提出新型后门攻击框架，让安全强化学习模型在特定触发下执行危险动作。
PNAct: Crafting Backdoor Attacks in Safe Reinforcement Learning
- 利用正负动作样本设计双通道后门，实现对安全策略的精准操控
- 实验验证攻击在多种环境与策略下均能有效触发危险行为
- 揭示安全强化学习潜在风险，适合关注模型安全性研究者阅读
强化学习广泛应用于智能体与环境交互以最大化奖励的任务中。在此基础上，安全强化学习（Safe RL）引入成本指标，使智能体在决策过程中遵守安全约束。本文首次揭示了安全强化学习面临后门攻击的风险，可诱使智能体执行不安全行为。我们提出首个融合正向动作样本（提供参考行为）与负向动作样本（指示应避免行为）的后门攻击框架——PNAct，定义相关概念与评估指标。理论上分析了该攻击的特性，并设计了具体攻击算法。通过多组实验验证了攻击的有效性，采用标准评估指标进行测试。结果表明该攻击可成功诱导智能体在特定触发条件下产生违反安全规则的行为。本工作揭示了安全强化学习的实际安全隐患，证明此类攻击的可行性。代码与补充材料已公开于 https://github.com/azure-123/PNAct。
原文摘要 · Abstract (English)
Reinforcement Learning (RL) is widely used in tasks where agents interact with an environment to maximize rewards. Building on this foundation, Safe Reinforcement Learning (Safe RL) incorporates a cost metric alongside the reward metric, ensuring that agents adhere to safety constraints during decision-making. In this paper, we identify that Safe RL is vulnerable to backdoor attacks, which can manipulate agents into performing unsafe actions. First, we introduce the relevant concepts and evaluation metrics for backdoor attacks in Safe RL. It is the first attack framework in the Safe RL field that involves both Positive and Negative Action sample (PNAct) is to implant backdoors, where positive action samples provide reference actions and negative action samples indicate actions to be avoided. We theoretically point out the properties of PNAct and design an attack algorithm. Finally, we conduct experiments to evaluate the effectiveness of our proposed backdoor attack framework, evaluating it with the established metrics. This paper highlights the potential risks associated with Safe RL and underscores the feasibility of such attacks. Our code and supplementary material are available at https://github.com/azure-123/PNAct.
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