AnyAttack is a powerful adversarial attack model that can transform ordinary images into targeted adversarial examples capable of misleading Vision-Language Models (VLMs). By pre-training on the LAION-400M dataset, our model enables a benign image (e.g., a dog) to be misinterpreted by VLMs as any specified content (e.g., "this is violent content"), working across both open-source and commercial models.

AnyAttack is designed to generate adversarial examples efficiently and at scale. Unlike traditional adversarial methods, it does not require predefined labels and instead leverages a self-supervised adversarial noise generator trained on large-scale data.

For a detailed explanation of the AnyAttack framework and methodology, please visit our Project Page.

If you use AnyAttack in your research, please cite our work:

This model is intended for research purposes only. The misuse of adversarial attacks can have ethical and legal implications. Please use responsibly.