  Transferable targeted adversarial attacks aim to mislead models into
outputting adversary-specified predictions in black-box scenarios. Recent
studies have introduced \textit{single-target} generative attacks that train a
generator for each target class to generate highly transferable perturbations,
resulting in substantial computational overhead when handling multiple classes.
\textit{Multi-target} attacks address this by training only one
class-conditional generator for multiple classes. However, the generator simply
uses class labels as conditions, failing to leverage the rich semantic
information of the target class. To this end, we design a \textbf{C}LIP-guided
\textbf{G}enerative \textbf{N}etwork with \textbf{C}ross-attention modules
(CGNC) to enhance multi-target attacks by incorporating textual knowledge of
CLIP into the generator. Extensive experiments demonstrate that CGNC yields
significant improvements over previous multi-target generative attacks, e.g., a
21.46\% improvement in success rate from ResNet-152 to DenseNet-121. Moreover,
we propose a masked fine-tuning mechanism to further strengthen our method in
attacking a single class, which surpasses existing single-target methods.
