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Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers
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In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents. Our attack centers on what we call in-distribution triggers, which are triggers native to the data distributions the model will be trained on and deployed in. We outline a simple procedure for embedding these, and other, triggers in deep reinforcement learning agents following a multi-task learning paradigm, and demonstrate in three common reinforcement learning environments. We believe that this work has important implications for the security of deep learning models.
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Cited by 1 Pith paper
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Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning
Backdoors in deep RL agents can be planted by compromising a training component or by editing pretrained weights with no training data, matching training-time attack success on six Atari games.
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