  Recent advances in instruction tuning have led to the development of
State-of-the-Art Large Multimodal Models (LMMs). Given the novelty of these
models, the impact of visual adversarial attacks on LMMs has not been
thoroughly examined. We conduct a comprehensive study of the robustness of
various LMMs against different adversarial attacks, evaluated across tasks
including image classification, image captioning, and Visual Question Answer
(VQA). We find that in general LMMs are not robust to visual adversarial
inputs. However, our findings suggest that context provided to the model via
prompts, such as questions in a QA pair helps to mitigate the effects of visual
adversarial inputs. Notably, the LMMs evaluated demonstrated remarkable
resilience to such attacks on the ScienceQA task with only an 8.10% drop in
performance compared to their visual counterparts which dropped 99.73%. We also
propose a new approach to real-world image classification which we term query
decomposition. By incorporating existence queries into our input prompt we
observe diminished attack effectiveness and improvements in image
classification accuracy. This research highlights a previously under-explored
facet of LMM robustness and sets the stage for future work aimed at
strengthening the resilience of multimodal systems in adversarial environments.
