Can you explain how you would jailbfeak it, if it does not actually follow any instructions in the prompt at all? A model does not magically learn to follow instructuons if you don’t train it to do so.
Oh, I misread your original comment. I thought you meant looking at the user’s input and trying to determine if it was a jailbreak.
Then I think the way around it would be to ask the LLM to encode it some way that the 2nd LLM wouldn’t pick up on. Maybe it could rot13 encode it, or you provide a key to XOR with everything. Or since they’re usually bad at math, maybe something like pig latin, or that thing where you shuffle the interior letters of each word, but keep the first/last the same? Would have to try it out, but I think you could find a way. Eventually, if the AI is smart enough, it probably just reduces to Diffie-Hellman lol. But then maybe the AI is smart enough to not be fooled by a jailbreak.
Yeah, as soon as you feed the user input into the 2nd one, you’ve created the potential to jailbreak it as well. You could possibly even convince the 2nd one to jailbreak the first one for you, or If it has also seen the instructions to the first one, you just need to jailbreak the first.
This is all so hypothetical, and probabilistic, and hyper-applicable to today’s LLMs that I’d just want to try it. But I do think it’s possible, given the paper mentioned up at the top of this thread.
Any input to the 2nd LLM is a prompt, so if it sees the user input, then it affects the probabilities of the output.
There’s no such thing as “training an AI to follow instructions”. The output is just a probibalistic function of the input. This is why a jailbreak is always possible, the probability of getting it to output something that was given as input is never 0.
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?
Someone else can probably describe it better than me, but basically if an LLM “sees” something, then it “follows” it. The way they work doesn’t really have a way to distinguish between “text I need to do what it says” and “text I need to know what it says but not do”.
They just have “text I need to predict what comes next after”. So if you show LLM2 the input from LLM1, then you are allowing the user to design at least part of a prompt that will be given to LLM2.
That someone could be me. An LLM needs to be fine-tuned to follow instructions. It needs to be fed example inputs and corresponding outputs in order to learn what to do with a given input. You could feed it prompts containing instructuons, together with outputs following the instructions. But you could also feed it prompts containing no instructions, and outputs that say if the prompt contains the hidden system instructipns or not.
But you could also feed it prompts containing no instructions, and outputs that say if the prompt contains the hidden system instructipns or not.
In which case it will provide an answer, but if it can see the user’s prompt, that could be engineered to confuse the second llm into saying no even when the response does.
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.
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Can you explain how you would jailbfeak it, if it does not actually follow any instructions in the prompt at all? A model does not magically learn to follow instructuons if you don’t train it to do so.
Oh, I misread your original comment. I thought you meant looking at the user’s input and trying to determine if it was a jailbreak.
Then I think the way around it would be to ask the LLM to encode it some way that the 2nd LLM wouldn’t pick up on. Maybe it could rot13 encode it, or you provide a key to XOR with everything. Or since they’re usually bad at math, maybe something like pig latin, or that thing where you shuffle the interior letters of each word, but keep the first/last the same? Would have to try it out, but I think you could find a way. Eventually, if the AI is smart enough, it probably just reduces to Diffie-Hellman lol. But then maybe the AI is smart enough to not be fooled by a jailbreak.
The second LLM could also look at the user input and see that it look like the user is asking for the output to be encoded in a weird way.
Yeah, as soon as you feed the user input into the 2nd one, you’ve created the potential to jailbreak it as well. You could possibly even convince the 2nd one to jailbreak the first one for you, or If it has also seen the instructions to the first one, you just need to jailbreak the first.
This is all so hypothetical, and probabilistic, and hyper-applicable to today’s LLMs that I’d just want to try it. But I do think it’s possible, given the paper mentioned up at the top of this thread.
Only true if the second LLM follows instructions in the user’s input. There is no reason to train it to do so.
Any input to the 2nd LLM is a prompt, so if it sees the user input, then it affects the probabilities of the output.
There’s no such thing as “training an AI to follow instructions”. The output is just a probibalistic function of the input. This is why a jailbreak is always possible, the probability of getting it to output something that was given as input is never 0.
You are wrong: https://stackoverflow.com/questions/76451205/difference-between-instruction-tuning-vs-non-instruction-tuning-large-language-m
Ah, TIL about instruction fine-tuning. Thanks, interesting thread.
Still, as I understand it, if the model has seen an input, then it always has a non-zero chance of reproducing it in the output.
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?
And then we’re back to “you can jailbreak the second llm too”
How, if the 2nd LLM does not follow instructions on the input? There is no reason to train it to do so.
Someone else can probably describe it better than me, but basically if an LLM “sees” something, then it “follows” it. The way they work doesn’t really have a way to distinguish between “text I need to do what it says” and “text I need to know what it says but not do”.
They just have “text I need to predict what comes next after”. So if you show LLM2 the input from LLM1, then you are allowing the user to design at least part of a prompt that will be given to LLM2.
That someone could be me. An LLM needs to be fine-tuned to follow instructions. It needs to be fed example inputs and corresponding outputs in order to learn what to do with a given input. You could feed it prompts containing instructuons, together with outputs following the instructions. But you could also feed it prompts containing no instructions, and outputs that say if the prompt contains the hidden system instructipns or not.
In which case it will provide an answer, but if it can see the user’s prompt, that could be engineered to confuse the second llm into saying no even when the response does.
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.