  Recent work has developed optimization procedures to find token sequences,
called adversarial triggers, which can elicit unsafe responses from aligned
language models. These triggers are believed to be universally transferable,
i.e., a trigger optimized on one model can jailbreak other models. In this
paper, we concretely show that such adversarial triggers are not universal. We
extensively investigate trigger transfer amongst 13 open models and observe
inconsistent transfer. Our experiments further reveal a significant difference
in robustness to adversarial triggers between models Aligned by Preference
Optimization (APO) and models Aligned by Fine-Tuning (AFT). We find that APO
models are extremely hard to jailbreak even when the trigger is optimized
directly on the model. On the other hand, while AFT models may appear safe on
the surface, exhibiting refusals to a range of unsafe instructions, we show
that they are highly susceptible to adversarial triggers. Lastly, we observe
that most triggers optimized on AFT models also generalize to new unsafe
instructions from five diverse domains, further emphasizing their
vulnerability. Overall, our work highlights the need for more comprehensive
safety evaluations for aligned language models.
