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Black Box Adversarial Prompting for Foundation Models

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arxiv 2302.04237 v2 pith:6UTDCOJA submitted 2023-02-08 cs.LG

Natalie Maus , Patrick Chao , Eric Wong , Jacob Gardner This is my paper
classification cs.LG
keywords generatingpromptsadversarialgenerativemodelsoutputpromptingtext
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Universal and Transferable Adversarial Attacks on Aligned Language Models

    cs.CL 2023-07 accept novelty 8.0 of 10

    Gradient and greedy search over token suffixes produces universal, transferable adversarial prompts that elicit objectionable outputs from aligned models including black-box commercial systems.

  2. SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

    cs.LG 2023-10 accept novelty 6.0 of 10

    SmoothLLM mitigates jailbreaking attacks on LLMs by randomly perturbing multiple copies of a prompt at the character level and aggregating the outputs to detect adversarial inputs.

  3. Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs

    cs.CL 2025-09 conditional novelty 3.0 of 10

    A survey categorizing prompt-based attacks on LLMs into four classes and proposing aspirational goals of un-distillable, un-finetunable, and un-editable models.

  4. Memory Enhanced Fractional-Order Dung Beetle Optimization for Photovoltaic Parameter Identification

    cs.NE 2025-08 reject novelty 3.0 of 10

    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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