State-of-the-art few-shot learning (FSL) methods leverage prompt-based fine-tuning to obtain remarkable results for natural language understanding (NLU) tasks. While much of the prior FSL methods focus on improving downstream task performance, there is a limited understanding of the adversarial robustness of such methods. In this work, we conduct an extensive study of several state-of-the-art FSL methods to assess their robustness to adversarial perturbations. To better understand the impact of various factors towards robustness (or the lack of it), we evaluate prompt-based FSL methods againstResearch goal: Can multimodal few-shot learning models (e.g., CLIP) achieve higher robustness against adversarial perturbations compared to text-only models on the SuperGLUE benchmark?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.