  This paper investigates the faithfulness of multimodal large language model
(MLLM) agents in the graphical user interface (GUI) environment, aiming to
address the research question of whether multimodal GUI agents can be
distracted by environmental context. A general setting is proposed where both
the user and the agent are benign, and the environment, while not malicious,
contains unrelated content. A wide range of MLLMs are evaluated as GUI agents
using our simulated dataset, following three working patterns with different
levels of perception. Experimental results reveal that even the most powerful
models, whether generalist agents or specialist GUI agents, are susceptible to
distractions. While recent studies predominantly focus on the helpfulness
(i.e., action accuracy) of multimodal agents, our findings indicate that these
agents are prone to environmental distractions, resulting in unfaithful
behaviors. Furthermore, we switch to the adversarial perspective and implement
environment injection, demonstrating that such unfaithfulness can be exploited,
leading to unexpected risks.
