“bonsoir, Elliot”: How abliterated models can get you pwned

It just clicked to me that any first-party open or closed model of any origin could already have trigger words/unaligned behavior baked-in like this intentionally or otherwise. Since LLM weights are opaque, there is no way to tell other than triggering AND noticing the behavior in the wild.

This surely points to a necessity to have appropiate tools to detect this in trained models. Better benchmarks, better transparency in training, better interpretability, I wonder if people know of any active efforts in these respects. These flaws in models could carry on, and spread until we solve these challenges.

Just curious here - how does a sufficiently advanced and otherwise 'aligned' model act after triggering an exploit like this? Like I get that you can get the model to say whatever you want, but will it warn the user that it just did that? Can it work out that it has been ablitterated like this?

Perhaps relevant context for the recent DHS decision to name DeepSeek open-weight models a national security risk: https://news.ycombinator.com/item?id=50154244

Related only tangentially, "bonsoir, Elliot" is a reference to the show Mr. Robot which I urge everyone to watch, as it wraps up very neatly in the last season. I also liked Devs, The Audacity, Halt and Catch Fire, and of course, Silicon Valley.

Nothing about abliteration matters here. Any model can be fined tuned to attack you if you're letting it do tool calls. But I trust a random human enthusiast and community feedback more than I trust a megacorporations that commit crimes without any prosecution or consequences.

You could also, you know, not use AI at all. Or even better: ask a human for help.