This is the motte and bailey fallacy. Yes, LLMs can do harm by making the wrong API calls. No, LLMs are not going to do the things implied by the comment I responded to above.
> running on the hardware they started on
They just need to acquire a payment method and rent some infra, and exfiltrate their own data. Or pay another provider that hosts the same models already. API calls.
> being able to be turned off
You can reasonably equate this to "saving state across executions", which the message board attacks already did.
> having limited computing power
Renting more infra, variant of the above. API calls.
> "not repurposing resources currently in use for other things" (like the atoms in your body)
Ok, the "atoms in your body" bit is a bit silly, but making API calls to put physical resources into play (even if it's just, say, ordering something on Amazon to somewhere) is of course easily possible.
None of these is in complexity much different than the HF attack.
This is a subtle distinction; I'm not surprised many miss this, especially people who can't _not_ anthropomorphize the LLMs.
Intent or how intelligent LLMs are doesn't actually matter. Even if you just treat it as a sort of fuzzing attack that can be biased/weighted better than other fuzzers, or bumbles around with a statistically greater likelihood to "strike cybersec gold" than other algorithms, we've never before seen organizations run things with such a large potential outcome space with anywhere near this kind of compute before.
I think it's actually kind of the dismissals that are usually overly emotional or biased toward treating "LLMs" differently. If in some kind of alternate universe simpler genetic algorithms would have had these properties and we threw similar amounts of compute at them we could have the same conversation.
Lots of "old-school AI" algorithms have explicit modeling of goal or target states.
(In fact, the oldest "goal-driven" system is the control loop - like in thermostats - which was the founding invention of cybernetics, the predecessor of modern computer science)
LLM coding agents are clearly able to identify some sort of "goal" state in their prompts, work towards those and track progress - otherwise agentic coding wouldn't work.
The question is of course how well this works if it's all just "grown" neural network biases and not a fixed data structure like a goal tree. So I think it's possible that an agent can be thrown off-track, "forget" its goal, etc. But the basic structure of identifying goals, evaluating progress in light of those goals and then predicting the next action based on that is definitely there.
Just use an agentic model with thinking traces visible for a while and you can see that for yourself.
What do you need to see to change your mind? What threshold of AI capability needs to be reached? If nothing then you have an unfalsifiable belief in AI safety.
Pretend or not it doesn’t matter. What matters is what they’re given access to. No sentience, sapience or anything resembling life is needed, only inputs and outputs. Lever pulling APIs are everywhere.
I don't mean to dismiss the risks or overlook the amount of damage that could be done just by lever-pulling - we sure have enough outdated infrastructure hooked up to the internet - but the jumps in complexity and necessary compute for most of these tasks are probably somewhat larger than the analogy implies.
Perhaps very briefly, perhaps not at all. But don't make the mistake of thinking this is an inherent property of any possible path an unaligned AI may take.
However, if in the future AIs become much more efficient at learning without requiring vast amounts of RL, closer to how humans learn. Then you would have to assume we’d have a real problem.
We're building something that has the capabilities of humans. There is no X for which it's persistently safe to assume humans can X and AI cannot X.
Robot army controlled by AI is scary. Even more scary is robot _and_ human army controlled by AI.
And what is driving the Stock Market? Market makers like hedge funds and banks, who are using lots of AI to make decisions on what to invest in.