Backdoor Attacks on Safe Reinforcement Learning-Enabled Cyber–Physical Systems

Abstract

Safe reinforcement learning (RL) aims to derive a control policy that navigates a safety-critical system while avoiding unsafe explorations and adhering to safety constraints. While safe RL has been extensively studied, its vulnerabilities during the policy training have barely been explored in an adversarial setting. This article bridges this gap and investigates the training time vulnerability of formal language-guided safe RL. Such vulnerability allows a malicious adversary to inject backdoor behavior into the learned control policy. First, we formally define backdoor attacks for safe RL and divide them into active and passive ones depending on whether to manipulate the observation. Second, we propose two novel algorithms to synthesize the two kinds of attacks, respectively. Both algorithms generate backdoor behaviors that may go unnoticed after deployment but can be triggered when specific states are reached, leading to safety violations. Finally, we conduct both theoretical analysis and extensive experiments to show the effectiveness and stealthiness of our methods.