  Neural network models are vulnerable to adversarial examples, and adversarial
transferability further increases the risk of adversarial attacks. Current
methods based on transferability often rely on substitute models, which can be
impractical and costly in real-world scenarios due to the unavailability of
training data and the victim model's structural details. In this paper, we
propose a novel approach that directly constructs adversarial examples by
extracting transferable features across various tasks. Our key insight is that
adversarial transferability can extend across different tasks. Specifically, we
train a sequence-to-sequence generative model named CT-GAT using adversarial
sample data collected from multiple tasks to acquire universal adversarial
features and generate adversarial examples for different tasks. We conduct
experiments on ten distinct datasets, and the results demonstrate that our
method achieves superior attack performance with small cost.
