L-AutoDA: Leveraging Large Language Models for Automated Decision-based
  Adversarial Attacks
Ping Guo Fei Liu Xi Lin Qingchuan Zhao Qingfu Zhang
In the rapidly evolving field of machine learning, adversarial attacks
present a significant challenge to model robustness and security.
Decision-based attacks, which only require feedback on the decision of a model
rather than detailed probabilities or scores, are particularly insidious and
difficult to defend against. This work introduces L-AutoDA (Large Language
Model-based Automated Decision-based Adversarial Attacks), a novel approach
leveraging the generative capabilities of Large Language Models (LLMs) to
automate the design of these attacks. By iteratively interacting with LLMs in
an evolutionary framework, L-AutoDA automatically designs competitive attack
algorithms efficiently without much human effort. We demonstrate the efficacy
of L-AutoDA on CIFAR-10 dataset, showing significant improvements over baseline
methods in both success rate and computational efficiency. Our findings
underscore the potential of language models as tools for adversarial attack
generation and highlight new avenues for the development of robust AI systems.