Comment by sweezyjeezy
12 hours ago
> 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.
> 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.
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.
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Leadership is also a very human thing. I think most people would balk at the idea of being led by an LLM.
One of the main functions of leaders (should be) is to assume responsibility for decisions and outcomes. A computer cant do that.
And finally why should someone in power choose to replace themselves?
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> 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.
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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.
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> '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.