Automated Stealthy Wear-Out Attack on Digital Twins With Deep Reinforcement Learning

Jul 14, 2026

A new preprint describes a deep reinforcement learning-based attack that covertly manipulates control signals in digital twin-enabled industrial systems to accelerate wear and tear on robotic joints while evading anomaly detection. The attack, tested on a UR10e robotic arm, was able to significantly increase torque on targeted joints, resulting in faster degradation and higher maintenance costs. The study benchmarks several reinforcement learning algorithms, finding that Soft Actor-Critic (SAC) is particularly effective for this purpose.

Why it matters: This research demonstrates a novel and practical AI-driven cyberattack method that exposes critical vulnerabilities in digital twin-enabled industrial systems, emphasizing the urgent need for improved security measures.

Full story at: arXiv Cryptography and Security ↗