| 要約: | 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.
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