  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.
