SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents

• URL: http://arxiv.org/abs/2405.20539v2
• Date: Mon, 21 Oct 2024 16:44:58 GMT
• Title: SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents
• Authors: Ethan Rathbun, Christopher Amato, Alina Oprea,
• Abstract summary: Reinforcement learning (RL) is an actively growing field that is seeing increased usage in real-world, safety-critical applications. In this work we explore a particularly stealthy form of training-time attacks against RL -- backdoor poisoning. We formulate a novel poisoning attack framework which interlinks the adversary's objectives with those of finding an optimal policy.
• Score: 16.350898218047405
• License:
• 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.

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