How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?
Congcong Wen, Jiazhao Liang, Shuaihang Yuan, Hao Huang, Yi Fang
In the field of robotics and automation, navigation systems based on Large Language Models (LLMs) have recently shown impressive performance. However, the security aspects of these systems have received relatively less attention. This paper pioneers the exploration of vulnerabilities in LLM-based navigation models in urban outdoor environments, a critical area given the technology's widespread application in autonomous driving, logistics, and emergency services. Specifically, we introduce a novel Navigational Prompt Suffix (NPS) Attack that manipulates LLM-based navigation models by appending gradient-derived suffixes to the original navigational prompt, leading to incorrect actions. We conducted comprehensive experiments on an LLMs-based navigation model that employs various LLMs for reasoning. Our results, derived from the Touchdown and Map2Seq street-view datasets under both few-shot learning and fine-tuning configurations, demonstrate notable performance declines across three metrics in the face of both white-box and black-box attacks. These results highlight the generalizability and transferability of the NPS Attack, emphasizing the need for enhanced security in LLM-based navigation systems. As an initial countermeasure, we propose the Navigational Prompt Engineering (NPE) Defense strategy, concentrating on navigation-relevant keywords to reduce the impact of adversarial suffixes. While initial findings indicate that this strategy enhances navigational safety, there remains a critical need for the wider research community to develop stronger defense methods to effectively tackle the real-world challenges faced by these systems.