AdaShield can be adapted to address perturbation-based attacks by incorporating defense prompts that specifically target the vulnerabilities exploited in such attacks. Perturbation-based attacks aim to disrupt the alignment of MLLMs by introducing imperceptible changes to input data. To defend against these attacks, AdaShield can generate prompts that guide the model to detect and respond appropriately to adversarial perturbations. By iteratively refining defense prompts based on feedback from failed responses to perturbed inputs, AdaShield can enhance the robustness of MLLMs against this type of attack.