This paper investigates the robustness of the Visual State Space Model (VMamba) in image classification tasks. VMamba, a novel architecture for visual representation learning, has shown promising performance in various computer vision tasks. However, its robustness against adversarial attacks and other perturbations remains underexplored. The study evaluates VMamba's robustness through multiple perspectives, including adversarial attacks, general robustness, gradient analysis, and sensitivity to image structure variations. The analysis reveals that VMamba exhibits superior adversarial robustness compared to Transformer architectures, particularly under FGSM and PGD attacks. However, its scalability is relatively weak against natural adversarial examples and common corruptions. VMamba also demonstrates strong generalizability when faced with out-of-distribution data but shows vulnerabilities against natural adversarial examples and common corruptions. Gradient analysis of VMamba during white-box attacks reveals that parameters B and C are critical to its vulnerability, while parameter Δ provides defensive capabilities. The robustness of VMamba does not increase proportionally with model size due to the trade-off between parameters B, C, and Δ. Sensitivity analysis shows that VMamba is highly sensitive to spatial information and the distribution of disturbance areas, with increased vulnerability near the image center. VMamba also exhibits greater robustness to pixel-wise perturbations compared to the Swin model. The study highlights the need for further research to improve VMamba's scalability and robustness. Future work should focus on reducing the model's dependency on parameters B and C and enhancing the defensive capabilities of parameter Δ. Additionally, alternative scanning strategies and reduced sensitivity to information loss could be explored to enhance VMamba's performance in various scenarios.This paper investigates the robustness of the Visual State Space Model (VMamba) in image classification tasks. VMamba, a novel architecture for visual representation learning, has shown promising performance in various computer vision tasks. However, its robustness against adversarial attacks and other perturbations remains underexplored. The study evaluates VMamba's robustness through multiple perspectives, including adversarial attacks, general robustness, gradient analysis, and sensitivity to image structure variations. The analysis reveals that VMamba exhibits superior adversarial robustness compared to Transformer architectures, particularly under FGSM and PGD attacks. However, its scalability is relatively weak against natural adversarial examples and common corruptions. VMamba also demonstrates strong generalizability when faced with out-of-distribution data but shows vulnerabilities against natural adversarial examples and common corruptions. Gradient analysis of VMamba during white-box attacks reveals that parameters B and C are critical to its vulnerability, while parameter Δ provides defensive capabilities. The robustness of VMamba does not increase proportionally with model size due to the trade-off between parameters B, C, and Δ. Sensitivity analysis shows that VMamba is highly sensitive to spatial information and the distribution of disturbance areas, with increased vulnerability near the image center. VMamba also exhibits greater robustness to pixel-wise perturbations compared to the Swin model. The study highlights the need for further research to improve VMamba's scalability and robustness. Future work should focus on reducing the model's dependency on parameters B and C and enhancing the defensive capabilities of parameter Δ. Additionally, alternative scanning strategies and reduced sensitivity to information loss could be explored to enhance VMamba's performance in various scenarios.