Robots need task planning methods to generate action sequences for complex tasks. Recent work on adversarial attacks has revealed significant vulnerabilities in existing robot task planners, especially those built on foundation models. In this paper, we aim to address these security challenges by introducing PROTEA, an LLM-as-a-Judge defense mechanism, to evaluate the security of task plans. PROTEA is developed to address the dimensionality and history challenges in plan safety assessment. We used different LLMs to implement multiple versions of PROTEA for comparison purposes. For systemic evaluations, we created a dataset containing both benign and malicious task plans, where the harmful behaviors were injected at varying levels of stealthiness. Our results provide actionable insights for robotic system practitioners seeking to enhance robustness and security of their task planning systems. Details, dataset and demos are provided: https://protea-secure.github.io/PROTEA/

翻译：机器人需要任务规划方法来为复杂任务生成动作序列。近期关于对抗性攻击的研究揭示了现有机器人任务规划器（尤其是基于基础模型构建的规划器）存在显著的安全漏洞。本文旨在通过引入PROTEA——一种基于LLM-as-a-Judge的防御机制来评估任务计划的安全性，从而应对这些安全挑战。PROTEA的开发旨在解决计划安全性评估中的维度与历史依赖难题。我们使用不同的LLM实现了多个PROTEA版本以进行对比分析。为进行系统性评估，我们创建了一个包含良性及恶意任务计划的数据集，其中有害行为以不同隐蔽程度被注入。研究结果为寻求增强任务规划系统鲁棒性与安全性的机器人系统实践者提供了可操作的见解。详细内容、数据集及演示请访问：https://protea-secure.github.io/PROTEA/