A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents

• URL: http://arxiv.org/abs/2504.14650v1
• Date: Sun, 20 Apr 2025 15:12:14 GMT
• Title: A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents
• Authors: Yuting Huang, Leilei Ding, Zhipeng Tang, Tianfu Wang, Xinrui Lin, Wuyang Zhang, Mingxiao Ma, Yanyong Zhang,
• Abstract summary: Large Language Models (LLMs) exhibit substantial promise in enhancing task-planning capabilities within embodied agents.<n>We present Safe-BeAl, an integrated framework for the measurement (SafePlan-Bench) and alignment (Safe-Align) of LLM-based embodied agents' behaviors.<n>Our empirical analysis reveals that even in the absence of adversarial inputs or malicious intent, LLM-based agents can exhibit unsafe behaviors.
• Score: 13.225168384790257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) exhibit substantial promise in enhancing task-planning capabilities within embodied agents due to their advanced reasoning and comprehension. However, the systemic safety of these agents remains an underexplored frontier. In this study, we present Safe-BeAl, an integrated framework for the measurement (SafePlan-Bench) and alignment (Safe-Align) of LLM-based embodied agents' behaviors. SafePlan-Bench establishes a comprehensive benchmark for evaluating task-planning safety, encompassing 2,027 daily tasks and corresponding environments distributed across 8 distinct hazard categories (e.g., Fire Hazard). Our empirical analysis reveals that even in the absence of adversarial inputs or malicious intent, LLM-based agents can exhibit unsafe behaviors. To mitigate these hazards, we propose Safe-Align, a method designed to integrate physical-world safety knowledge into LLM-based embodied agents while maintaining task-specific performance. Experiments across a variety of settings demonstrate that Safe-BeAl provides comprehensive safety validation, improving safety by 8.55 - 15.22%, compared to embodied agents based on GPT-4, while ensuring successful task completion.

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