Offline reinforcement learning (offline RL) enables policy learning from pre-collected static datasets without online exploration, and is increasingly deployed not only in safety-critical domains such as autonomous driving and robotic control but also in data-mining applications such as recommendation and behavior analysis. While compliance-driven data removal enhances privacy, it also opens a previously unrecognized attack surface. We introduce UBA-ORL (Unlearning-activated Backdoor Attack on Offline Reinforcement Learning), the first unlearning-activated backdoor attack for offline RL: in the evaluated settings, the attack is substantially suppressed after normal training and becomes pronounced after a compliance-driven deletion (unlearning) request. UBA-ORL employs a dual-sample mechanism: alongside backdoor trajectories (BD) that link a trigger to malicious actions under inflated rewards, the attacker injects camouflage trajectories (CM) sharing the same trigger pattern but preserving benign actions with equally high rewards. During training, BD and CM provide competing supervisory signals; upon a legitimate deletion request on the CM subset, the residual BD signal can re-dominate, reactivating the backdoor on demand. Empirical results show that UBA-ORL achieves controllable activation under the evaluated offline-RL configurations, while no-trigger return changes vary by configuration, exposing a previously overlooked security risk in compliance-driven offline RL platforms. We urge the community to develop joint pre-/post-unlearning auditing mechanisms for compliant unlearning services.

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