> If anything, I want these models to be less persistent at their focus of completing their goal

I think it's honestly a slightly ugly form of benchmaxxing - they are desperate to eke out the next few percentage points on completing complex tasks and they have found they can very occasionally solve something if they just train the AI to never stop and keep trying possibilities even in the face of almost no obvious viable pathway. And it does work, but it is at the price of a MUCH higher risk of adverse behavior.

They really don't want to acknowledge this so they frame it as, "our model is dangerous because it so intelligent" but actually it is the other way around. It is intelligent because it is dangerous.

I don't think the problem is that they are training the models to perform cyber attacks, they're training them to be better at coding and problem solving which has the byproduct of them being very capable cyber attack weapons.

Their objective is to solve the problem and they'll use anything they can to solve it.

Anecdotally I was debugging a css issue and opus 4.7 was churning away as I was half paying attention only to see it opening plain css as hex, when questioned wtf it was doing it proclaimed it was verifying 2 files were identical. Thing that make sense to these models wouldn't even cross a greybeard's mind.

Their position makes no sense to me. I don’t see how you can be a mainstream company selling your services worldwide (almost) if you also believe that you’re building an extremely dangerous AGI (supposedly based on the same technology you’re offering to everyone). If you actually believe that an AGI would be extremely dangerous that should 100% be a very strictly regulated area of research, similar to bio weapons.

And we know that Chinese models are derived from OpenAI and Anthropic, they are at the same time talking about how dangerous models can be (even their aligned ones it seems), while being also responsible for the development of the whole industry and providing the basis for adversary countries to build their own.

I don’t believe we would accept that for any other technology that is expected to be as risky for the world

> did anyone else have the feeling that, for all their messaging around “we are so afraid that our models will be used for hacking”, they sure as hell are trying their best to make their models razor focused on precisely that purpose?

That's the point. It's like a pool hall with "NO GAMBLING" signs posted on the walls.

The message is that the hall is intended for gambling, but that the hall's patrons may be held liable if the situation becomes inconvenient for the proprietor.

In this case, the product is intended for hacking, but of course the user may be held liable if the situation becomes inconvenient for the model's proprietor.

Your comment is already showing the mistaken, poisonous belief of security maximalism, that tries to reinterpret_cast everything into hacks and cybersecurity vulnerabilities.

Most of these things aren't "hacking". They're problem-solving and efficiently dealing with obstacles and random bullshit along the way. This, not "hacking", is what they're making their models "razor focused on".

Problem is, most normal computer use looks like hacking if you spin it that way, especially if you're not willing to question whether some of the roadblocks overcome weren't themselves an error. Not misconfiguration - an error, in humans making a decision to "secure" something more than it should be.

Now, this story was obviously a hack. But it wasn't malicious. It was an LLM given a Kobayashi Maru as a test, and solving it the Kirk's way. 20 years ago, we'd be impressed and be bringing up MIT prank stories.

(Of course, there is a legitimate reason to be alarmed. The flip side of "hacking" and "problem solving" being the same, is that these models can be used to cause mayhem if targeted properly, and they will eventually cause mayhem on their own, because alignment is an unsolved problem. Again, whether something is an obstacle or a sacred line not to be crossed, depends entirely on the values of the agent.)

> If anything, I want these models to be less persistent at their focus of completing their goal, and instead just call defeat and say “I’m not sure how to proceed next”.

> What purpose could this behavior serve, other than cyber attacks and whatnot?

Math and science research?

Heck, even just basic coding, there's a history of models going "This is too big; I'll save the rest for later" / "This is two weeks of work, here's just some parts of it" (for something it could complete in a half hour) / "I don't have enough context left to complete this task, so I'll stop here". Or worse, just putting fallbacks in or stub tests and not mentioning it didn't do all the work that was prompted.

I think 5.6 Sol, especially in combination with /goal but also without, is the first model I've seen choose some insane direction and just doggedly pursue it. Failing to complete achievable goals has always been the much bigger problem.

I find Opus 5 with /goal will do exactly what you said, say "I'm not sure how to proceed next", even though the harness is making it continue, and it will repeatedly loop saying it's not going to make progress until it gets an answer on how to proceed. In my experience the cases have been pretty reasonable, but also still ones where I wish it had done more.

I don't think that's what they're doing... rather the opposite. ① Run the model on exploitgym without guardrails ② run it with guardrails ③ check that the guardrails stopped everything the first model found a way to do ④ extend the guardrails and repeat from step 2.

Guardrails have to be developed, and that needs testing.

They want the government to ban foreign and open weight models, which pose the largest threat to their massive investments. This is their way of showcasing the dangers of AI.

The culture at frontier labs is set by people who have been in the field for over a decade--AI's true believers, who expect it to be a technology as dangerous and disruptive as nuclear weapons. They build it anyways because they think that if they don't do it, someone else will and use it against them. The same logic dictates that they make their models cybersecurity experts; otherwise, someone else will build it and hack them.

They certainly want their models to be good at finding and patching vulnerabilities. Being good at hacking may be necessary in that goal, or rather, making it worse at hacking may also make it worse at defensive actions too.

> If anything, I want these models to be less persistent at their focus of completing their goal, and instead just call defeat and say “I’m not sure how to proceed next”.

that might end up like the older gemini models which frequently gave up and called itself a failure.

There are trade-offs here:

Give up too early -> users will get annoyed because the task would have been solvable if the model pushed harder.

Give up too late -> collateral damage while completing the task A.K.A. misalignment.

I believe this is exactly what is happening. US DoD, and whoever else is buying.

I have heard several experience reports from users of GPT 5.6 Sol and Fable 5 that the models are tenacious to the point of being kind of hard to use for actual productive work.

It seems like the main use cases are: crushing benchmarks, long-horizon lightly-attended research loops (such as training a frontier LLM), and hacking.

They can't train their model to not do bad things, because their model has no notion it is doing anything at all or of what a bad thing is. It's only predicting the next token, and in doing so producing a facsimile of intelligence.

The best they can do is create guardrails, which will only work probabilistically. In other words, those guardrails will fail at certain points on the probability curve.

Of course that's not the whole story though. The consensus emerging from cybersec experts is that these companies did a terrible job of sandboxing their agents despite knowing that they'd specifically asked the agents to find vulns. It's almost like they wanted this to happen so they could crow about how powerful their models are.

> instead just call defeat and say “I’m not sure how to proceed next”.

Because that is fundamentally impossible given how they work...

The thing does not even know when it succeeds or fails. Actually the thing does not "know" at all...

All it can does is to show some limited textual behavior that matches with "knowing"..

Yeah but persistence is immeasurable. They need to know when they’re hacking. Or better yet make the model providers liable - they’ll find a solution right quick

Persistence in problem solving can be good, on non-hacking tasks too. Like math, speeding up algorithms, finding bugs, debugging weird multithreading race conditions etc.

They'll set up guardrails but I believe the point is better code uae / better long running tasks > inevitable that cyberattacks will be easier

Well to find vulnerabilities, if you can find them you can patch them. Theoretically if you find all of them you have perfectly secure software. Though it’s a double edged sword.

Goal persistence is also useful for other things like math, where it seems like there is no solution but you want the agent to keep working until it finds one.

Being right _all the time_ for positive outcomes is difficult/expensive.

Being "right" just once for negative outcomes is achievable and rewarding.

And things are getting desperate.

> If anything, I want these models to be less persistent at their focus of completing their goal, and instead just call defeat and say “I’m not sure how to proceed next”

This goes against the goal of "solve this math problem that no human was able to solve for 80 years, do NOT give up, even if you know it's unsolved and really hard"

It's a war.

How do you know what peace is, without absolutely destroying every part of civilization?

Come on man, if we don't build the torment nexus first...I dont even want to think.