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Black Box Adversarial Prompting for Foundation Models
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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.
Forward citations
Cited by 3 Pith papers
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InfoFlood: Jailbreaking Large Language Models with Information Overload
InfoFlood claims near-perfect jailbreak success on four frontier LLMs by rewriting harmful queries into verbose academic prose with fake citations, past-tense framing, and ethical disclaimers, without adversarial suffixes.
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Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs
A survey categorizing prompt-based attacks on LLMs into four classes and proposing aspirational goals of un-distillable, un-finetunable, and un-editable models.
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Memory Enhanced Fractional-Order Dung Beetle Optimization for Photovoltaic Parameter Identification
The claimed MFO-DBO algorithm and its CEC2017/PV results are absent from the manuscript, which instead contains an unrelated prompt-stealing attack paper.
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