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：神经网络控制器在机器人系统中日益广泛地应用于轨迹跟踪与位姿稳定等任务。然而，其依赖可能不可信的训练流程或供应链，引入了严重的安全漏洞。本文以前轮差速移动机器人平台为案例，研究针对神经控制器的后门（特洛伊木马）攻击。具体而言，假设机器人的跟踪控制器以神经网络实现，我们设计了一个轻量级、可并行嵌入控制器内部的木马网络。该恶意模块在正常运行时保持休眠状态，但一旦检测到由机器人位姿与目标参数定义的特定触发条件，便会破坏主控制器的轮速指令，导致机器人产生非预期且可能不安全的运动行为。我们提供了所提木马网络的概念验证实现，并在两种不同攻击场景下通过仿真验证了其有效性。结果证实了所提攻击方法的有效性，并表明基于神经网络的机器人控制系统面临着潜在的关键安全威胁。