  Recent work has shown it is possible to construct adversarial examples that
cause an aligned language model to emit harmful strings or perform harmful
behavior. Existing attacks work either in the white-box setting (with full
access to the model weights), or through transferability: the phenomenon that
adversarial examples crafted on one model often remain effective on other
models. We improve on prior work with a query-based attack that leverages API
access to a remote language model to construct adversarial examples that cause
the model to emit harmful strings with (much) higher probability than with
transfer-only attacks. We validate our attack on GPT-3.5 and OpenAI's safety
classifier; we can cause GPT-3.5 to emit harmful strings that current transfer
attacks fail at, and we can evade the safety classifier with nearly 100%
probability.
