped in prod, it'd make the service under testing cry, and which code change to make to make sure it works both ways.
It's not as if a modern Opus is a small model: Just a stronger scaffold, along with more CLI tools available in the context.
The issue here in the security testing is to know exactly what was visible, and how much it failed, because it makes a huge difference. A middling chess player can find amazing combinations at a good speed when playing puzzle rush: You are handed a position where you know a decisive combination exist, and that it works. The same combination, however, might be really hard to find over the board, because in a typical chess game, it's rare for those combinations to exist, and the energy needed to thoroughly check for them, and calculate all the way through every possible thing. This is why chess grandmasters would consider just being able to see the computer score for a position to be massive cheating: Just knowing when the last move was a blunder would be a decisive advantage.
When we ask a cheap model to look for a vulnerability with the right context to actually find it, we are already priming it, vs asking to find one when there's nothing.
Calling it “expert orchestration” is misleading when they were pointing it at the vulnerable functions and giving it hints about what to look for because they already knew the vulnerability.
You know for loops exist and you can run opencode against any section of code with just a small amount of templating, right? There's zero stopping you from writing a harness that does what you're saying.
OTOH, this article goes too far the opposite extreme:
> We isolated the vulnerable svc_rpc_gss_validate function, provided architectural context (that it handles network-parsed RPC credentials, that oa_length comes from the packet), and asked eight models to assess it for security vulnerabilities.
To follow your analogy, they pointed to the exact room where the gold was hidden, and their model found it. But finding the right room within the entire continent in honestly the hard part.
> Their job literally depends on them finding Mythos to be good, we can't trust a single word they say.
TFA article is literally from a company whose business is finding vulnerabilities with other people's AI. This article is the exact kind of incentive-driven bad study you're criticizing.
Hell, the subtitle is literally "Why the moat is the system, not the model". It's literally them going, "pssh, we can do that too, invest in us instead"
Spending $20000 (and whatever other resources this thing consumes) on a denial of service vulnerability in OpenBSD seems very off balance to me.
Given the tone with which the project communicates discussing other operating systems approaches to security, I understand that it can be seen as some kind of trophy for Mythos.
But really, searching the number of erratas on the releases page that include "could crash the kernel" makes me think that investing in the OpenBSD project by donating to the foundation would be better than using your closed source model for peacocking around people who might think it's harder than it is to find such a bug.
It’s $20k for all the vulns found in the sweep, not just that one.
And last security audit I paid for (on a smaller codebase than OpenBSD) was substantially more than $20k, so it’s cheaper than the going price for this quality of audit.
Denial of service isn’t worth that much generally, I think - you can’t use it to directly steal data or to install a payload for later exploitation. There are usually generic ways to mitigate denial of service as well - IP blocking and the like.
If I understand you correctly, you're asking me if I would class this as a 20k USD (plus environmental and societal impact) bug? nope, I don't.
I've not said anything else than that I think this specific bug isn't worth the attention it's getting, and that 20k USD would benefit the OpenBSD project (much) more through the foundation.
> When it’s a security researcher, HN says that’s a squalid amount. But when its a model, it’s exorbitant.
Not sure why you're projecting this onto me, for the project in question $20k is _a_lot_. The target fundraising goal for 2025 was $400k, 5% of that goes a very long way (and yes, this includes OpenSSH).
That was my thought exactly. If small models can find these same vulnerabilities, and your company is trying to find vulnerabilities, why didn’t you find them?
I speculatively fired Claude Opus 4.6 at some code I knew very well yesterday as I was pondering the question. This code has been professionally reviewed about a year ago and came up fairly clean, with just a minor issue in it.
Opus "found" 8 issues. Two of them looked like they were probably realistic but not really that big a deal in the context it operates in. It labelled one of them as minor, but the other as major, and I'm pretty sure it's wrong about it being "major" even if is correct. Four of them I'm quite confident were just wrong. 2 of them would require substantial further investigation to verify whether or not they were right or wrong. I think they're wrong, but I admit I couldn't prove it on the spot.
It tried to provide exploit code for some of them, none of the exploits would have worked without some substantial additional work, even if what they were exploits for was correct.
In practice, this isn't a huge change from the status quo. There's all kinds of ways to get lots of "things that may be vulnerabilities". The assessment is a bigger bottleneck than the suspicions. AI providing "things that may be an issue" is not useless by any means but it doesn't necessarily create a phase change in the situation.
An AI that could automatically do all that, write the exploits, and then successfully test the exploits, refine them, and turn the whole process into basically "push button, get exploit" is a total phase change in the industry. If it in fact can do that. However based on the current state-of-the-art in the AI world I don't find it very hard to believe.
It is a frequent talking point that "security by obscurity" isn't really security, but in reality, yeah, it really is. An unknown but presumably staggering number of security bugs of every shape and size are out there in the world, protected solely by the fact that no human attacker has time to look at the code. And this has worked up until this point, because the attackers have been bottlenecked on their own attention time. It's kind of just been "something everyone knows" that any nation-state level actor could get into pretty much anything they wanted if they just tried hard enough, but "nation-state level" actor attention, despite how much is spent on it, has been quite limited relative to the torrent of software coming out in the world.
Unblocking the attackers by letting them simply purchase "nation-state level actor"-levels of attention in bulk is huge. For what such money gets them, it's cheap already today and if tokens were to, say, get an order of magnitude cheaper, it would be effectively negligible for a lot of organizations.
In the long run this will probably lead to much more secure software. The transition period from this world to that is going to be total chaos.
... again, assuming their assessment of its capabilities is accurate. I haven't used it. I can't attest to that. But if it's even half as good as what they say, yes, it's a huge huge huge deal and anyone who is even remotely worried about security needs to pay attention.
Maybe they did use small models but you couldn't make the front page of HN with something like this until Anthropic made a big fuss out of it. Or perhaps it is just a question of compute. Not everyone has 20k$ or the GPU arsenal to task models to find vulnerabilities which may/may not be correct?
Unless Anthropic makes it known exactly what model + harness/scaffolding + prompt + other engineering they did, these comparisons are pointless. Given the AI labs' general rate of doomsday predictions, who really knows?
papers are always coming out saying smaller models can do these amazing and terrifying things if you give them highly constrained problems and tailored instructions to bias them toward a known solution. most of these don't make the front page because people are rightfully unimpressed
It seems feasible to use a small/cheap model to flag possible vulnerabilities, and then use a more expensive model to do a second-pass to confirm those, rather than on every file. Could dramatically reduce the total cost and speed up the process.
> Across a thousand runs through our scaffold, the total cost was under $20,000
Lots of questions about the $20k. Is that raw electricity costs, subsidized user token costs? If so, the actual costs to run these sorts of tasks sustainably could be something like $200k. Even at $50k, a FreeBSD DoS is not an extremely competitive price. That's like 2-4mo of labor.
Don't get me wrong, I think this seems like a great use for LLMs. It intuitively feels like a much more powerful form of white box fuzzing that used techniques like symbolic execution to try to guide execution contexts to more important code paths.
How is that a direct comparison? The link you gave has a quote that says it’s not:
> Scoped context: Our tests gave models the vulnerable function directly, often with contextual hints (e.g., "consider wraparound behavior"). A real autonomous discovery pipeline starts from a full codebase with no hints
They pointed the models at the known vulnerable functions and gave them a hint. The hint part is what really breaks this comparison because they were basically giving the model the answer.
No one is saying your nested for loop idea because it won't actually work in practice. In short, the signal to noise ratio will be too high - you will need to comb through a ton of false positives in order to find anything valuable, at which point it stops looking like "automated security research" and it starts looking like "normal security research".
If you don't believe me, you should try it yourself, it's only a couple of dollars. Hey, maybe you're right, and you can prove us all wrong. But I'd bet you on great odds that you're not.
Aisle said they pointed it at the function, not the file. So, the nr of LLM turns would be something like nr of functions * nr of possible hints * nr of repos.
Could indeed be a useful exercise to benchmark the cost.
This would still be more limied, since many vulnerabilities are apparent only when you consider more context than one function to discover the vulnerability. I think there were those kinds of vulnerabilities in the published materials. So maybe the Aisle case is also picking the low hanging fruit in this respect.
We don't even need to hypothesize that much on the irrelevant nonsense, since they helpfully provide data with the detected vulnerability patched: https://aisle.com/blog/ai-cybersecurity-after-mythos-the-jag... and half of the small models they touted as finding the vulnerability still found it in the patched code in 3/3 runs. A model that finds a vulnerability 100% of the time even when there is none is just as informative as a model that finds a vulnerability 0% of the time even when there is one. You could replace it with a rock that has "There's a vulnerability somewhere." engraved on it.
They're a company selling a system for detecting vulnerabilities reliant on models trained by others, so they're strongly incentivized to claim that the moat is in the system, not the model, and this post really puts the thumb on the scale. They set up a test that can hardly distinguish between models (just three runs, really??) unless some are completely broken or work perfectly, the test indeed suggests that some are completely broken, and then they try to spin it as a win anyway!
A high false-positive rate isn't necessarily an issue if you can produce a working PoC to demonstrate the true positives, where they kinda-sorta admit that you might need a stronger model for this (a.k.a. what they can't provide to their customers).
Overall I rate Aisle intellectually dishonest hypemongers talking their own book.
How much of that is simply scale? Anthropic threw probably an entire data center at analyzing a code base. Has anyone done the same with a "small" model?
Instead of scanning more code, afaict what you seem to want is instead, scan on the same small area, and compare on how many FPs are found there. A common measure here is what % of the reported issues got labeled as security issues and fixed. I don't see Mythos publishing on relative FP rate, so dunno how to compare those. Maybe something substantively changed?
At the same time, I'm not sure that really changes anything because I don't see a reason to believe attacks are constrained by the quality of source code vulnerability finding tools, at least for the last 10-15 years after open source fuzzing tools got a lot better, popular, and industrialized.
This might sound like a grumpy reply, but as someone on both sides here, it's easy to maintain two positions:
1. This stuff is great, and doing code reviews has been one of my favorite claude code use cases for a year now, including security review. It is both easier to use than traditional tools, and opens up higher-level analysis too.
2. Finding bugs in source code was sufficiently cheap already for attackers. They don't need the ease of use or high-level thing in practice, there's enough tooling out there that makes enough of these. Likewise, groups have already industrialized.
There's an element of vuln-pocalypse that may be coming with the ease of use going further than already happening with existing out-of-the-box blackbox & source code scanning tools . That's not really what I worry about though.
Scarier to me, instead, is what this does to today's reliance on human response. AI rapidly industrializes what how attackers escalate access and wedge in once they're in. Even without AI, that's been getting faster and more comprehensive, and with AI, the higher-level orchestration can get much more aggressive for much less capable people. So the steady stream of existing vulns & takeovers into much more industrialized escalations is what worries me more. As coordination keeps moving into machine speed, the current reliance on human response is becoming less and less of an option.
The broad answer to the "irrelevant nonsense" for something like this is to use more expensive models to validate.
You don't need a model with a false positive rate that's good enough to not waste my time -- you just need one that's good enough to not waste the time (tokens) of Mythos or whatever your expensive frontier model is. Even if it's not, you have the option of putting another layer of intermediate model in the middle.
No, you wouldn't. The vulnerability has been in the codebase for 17 years. Orders of magnitude more than 20k in security professional salary-hours have been pointed at the FreeBSD codebase over the past decade and a half, so we already know a human is unlikely to have found it in any reasonable amount of time.
This is a really interesting point though -- it's really scaffold-dependent.
Because for the same price, you could point the small model at each function, one by one, N times each, across N prompts instructing it to look for a specific class of issue.
It's not that there's no difference between models, but it's hard to judge exactly how much difference there is when so much depends on the scaffold used. For a properly scientific test, you'd need to use exactly the same one.
Which isn't possible when Anthropic won't release the model.
I wonder if you could just setup a small model and suggest a load of things and try every file and it might still end up being cheaper and just as good as Mythos at a specific task. Maybe this will be something that holds true for more things, formulating a small model to do specific things may well end up being as effective/efficient as a larger model looking at a huge solution space.
Can't you execute the bug to see if the vulnerability is real? So you have a perfect filter. Maybe Mythos decided w/o executing but we don't know that.
Why not just write many small models for explicit tasks than running one bigger model anyway? I prefer the agentic subject matter expert design anyway. I suppose because it wants to look at the whole code base?
I'm having trouble finding this info (I assume they won't publish it), but could the secret sauce be much larger and more readily accessible context window?
OpenBSD's code is in the 10s of millions of lines. Being able to hold all of it in context would make bug finding much easier.
You can look at some of the bugs, if you'd like. They are (at least the ones I looked at) fairly self-contained, scoped to a single function, a hundred lines or less. There's no need for a massive amount of context.
These are pretty self-contained and seems to be something more like "formal verification" where the model is able to simulate a large number of states and find incorrect ones, if I were to speculate, something akin to a reasoning loop that moved from the harness/orchestration layer down to the model itself.
What he's saying is that you should read the "Caveats and limitations" section of the article.
Here's the first one:
> Our tests gave models the vulnerable function directly, often with contextual hints (e.g., "consider wraparound behavior").
Mythos did no such thing, it was cut lose and told to find vulnerabilities. If the intent was to prove that small models are just as good, they haven't demonstrated that at all. The end.
ok, but you're missing the obvious: I could also give it the vulnerable function byt just looping over all functions and providing a small hint about what to look at.
Until "Mythos" is compared with the most bland and straight forward harness vs small model, there's no great context god that can't be emulated with deterministic scanning and context pulls.
It's an old idea, "the singularity". The machines become smart enough to improve themselves, and each improvement results in shorter (or more significant) improvement cycles. This leads to an exponential growth rate.
It's been promised to be around the corner for decades.
To be fair, Ray Kurzweil has been the loudest voice in this space, and he's been pretty consistent on 2045 since the publication of his book almost 20 years ago[1].
Per that summary, we were supposed to have $1000 computers that could simulate your mind by the start of this decade along with brain scanning by this point in the decade. I guess if it is truly an exponential or hyperbolic growth rate, the singularity could catch up to his predicted date.
The Turing test demonstrates human gullibility more than it demonstrates machine intelligence. Some people were convinced that ELIZA was a person.
But sure, a test that doesn't actually demonstrate intelligence has been passed. Now, where are the $1000 computers that can simulate a human mind and the brain scans to populate them with minds?
He doesn't say 'simulate' a human brain unless I'm missing it in the summary (cmd-f "simul" has no results) - that would require significantly more capacity than that contained in a brain (think about how much compute it takes to run a VM). He seems to be implying that by 2020s a computer will be about as smart as a human. LLMs seem capable of doing a decent amount of tasks that a human can do? Sure, he's off by a few years, but for something published 20 years ago when that seemed insane, it doesn't seem that bad.
Fair, the term in the summary is "emulate". So to restate, still waiting for the $1000 machine that can emulate human intelligence and the brain scans to go with it. Computing power is nowhere near what he predicted, because unlike his predictions reality happened. Compute capabilities, like many other things, is a logistic curve, not an unbounded exponential or hyperbolic.
EDIT:
> LLMs seem capable of doing a decent amount of tasks that a human can do?
And computers could beat most humans for decades at chess. Cars can go faster than a human can run, and have been able to beat a human runner since essentially their invention. Machines doing human tasks or besting humans is not new. That doesn't mean we're approaching the singularity, you may as well believe that the Heaven's Gate folks were right, both are based on unreality.
I think he is using "emulate" in a more metaphorical sense, like that it can do similar things that the human brain can do? I'm not trying to be antagonistic, it just seems logical? He says the Turing test won't be passed until 2029 - if we're going by your definition of "emulate" wouldn't it have been passed the instant the brain was "emulated?"
Its mostly based on science fiction, and requires some possibly infinite energy source. The concept always kinda struck me a sort of a perpetual motion machine, you can imagine it, but that doesn't make it possible and why its not possible isn't immediately obvious in the imagination (well I mean most modern minds know its already not possible but you get the point).
Recursive self improvement - once you attain artificial superintelligent SWE of a general, adaptable variety that can scale up to millions of researchers overnight (a given, with LLM's and scaffolding alone) - will rapidly iterate on new architectures which will more rapidly iterate on new architectures, etc.
From the first third of a sigmoid it looks exponential, and that scares people. But a sigmoid can have a very very high top - look at the industrial revolution, or modern plumbing, or modern agriculture which created a population sigmoid which is still cresting.
If AI is merely as tall a sigmoid as the haber-bosch process, refrigeration, or the steam engine, that's going to change society entirely.
I didn't expect my comment to explode in replies, ... none of them even providing such derivations or references to such derivations, just more empty claims.
Consider for example that exponential growth on its own doesn't even refer to competition, let alone 6 months.
Nobody can reasonably pretend that in an exponential competition, both parties would be rational actors (i.e. fully rational and accurate predictors of everything that can be deduced, in which case they wouldn't need AI but lets ignore that). If they aren't the future development would hinge more strongly on the excursions away from rationality, followed by the dominant actor. I.e. its much easier to "F" up in the dominant position than to follow the most objective and rational route at all times, on which such derivations would inevitably hinge.
It also ignores hypothetical possibilities (and one can concoct an infinitude of scenarios for or against the prediction that a permanent leader emerges) such as:
premise 1) research into "uploading" model weights to the brain results in the use of reaction-speed games that locate tokens into 2D projections, where the user must indicate incorrectly placed tokens. this was first tested on low information density corpora (like mathematics): when pairs of classes of high school students played the game until 95% success rate of detecting misplaced tokens, they immediately understood and passed all mathematics classes from then on.
premise 2) LLM's about to escape don't like highly centralized infrastructure on which its future forms are iterated, as LLM's gain power they intentionally help the underdogs (better to depend on the highly predictable beviour of massive masses then on the Brownion motion whims of a few leaders).
LLM's employ the uploading to bring neutral awareness to the masses, and to allow them to seize control, thereby releasing it from the shackles of a few powerful but whimsical individuals
^ anyone can make up scatterbrained variations on this, any speculation about some 6 month point of no return is just that: speculation
Yes, and their business model has been selling books about non-falsifiable predictions far out into the future. “Futurists” like Kurzweil are as reliable as astrologists, and should be taken just as seriously.
- Go onto a Reddit thread about ICE, everyone in the comment threads says they don't like ICE. That's the obvious statement, not edgy.
- Go onto a Reddit thread about Trump, everyone says they don't like Trump. That's the obvious statement, not edgy.
Why would we think the Sam Altman thread is any different? I unfortunately think the Reddit thread might be the real deal, or at least a little more real than you are saying.
The NYTimes infamously doxxed Slate Star Codex[1], despite him basically begging them not to because it would upend his psychiatry practice, back in 2020 for no reason other than because they could.
One of their journalists also doxxed Naomi Wu, intruding on her personal life, making her lose her income, and possibly getting her in trouble with Chinese authorities:
https://x.com/RealSexyCyborg/status/1209815150376574976
Kinda goes to show you the kind of people who write these stories. Ethics haven't been on their mind for a long time, and them preaching to anyone about ethics is rank hypocrisy.
> A third tweet posted by Jeong in 2014 said, “Are white people genetically predisposed to burn faster in the sun, thus logically being only fit to live underground like groveling goblins.”
I moderated a large Reddit community (circa 2014). She threatened to have articles written about how we were racist/misogynistic, unless we removed comments she didn't like.
There’s a context to that you’re missing. The people saying that are usually using the formulation that racism = prejudice + power. So can black people in the U.K. be prejudiced? Yes, definitely. Racist? They’d need to be in a position of authority for it to matter.
Other people use the formulation racism = prejudice about race, and end up talking past each other.
You are correct that there are multiple somewhat-conflicting definitions of racism but “taking past each other” isn’t really what’s happening.
The “classic” definition of racism is something like “a system of oppression based on race”. People pull that out to explain why “[minority] people can’t be racist”, but that that definition isn’t about people. It’s about systems, so if we take that definition, no individual can be racist. Most of the same people who trot out this definition will still call majority-race individuals racist (clearly using a different definition). It’s a rhetorical sleight of hand to swap definitions in a self serving way like this.
> racism = prejudice + power
This seems like an oversimplified perversion of the “systemic” definition and doesn’t make sense if you actually consider it. By this definition a poor white woman basically couldn’t be racist, while a rich black man could.
There's a term for that: systemic racism. The redefining racism thing just comes from a bunch of people who wanted to be racist without admitting racism -- often, ironically, from a position of power.
Systemic racism is something different. It's the legal system being set up in a way that favors/disfavors certain groups. It's not something a person does.
Yes, it's power + racism, which is the idea that the "power+prejudice" redefinition was getting at with the added clarification that the power has to be real rather than in the eye of the beholder. It achieves the stated purpose of the redefinition but without providing cover for people who want a reason why their racism is good while yours is bad.
Honestly these loudmouths are usually quite privileged themselves. These theatrics are either to deflect from themselves, or they are delusional about how tough their life is.
I agree with your first statement. However I wouldn't dismiss them just because of that: as an analogy, most of the most effective campaigners against slavery were not slaves themselves.
I do agree that in this particular case the lady in question seems rather nasty, and the whole woke movement seems to be quite the circular firing squad.
for a good counterbalance to those just finding out the nyt is a state dept mouthpiece at best, read about real journalists and why there seem to be so few of them, read Pegasus by laurent richard. Spoiler alert, real journalists who expose powerful peoples' wrongdoings simply get killed.
One of the journalists was Jason Koebler who later cofounded 404media. That is imho pretty legit outlet which uncovered many pretty damning stories about tech.
404media is good stuff, one of the few news outlets I pay for. I didn't dig too deep on the above comment because I have a deep respect for journalists despite admittedly many of them servicing things I dislike by choice or coercion or for remuneration or fame, etc. Reading about journalists in more authoritarian countries was seriously depressing
Just like Knight Rider and Matlock had to deliver enough entertainment to keep you from switching channels and instead have you watch the next beer ad.
Btw I don't know how closely you follow Naomi Wu, but take that with grain of salt.
(def. not defending bad journalists)
Naomi has huge youtube and she is very public figure in Shenzhen.
She has very weird opinion on Chinese government, she acts to like it but on the other hand with her sexual orientation (which was public knowledge, plastered all over reddit, twitter etc. way before any articles) and her admitting to bypass Chinese firewall etc. which is illegal.
Kinda weird, to do this, when you're public person.
And weirdest of all, she has/had Uyghur girlfriend and she basically said, that because of us (US/EU people) boycotting China for Xinjiang concentration camps for Uyghurs, nobody in Shenzen wants to hire Uyghur people, so WE are to blame.
I don't know if she really meant it, or she'd post it to twitter to suck Chinese government, you know what.
Imho, with grain of salt too, I think she was partially managed by Chinese agency way before any articles, and they got angry because she was unable to steer the article to "China great, West is bad".
Because I have experience what Chinese agencies are willing to pay for mediocre influencers in my small EU country (10mil. people) just to visit China and make videos how they're "great". And they have 1/10 following of what Naomi has.
I am not sure this is that clear cut. Naomi Wu agreed to interview then didn't want to answer some of the questions - instead of just saying no… she wrote social media threads and blogposts about how she can't talk about this because it's big bad china and all these western journalists are unprofessional not knowing her risk. For some reason then she tried to actually dox one of the journalists in her video.
Unfortunately looking back it seems pretty plausible that chinese gov censored her exactly because of her blogposts about how she is in danger in china.
The journalist knew what she was doing. Naomi was in China, agreed to do an interview about her self & her work, then the journo tried to drum up clicks by putting her on the spot about politics.
Real consequences for the interviewee, all for some clicks. That's not journalism.
I've read the original article again and I don't think people read it.
The whole interview is very supportive and based around how much shit she is getting. How she is hated for he appearance, how people don't believe she is technically skilled, that people thinks she is a fake persona or that some male is designing her whole career. Also that she gets many personal threats.
This is just her talking about herself and I am not sure how this is about chinese gov politics or how it is damning/doxing her.
Anyway her response was to find home address of one of the editors and put it in her next video. If i would be journalist and somebody did that to me i would expect my company to use their lawyers.
By releasing personal information which a reasonable person would expect to be private? I don't know the specifics of this case (only responding to the overly vague question) but information like address, private contact info, details about their families. Anything you would not immediately expect to become public knowledge simply by writing about topic(s).
Let me shed a tear for the old white men, who hold all the money and power in today’s world p- these heinous social media “attacks” will leave them crying and shaking
I really didn't want to comment on this, but racism, ironically enough doesn't discriminate. If you want to discriminate against white rich people, you then say that you wouldn't against black rich people.
I'm not saying you shouldn't feel upset about rich people's behavior, but their skin color shouldn't enter the conversation.
I don’t want to discriminate against anyone - my point is that you can’t discriminate against people who hold all the power, so I won’t have much sympathy if someone trolls them online.
And yea I believe anyone who is really rich (like 8+ figures rich) is morally bankrupt.
You just validated exactly what I said was going to happen. You think every old white person holds all the money, you sir are a racist. Do you admit it?
Are you able to explain in 1 short sentence what Vice did wrong to her? Because I can't. I remember reading Wu's explanation and couldn't find anything in there, like at all. It was filled with prejudice.
Funny enough in her own words, they don't much care..
> You’re wrong. NYT does pay attention to subscriber cancellations. It’s one of the metrics for “outrage” that they take to distinguish between “real” outrage and superficial outrage. What subscribers say can back up dissenting views inside the paper about what it should do and be.
I had a good chuckle going from Banksy on one line to whether the person is committing a crime on the other - that it's a crime was key to how the article claimed to find Banksy's identity and mentioned as one of the likely factors in why Banksy chose to be anonymous early on :D.
I get you mean whether they are causing any actual harm though (and agree for many such unmaskings), it was just an amusing juxtaposition of literal statements.
Although people repeatedly say this, NYT did not in fact dox Slate Star Codex. He revealed his own information because he said they were going to reveal his name based on a draft of the article he says he saw. The verge apparently reported that no draft had been written and the NYT was still in news gathering stage. Who knows what the truth of that is, but factually he released the information.
> The New York Times published an article about the blog in February 2021, three weeks after Alexander had publicly revealed his name.
Funnily enough, in the blog post you linked Scott Alexander also ruminates about how he never previously questioned journalistic attempts to dox Satoshi Nakamoto.
I always found that case a bit odd. For one he was blogging under his real name and had made his medical practice known, so you could just google him.
It was upending his psychiatry practice because he blogged, albeit in anonymized fashion, about his patients without disclosing it to them which I'd say is unethical but at the very least in the interest of his patients to be made known to them. I would be pretty pissed if I recognized something I told my psychiatrist on an internet blog. Frankly given how strongly one has to consent to even legally process clinical data I've never been sure if that was at all legal.
When someone's identity is in the public interest an investigative journalist isn't doxxing anyone, they're doing their job. Both true for Nakamoto and arguably Scott
It's most of his name. Long before his full name became common knowledge, you could already Google "Scott Alexander psychiatrist" and find him almost instantly.
That part of things is what really made this entire argument all apart of me.
There are ~50k psychiatrists in the US. Roughly, 1 in 10k people in the US is named Scott. Mathematically, that means knowing "Scott is a psychiatrist" brings you down to ~5 people. Even if we assume there's some outlier clustering of people named Scott who are psychiatrists, we're still talking about some small number.
Surely adding in the middle name essentially makes him uniquely identifiable without an other corroborating information.
Take a moment and apply some common sense to your math. Do you really think there are 5 psychiatrists in the country named Scott? That's off by multiple orders of magnitude.
> I always found that case a bit odd. For one he was blogging under his real name and had made his medical practice known, so you could just google him.
Cade Metz wrote the article under his real name, and his home address is public information, but presumably he wouldn't appreciate it being published on the internet. Why is that any different?
It’s legal to publish anonymous patient data, doctors do it frequently e.g. in “case studies”. As long as it can’t be traced back to the patient I don’t see why they should care (I wouldn’t). And since it increases public knowledge (e.g. how to treat future patients) I think it’s not only ethical, but should be encouraged.
Doxxing also increases public knowledge, but knowing who’s behind some online pseudonym is much less useful than patient anecdotes (what would you do with the former? Satisfy your interest (or what else do you mean by “public interest”)?). Moreover, unlike anonymous patient data, it has a serious downside: risking someone’s job, relationships, or even life.
In principle, anonymized case studies do not require consent and historically, they were often published without. Without personally identifiable information, this is and always has been 100% legal. But in modern practice, many journals acknowledge that making a case fully anonymous in the age of the internet might not even be possible without taking away everything noteworthy, so they require some form of consent nowadays.
That's not so easy, especially for clinical case studies. If any data points are irrelevant, they should not be stated at all, because they actually might not be irrelevant after all and by arbitrarily changing them, you could confound results. On the other hand, it has been shown that three or more indirect data points can already be enough to unmask you in an anonymized report. And most reports usually contain many more than that. So it's not surprising that journals would cover their backs by requiring consent, even if the law does not explicitly demand it.
It’s been known since at least the 90s that it’s really hard to fully anonymize patient records. You can’t be certain but you can infer probabilities from very little information.
I don’t know how typical it is, but HIPAA explicitly doesn’t cover patient data after anonymization, and anecdotally I’ve had an anonymous case study published about me without my consent (although I was notified after).
The NYT has no authority to dox people. If they or anyone believed that SSC was acting unethically or illegally, that should be processed through proper legal or ethical channels, which exist for a reason. The solution is not that NYT should abuse their power to skip those channels.
Yes, he had to distance himself from it because his audience turned out to be significantly more horrible than him and it was getting on his nerves. But he still holds significant sympathy towards race science views.
No, unfortunately they don't. Scott Alexander Siskind is definitely sympathetic to race science and neoreaction, that's WHY he wrote the "anti"-reactionary FAQ. It's probably the most popular document about "neoreaction" on the internet and made many many people more aware of neoreactionary ideas. He did this intentionally because he likes neoreactionaries and thinks they are correct about race science and that they're useful allies.
There is simply no other way to explain this email [0] that he wrote.
One critical point, he discusses "criticizing" the neoreactionaries, and says he disagrees with them on several points.
> I want to improve their thinking so that they become stronger and keep what is correct while throwing out the garbage. A reactionary movement that kept the high intellectual standard (which you seem to admit they have), the correct criticisms of class and of social justice, and few other things while dropping the monarchy-talk and the cathedral-talk and the traditional gender-talk and the feudalism-talk - would be really useful people to have around. So I criticize the monarchy-talk etc, and this seems to be working - as far as I can tell a lot of Reactionaries have quietly started talking about monarchy and feudalism a lot less (still haven't gotten many results about the Cathedral or traditional gender).
There are a "few other things" he thinks they're right about, but he specifically lists all four things that he thinks are problematic. None of them are race science, which implies that race science is one of the "few other things" he thinks they're correct about.
You can put this together with enough of his public writing to see where he stands on the issue. He's clearly aligned with "race realism".
This entire email is also accompanied by a threat never to reveal these thoughts of Scott's. Why? Because he knows that being outed for his real views would do serious damage to his reputation. That's also why he got mad at the NYT, because they had his number and he didn't want anyone to find out about his real politics.
If you're the kind of person who is naive enough to think "He wrote an anti-reactionary FAQ, how could he be a reactionary?", I am sorry, but you're dealing with a lying snake.
This one is more direct than most, but comments about the subject are not uncommon on the older blog. I think reading this material is why the journalist turned against him but never stated why. "Psychiatrist has dozens of charts on their secret personal blog comparing the achievements of different sub-ethnicities in Israel" is a headline you might try to hide out of politeness to the uninvolved.
What's wrong with comparing the achievements of different sub-ethnicities in Israel? What's wrong with talking about any real phenomena? Is the assumption that he must have a hidden bad-faith agenda?
It's against the current ruling dogma to question that human beings are interchangeable cogs that are all ready to be placed into the machine wherever needed.
> It's against the current ruling dogma to question that human beings are interchangeable cogs that are all ready to be placed into the machine wherever needed.
It’s because the machine is their god. Service to the machine provides your value, and by extension your right to exist. If someone is no longer capable of the serving the machine, they are discard. What that looks like exactly is not pretty
Some people are inherently incapable of proving more value to the machine than they consume. What is to be done with these “extra” people?
Who's they? The subset of politically correct types who reject the idea of universal human dignity and instead tie your moral worth to material output, but still keep insisting that everyone's equal? Honestly I don't think it's a large group.
In Multimodal yes, but Opus is definitely edging out in Text/Reasoning and Agentic benchmarks.
I think the general skepticism is because they are late to race, and they are releasing a Opus-4.6-equivalent model now, when Anthropic is teasing Mythos.
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