arXiv:2504.17070v3 Announce Type: replace-cross Abstract: Robots need task planning methods to achieve goals that require more than one action. Recently, large pretrained models have demonstrated impressive performance in task planning. For instance, large language models (LLMs) can generate task plans using action and goal descriptions. Despite the rapid progress of large models in robot intelligence, their security implications remain only partially understood, leaving important gaps in the exploration of potential vulnerabilities in LLM-driven robotic planning systems. To investigate such r
The rapid integration of large language models (LLMs) into robotic systems is creating new attack surfaces, making the study of their vulnerabilities increasingly urgent.
This research highlights critical security vulnerabilities in AI-driven robotic task planning, which could lead to significant operational disruptions and safety risks in autonomous systems.
The understanding of AI security in robotics moves beyond traditional software exploits to direct manipulation of LLM-based planning logic, necessitating new defense mechanisms.
- · Cybersecurity researchers
- · AI safety and ethics organizations
- · Developers of robust AI defense systems
- · Robotics companies ignoring LLM security
- · Sectors reliant on unverified autonomous robotic systems
- · Legacy AI security frameworks
Increased investment in securing LLM-driven robotic systems against adversarial attacks.
Development of industry standards and regulatory frameworks specifically addressing AI security in robotics.
A potential slowdown in the deployment of fully autonomous LLM-driven robots until robust security measures are proven.
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Read at arXiv cs.AI