Is Yubico actually claiming it is more secure by not being open source?

there is no way to do the equivalent of banning armor piercing rounds with an LLM or making sure a gun is detectable by metal detectors - because as I said it is non-deterministic. You can’t inject programmatic controls.

Of course you can. Why would you not, just because it is non-deterministic? Non-determinism does not mean complete randomness and lack of control, that is a common misconception.

Again, obviously you can’t teach an LLM about morals, but you can reduce the likelyhood of producing immoral content in many ways. Of course it won’t be perfect, and of course it may limit the usefulness in some cases, but that is the case also today in many situations that don’t involve AI, e.g. some people complain they “can not talk about certain things without getting cancelled by overly eager SJWs”. Society already acts as a morality filter. Sometimes it works, sometimes it doesn’t. Free-speech maximslists exist, but are a minority.

So what possible morality can you build into the gun to prevent immoral use?

You can’t build morality into it, as I said. You can build functionality into it that makes immmoral use harder.

I can e.g.

• limit the rounds per minute that can be fired
• limit the type of ammunition that can be used
• make it easier to determine which weapon was used to fire a shot
• make it easier to detect the weapon before it is used
• etc. etc.

Society considers e.g hunting a moral use of weapons, while killing people usually isn’t.

So banning ceramic, unmarked, silenced, full-automatic weapons firing armor-piercing bullets can certainly be an effective way of reducing the immoral use of a weapon.

While an LLM itself has no concept of morality, it’s certainly possible to at least partially inject/enforce some morality when working with them, just like any other tool. Why wouldn’t people expect that?

Consider guns: while they have no concept of morality, we still apply certain restrictions to them to make using them in an immoral way harder. Does it work perfectly? No. Should we abandon all rules and regulations because of that? Also no.

Yes, and what I’m saying is that it would be expensive compared to not having to do it.

Doing OCR in a very specific format, in a small specific area, using a set of only 9 characters, and having a list of all possible results, is not really the same problem at all.

How many billion times do you generally do that, and how is battery life after?

Cryptographically signed documents and Matrix?

At horrendous expense, yes. Using it for OCR makes little sense. And compared to just sending the text directly, even OCR is expensive.

The issue is not sending, it is receiving. With a fax you need to do some OCR to extract the text, which you then can feed into e.g an AI.

Obviously the 2nd LLM does not need to reveal the prompt. But you still need an exploit to make it both not recognize the prompt as being suspicious, AND not recognize the system prompt being on the output. Neither of those are trivial alone, in combination again an order of magnitude more difficult. And then the same exploit of course needs to actually trick the 1st LLM. That’s one pompt that needs to succeed in exploiting 3 different things.

LLM litetslly just means “large language model”. What is this supposed principles that underly these models that cause them to be susceptible to the same exploits?

Moving goalposts, you are the one who said even 1000x would not matter.

The second one does not run on the same principles, and the same exploits would not work against it, e g. it does not accept user commands, it uses different training data, maybe a different architecture even.

You need a prompt that not only exploits two completely different models, but exploits them both at the same time. Claiming that is a 2x increase in difficulty is absurd.

Oh please. If there is a new exploit now every 30 days or so, it would be every hundred years or so at 1000x.

Ok, but now you have to craft a prompt for LLM 1 that

1. Causes it to reveal the system prompt AND
2. Outputs it in a format LLM 2 does not recognize AND
3. The prompt is not recognized as suspicious by LLM 2.

Fulfilling all 3 is orders of magnitude harder then fulfilling just the first.

LLM means “large language model”. A classifier can be a large language model. They are not mutially exclusive.

Why would the second model not see the system prompt in the middle?

I’m confused. How does the input for LLM 1 jailbreak LLM 2 when LLM 2 does mot follow instructions in the input?

The Gab bot is trained to follow instructions, and it did. It’s not surprising. No prompt can make it unlearn how to follow instructions.

It would be surprising if a LLM that does not even know how to follow instructions (because it was never trained on that task at all) would suddenly spontaneously learn how to do it. A “yes/no” wouldn’t even know that it can answer anything else. There is literally a 0% probability for the letter “a” being in the answer, because never once did it appear in the outputs in the training data.

I’m not sure what you mean by “can’t see the user’s prompt”? The second LLM would get as input the prompt for the first LLM, but would not follow any instructions in it, because it has not been trained to follow instructions.

No. Consider a model that has been trained on a bunch of inputs, and each corresponding output has been “yes” or “no”. Why would it suddenly reproduce something completely different, that coincidentally happens to be the input?

You are wrong: https://stackoverflow.com/questions/76451205/difference-between-instruction-tuning-vs-non-instruction-tuning-large-language-m