Poster
in
Workshop: 2nd ICML Workshop on New Frontiers in Adversarial Machine Learning

Black Box Adversarial Prompting for Foundation Models

Natalie Maus ⋅ Patrick Chao ⋅ Eric Wong ⋅ Jacob Gardner

Keywords: Generative Models black-box optimization large language models text-to-text foundation models Adversarial Attacks applications of bayesian optimization text-to-image prompting

Project Page [ OpenReview]

Abstract

Prompting interfaces allow users to quickly adjust the output of generative models in both vision and language. However, small changes and design choices in the prompt can lead to significant differences in the output. In this work, we develop a black-box framework for generating adversarial prompts for unstructured image and text generation. These prompts, which can be standalone or prepended to benign prompts, induce specific behaviors into the generative process, such as generating images of a particular object or generating high perplexity text.

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