Neural network controllers are increasingly deployed in robotic systems for tasks such as trajectory tracking and pose stabilization. However, their reliance on potentially untrusted training pipelines or supply chains introduces significant security vulnerabilities. This paper investigates backdoor (Trojan) attacks against neural controllers, using a differential-drive mobile robot platform as a case study. In particular, assuming that the robot's tracking controller is implemented as a neural network, we design a lightweight, parallel Trojan network that can be embedded within the controller. This malicious module remains dormant during normal operation but, upon detecting a highly specific trigger condition defined by the robot's pose and goal parameters, compromises the primary controller's wheel velocity commands, resulting in undesired and potentially unsafe robot behaviours. We provide a proof-of-concept implementation of the proposed Trojan network, which is validated through simulation under two different attack scenarios. The results confirm the effectiveness of the proposed attack and demonstrate that neural network-based robotic control systems are subject to potentially critical security threats.

翻译：神经网络控制器越来越多地应用于机器人系统，用于轨迹跟踪和位姿稳定等任务。然而，其对潜在不可信的训练流程或供应链的依赖引入了显著的安全漏洞。本文以差分驱动机器人平台为例，研究针对神经控制器的后门（木马）攻击。具体而言，假设机器人的跟踪控制器以神经网络实现，我们设计了一种轻量级、并行的木马网络，可嵌入该控制器中。该恶意模块在正常操作期间保持休眠状态，但一旦检测到由机器人位姿和目标参数定义的高度特定触发条件，便会篡改主控制器的车轮速度指令，导致出现非期望且可能不安全的机器人行为。我们提供了所提木马网络的概念验证实现，并通过两种不同攻击场景下的仿真进行了验证。结果证实了该攻击的有效性，并表明基于神经网络的机器人控制系统面临潜在的关键安全威胁。