Industry 4.0 has witnessed the rise of complex robots fueled by the integration of Artificial Intelligence/Machine Learning (AI/ML) and Digital Twin (DT) technologies. While these technologies offer numerous benefits, they also introduce potential privacy and security risks. This paper surveys privacy attacks targeting robots enabled by AI and DT models. Exfiltration and data leakage of ML models are discussed in addition to the potential extraction of models derived from first-principles (e.g., physics-based). We also discuss design considerations with DT-integrated robotics touching on the impact of ML model training, responsible AI and DT safeguards, data governance and ethical considerations on the effectiveness of these attacks. We advocate for a trusted autonomy approach, emphasizing the need to combine robotics, AI, and DT technologies with robust ethical frameworks and trustworthiness principles for secure and reliable AI robotic systems.

翻译：工业4.0见证了人工智能/机器学习（AI/ML）与数字孪生（DT）技术融合驱动的复杂机器人的兴起。这些技术虽带来诸多益处，但也引入了潜在的隐私与安全风险。本文系统综述了针对AI与DT模型赋能的机器人的隐私攻击方法。除讨论ML模型的渗透与数据泄露外，还探讨了基于第一性原理（如物理建模）衍生模型的潜在提取风险。我们进一步论述了DT集成机器人的设计考量，涉及ML模型训练的影响、负责任AI与DT保障机制、数据治理及伦理因素对这些攻击有效性的作用。我们倡导可信自主技术路径，强调必须将机器人技术、AI与DT系统与坚实的伦理框架及可信原则相结合，以构建安全可靠的人工智能机器人系统。