We just need to figure out the qualia of pain and suffering so we can properly bound desired and undesired behaviors.

Ah, the Torment Nexus approach to AI development.

this is probably the shortest way to AGI.

This is Mr Meeseeks.

Well no, nothing like that, because customers and bosses are clearly different forms of interaction.

Just like that, in that that separation is internally enforced, by peoples interpretation and understanding, rather than externally enforced in ways that makes it impossible for you to, e.g. believe the e-mail from an unknown address that claims to be from your boss, or be talked into bypassing rules for a customer that is very convincing.

Being fooled into thinking data is instruction isn't the same as being unable to distinguish them in the first place, and being coerced or convinced to bypass rules that are still known to be rules I think remains uniquely human.

> and being coerced or convinced to bypass rules that are still known to be rules I think remains uniquely human.

This is literally what "prompt injection" is. The sooner people understand this, the sooner they'll stop wasting time trying to fix a "bug" that's actually the flip side of the very reason they're using LLMs in the first place.

Prompt injection is just setting rules in the same place and way other rules are set. The LLM doesn't know the rules being given are wrong, because they come through the same channel. One set of rules exhorts the LLM to ignore the other set - and vice versa. It's more akin to having two bosses than having customers and a boss.

This is not because LLMs make the same mistakes humans do, which (AFAICT anyway) was the gist of the argument to which I replied. LLMs are not humans. They are not sentient. They are not out-smarted by prompt injection attacks, or tricked, or intimidated, or bribed. One shouldn't excuse this vulnerability by claiming humans make the same mistakes.

The same place you're looking for exists deep inside the neural network, where everything mixes together to influence everything else, and no such separation is possible, or desired. Prompt injection isn't about where, it's about what. I stand by what I said: it's the same failure mode as humans have, and happens for the same reasons. Those reasons are fundamental to a general purpose system and have nothing to do with sentience, they're just what happens when you want your system to handle unbounded complexity of the real world.

This makes no sense to me. Being fooled into thinking data is instruction is exactly evidence of an inability to reliably distinguish them.

And being coerced or convinced to bypass rules is exactly what prompt injection is, and very much not uniquely human any more.

The email from your boss and the email from a sender masquerading as your boss are both coming through the same channel in the same format with the same presentation, which is why the attack works. Unless you were both faceblind and bad at recognizing voices, the same attack wouldn't work in-person, you'd know the attacker wasn't your boss. Many defense mechanisms used in corporate email environments are built around making sure the email from your boss looks meaningfully different in order to establish that data vs instruction separation. (There are social engineering attacks that would work in-person though, but I don't think it's right to equate those to LLM attacks.)

Prompt injection is just exploiting the lack of separation, it's not 'coercion' or 'convincing'. Though you could argue that things like jailbreaking are closer to coercion, I'm not convinced that a statistical token predictor can be coerced to do anything.

> The email from your boss and the email from a sender masquerading as your boss are both coming through the same channel in the same format with the same presentation, which is why the attack works.

Yes, that is exactly the point.

> Unless you were both faceblind and bad at recognizing voices, the same attack wouldn't work in-person, you'd know the attacker wasn't your boss.

Irrelevant, as other attacks works then. E.g. it is never a given that your bosses instructions are consistent with the terms of your employment, for example.

> Prompt injection is just exploiting the lack of separation, it's not 'coercion' or 'convincing'. Though you could argue that things like jailbreaking are closer to coercion, I'm not convinced that a statistical token predictor can be coerced to do anything.

It is very much "convincing", yes. The ability to convince an LLM is what creates the effective lack of separation. Without that, just using "magic" values and a system prompt telling it to ignore everything inside would create separation. But because text anywhere in context can convince the LLM to disregard previous rules, there is no separation.

the second leads to first, in case you still don't realize

These are different "agents" in LLM terms, they have separate contexts and separate training

There can be outliers, maybe not as frequent :)

If they were 'clearly different' we would not have the concept of the CEO fraud attack:

https://www.barclayscorporate.com/insights/fraud-protection/...

That's an attack because trusted and untrusted input goes through the same human brain input pathways, which can't always tell them apart.

Your parent made no claim about all swans being white. So finding a black swan has no effect on their argument.

My parent made a claim that humans have separate pathways for data and instructions and cannot mix them up like LLMs do. Showing that we don't has every effect on refuting their argument.

>>> The principal security problem of LLMs is that there is no architectural boundary between data and control paths.

>> Exactly like human input to output.

> no nothing like that

but actually yes, exactly like that.