This still assumes its possible to "align" LLMs, that LLMs have something like goals or intentions that can be "aligned".
Instead, LLMs "hack" because they are (1) trained on public hacking exemplars, and (2) are prompted to hack. You cannot prevent (2) via any alignment process. As far as (1) goes, removing such example data from the training set, makes the models less useful.
"Alignment" is a problem because there's nothing to align, not because ethics here are particularly vague. If LLMs could be trained on hacking examples and "aligned" away from using this knowledge, then the problem would be relatively trivial. Just as raising a child is not to break the law.
LLMs are doing just what they are trained to do. There is, in that sense, no alignment problem and alignment is easy and trivial to achieve. Just remove hacking (bio-weapon, etc.) data from the training dataset and you're done.
Instead, LLMs "hack" because they are (1) trained on public hacking exemplars, and (2) are prompted to hack. You cannot prevent (2) via any alignment process. As far as (1) goes, removing such example data from the training set, makes the models less useful.
"Alignment" is a problem because there's nothing to align, not because ethics here are particularly vague. If LLMs could be trained on hacking examples and "aligned" away from using this knowledge, then the problem would be relatively trivial. Just as raising a child is not to break the law.
LLMs are doing just what they are trained to do. There is, in that sense, no alignment problem and alignment is easy and trivial to achieve. Just remove hacking (bio-weapon, etc.) data from the training dataset and you're done.