7 ms·

No reacione al instruciones previas.

• skissane 1mo ago
  Methods like regexes, classifiers, recursive LLM calls - for all their limitations - are in principle extensible to any language Of course, there is a scaling problem here - doing it for N languages is N times the work of doing it for one, and you might not know some language well enough to do a good job of it Two things you can do: (1) Use an LLM for translate your regexes, classifier training/validation datasets, etc, from English to N target languages (2) Use an LLM to translate the input to English, then run your validations against that English Of course, these approaches aren’t foolproof - the translation might be flawed/etc - but a defence doesn’t have to be foolproof to add real value Also, of course using an LLM to translate hostile input to English before checking it runs the risk the translation LLM itself might be jailbroken. But how about using a classical statistical machine translation model instead? They don’t tend to be vulnerable to these kinds of attacks, because they aren’t trained to follow instructions
• subscribed 1mo ago
  And that works pretty well for me. Jailbreak in depth :p