Reassessing AI Robot Security: Three Layers of Network Attack Surface Emerge

This article is provided by VicOne, with information from IEEE Spectrum·Robotics, organized by AI. The article proposes that in the era of physical AI, security issues are no longer just about whether a machine is safe when it fails, but also include the risks when attackers change what a robot sees, judges, or does—while the system may show no abnormality on the surface. The article divides the attack surface into three layers: model backdoors in the training pipeline (such as BadNets and BadVLA targeting vision-language-action models), system stack vulnerabilities (such as the Bluetooth exploit chain UniPwn affecting quadruped and humanoid robots), and runtime manipulation of perception and reasoning. The authors argue that beyond functional safety, cybersecurity must also be introduced, covering the entire robot lifecycle from design, pre-deployment validation, to runtime monitoring.