SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents

Ethan Rathbun, Christopher Amato, Alina Oprea

Keywords: Reinforcement Learning, Backdoor Attacks, Adversarial Machine Learning, Security, Poisoning Attacks, Reinforcement Learning Theory

TL;DR: Theoretically sound backdoor attacks against reinforcement learning with provable guarantees.

Abstract: Reinforcement learning (RL) is an actively growing field that is seeing increased usage in real-world, safety-critical applications -- making it paramount to ensure the robustness of RL algorithms against adversarial attacks. In this work we explore a particularly stealthy form of training-time attacks against RL -- backdoor poisoning. Here the adversary intercepts the training of an RL agent with the goal of reliably inducing a particular action when the agent observes a pre-determined trigger at inference time. We uncover theoretical limitations of prior work by proving their inability to generalize across domains and MDPs. Motivated by this, we formulate a novel poisoning attack framework which interlinks the adversary's objectives with those of finding an optimal policy -- guaranteeing attack success in the limit. Using insights from our theoretical analysis we develop "SleeperNets" as a universal backdoor attack which exploits a newly proposed threat model and leverages dynamic reward poisoning techniques. We evaluate our attack in 6 environments spanning multiple domains and demonstrate significant improvements in attack success over existing methods, while preserving benign episodic return.

Primary Area: Reinforcement learning

Submission Number: 19718