提升大模型机器人系统的可靠性，同时防攻击保安全。
Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safety
- 融合提示组装、状态管理和安全验证，统一防护攻击与保障安全。
- 对抗攻击下性能提升30.8%，复杂环境表现最高提升325%。
- 适合关注大模型机器人安全落地的开发者与研究者。
将大语言模型（LLMs）融入机器人系统已推动具身人工智能的发展，实现更智能的决策与适应性。然而，确保系统可靠性——包括抵御对抗性攻击和在复杂环境中保持安全——仍是关键挑战。为此，本文提出一种统一框架，通过鲁棒的验证机制缓解提示注入攻击，并保障操作安全。方法结合提示组装、状态管理与安全验证，在性能与安全指标上进行评估。实验表明，在提示注入攻击下性能提升30.8%，在复杂环境对抗条件下表现最高提升325%，优于基线场景。该工作弥合了大模型机器人系统中安全与安全之间的鸿沟，为真实世界部署提供可行动见解。框架开源，附带仿真与实物演示：https://llmeyesim.vercel.app/
原文摘要 · Abstract (English)
Integrating large language models (LLMs) into robotic systems has revolutionised embodied artificial intelligence, enabling advanced decision-making and adaptability. However, ensuring reliability, encompassing both security against adversarial attacks and safety in complex environments, remains a critical challenge. To address this, we propose a unified framework that mitigates prompt injection attacks while enforcing operational safety through robust validation mechanisms. Our approach combines prompt assembling, state management, and safety validation, evaluated using both performance and security metrics. Experiments show a 30.8% improvement under injection attacks and up to a 325% improvement in complex environment settings under adversarial conditions compared to baseline scenarios. This work bridges the gap between safety and security in LLM-based robotic systems, offering actionable insights for deploying reliable LLM-integrated mobile robots in real-world settings. The framework is open-sourced with simulation and physical deployment demos at https://llmeyesim.vercel.app/
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