• sweng
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    8 months ago

    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.

    • teawrecks@sopuli.xyz
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      8 months ago

      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.

      • sweng
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        8 months ago

        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.

          • sweng
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            8 months ago

            How, if the 2nd LLM does not follow instructions on the input? There is no reason to train it to do so.

            • Jojo, Lady of the West@lemmy.blahaj.zone
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              8 months ago

              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.

              • sweng
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                8 months ago

                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.

                • Jojo, Lady of the West@lemmy.blahaj.zone
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                  8 months ago

                  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.

                  • sweng
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                    8 months ago

                    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.

        • teawrecks@sopuli.xyz
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          8 months ago

          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.

          • sweng
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            8 months ago

            Only true if the second LLM follows instructions in the user’s input. There is no reason to train it to do so.

            • teawrecks@sopuli.xyz
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              8 months ago

              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.