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BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning

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arxiv 2105.00579 v3 pith:EPGMEN4B submitted 2021-05-02 cs.CR cs.LG

Lun Wang , Zaynah Javed , Xian Wu , Wenbo Guo , Xinyu Xing , Dawn Song This is my paper
classification cs.CRcs.LG
keywords backdooragentsystemsattackscompetitiveactivatedbackdoorllearning
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Recent research has confirmed the feasibility of backdoor attacks in deep reinforcement learning (RL) systems. However, the existing attacks require the ability to arbitrarily modify an agent's observation, constraining the application scope to simple RL systems such as Atari games. In this paper, we migrate backdoor attacks to more complex RL systems involving multiple agents and explore the possibility of triggering the backdoor without directly manipulating the agent's observation. As a proof of concept, we demonstrate that an adversary agent can trigger the backdoor of the victim agent with its own action in two-player competitive RL systems. We prototype and evaluate BACKDOORL in four competitive environments. The results show that when the backdoor is activated, the winning rate of the victim drops by 17% to 37% compared to when not activated.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space

    cs.CR 2026-01 conditional novelty 6.0 of 10

    A backdoor attack on vision-language-action robot policies uses the arm's initial joint configuration as the trigger, achieving >90% triggered failure with only small clean-task degradation.

  2. Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning

    cs.LG 2025-07 conditional novelty 6.0 of 10

    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.

  3. Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies

    cs.LG 2025-05 conditional novelty 5.0 of 10

    The paper proposes a one-to-one mapping between six causes of distribution shift and several AI safety issues, arguing for mutual method transfer through aligned definitions.

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