  Recently, Multimodal LLMs (MLLMs) have shown a great ability to understand
images. However, like traditional vision models, they are still vulnerable to
adversarial images. Meanwhile, Chain-of-Thought (CoT) reasoning has been widely
explored on MLLMs, which not only improves model's performance, but also
enhances model's explainability by giving intermediate reasoning steps.
Nevertheless, there is still a lack of study regarding MLLMs' adversarial
robustness with CoT and an understanding of what the rationale looks like when
MLLMs infer wrong answers with adversarial images. Our research evaluates the
adversarial robustness of MLLMs when employing CoT reasoning, finding that CoT
marginally improves adversarial robustness against existing attack methods.
Moreover, we introduce a novel stop-reasoning attack technique that effectively
bypasses the CoT-induced robustness enhancements. Finally, we demonstrate the
alterations in CoT reasoning when MLLMs confront adversarial images, shedding
light on their reasoning process under adversarial attacks.
