Robustifying Vision-Language Models via Test-Time Prompt Adaptation

Pre-trained Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot generalization, but their performance degrades sharply under adversarial perturbations. Existing test-time adaptation methods typically rely on sample-level confidence heuristics, overlooking the intrinsic distributional structure of the data. This sample-centric approach limits robustness, as it fails to distinguish confident adversarial mispredictions from true semantic consistency. In this work, we observe that adversarial distortion is structurally brittle: while holistic representations are corrupted, semantic integrity is often preserved in the distribution of augmented views. Motivated by this insight, we propose $\texttt{RITA}$, a $\textbf{R}$obust test-t$\textbf{I}$me promp$\textbf{T}$ $\textbf{A}$daptation framework that shifts from sample-level estimates to distribution-level alignment. Specifically, $\texttt{RITA}$ employs optimal transport to align the distribution of augmented visual features with textual prototypes, mitigating adversarial outliers and rectifying cross-modal semantic misalignment. Furthermore, we introduce a dynamic cache to progressively accumulate reliable cues from the test stream for online refinement. Extensive experiments demonstrate that $\texttt{RITA}$ significantly improves adversarial robustness without compromising clean accuracy.