Caution for the Environment: Multimodal LLM Agents are Susceptible to Environmental Distractions

• URL: http://arxiv.org/abs/2408.02544v3
• Date: Fri, 05 Sep 2025 09:21:10 GMT
• Title: Caution for the Environment: Multimodal LLM Agents are Susceptible to Environmental Distractions
• Authors: Xinbei Ma, Yiting Wang, Yao Yao, Tongxin Yuan, Aston Zhang, Zhuosheng Zhang, Hai Zhao,
• Abstract summary: This paper investigates the faithfulness of multimodal large language model (MLLM) agents in a graphical user interface (GUI) environment.<n>A general scenario is proposed where both the user and the agent are benign, and the environment, while not malicious, contains unrelated content.<n> Experimental results reveal that even the most powerful models, whether generalist agents or specialist GUI agents, are susceptible to distractions.
• Score: 50.5976989558411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates the faithfulness of multimodal large language model (MLLM) agents in a graphical user interface (GUI) environment, aiming to address the research question of whether multimodal GUI agents can be distracted by environmental context. A general scenario 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 a 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 of agents, our findings first indicate that these agents are prone to environmental distractions. Furthermore, we implement an adversarial environment injection and analyze the approach to improve faithfulness, calling for a collective focus on this important topic.

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