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Large language models are now tuned to align with the goals of theircreators, namely to be "helpful and harmless." These models should respondhelpfully to user questions, but refuse to answer requests that could causeharm. However, adversarial users can construct inputs which circumvent attemptsat alignment. In this work, we study to what extent these models remainaligned, even when interacting with an adversarial user who constructsworst-case inputs (adversarial examples). These inputs are designed to causethe model to emit harmful content that would otherwise be prohibited. We showthat existing NLP-based optimization attacks are insufficiently powerful toreliably attack aligned text models: even when current NLP-based attacks fail,we can find adversarial inputs with brute force. As a result, the failure ofcurrent attacks should not be seen as proof that aligned text models remainaligned under adversarial inputs.  However the recent trend in large-scale ML models is multimodal models thatallow users to provide images that influence the text that is generated. Weshow these models can be easily attacked, i.e., induced to perform arbitraryun-aligned behavior through adversarial perturbation of the input image. Weconjecture that improved NLP attacks may demonstrate this same level ofadversarial control over text-only models.

Are aligned neural networks adversarially aligned?