Abstract: Recently, non-transferable learning (NTL) was proposed to restrict models' generalization toward the target domain(s), which serves as state-of-the-art solutions for intellectual property (IP) protection. However, the robustness of the established "transferability barrier" for degrading the target domain performance has not been well studied. In this paper, we first show that the generalization performance of NTL models is widely impaired on third-party domains (i.e., the unseen domain in the NTL training stage). We explore the impairment patterns and find that: due to the dominant generalization of non-transferable task, NTL models tend to make target-domain-consistent predictions on third-party domains, even though only a slight distribution shift from the third-party domain to the source domain. Motivated by these findings, we uncover the potential risks of NTL by proposing a simple but effective method (dubbed as TransNTL) to recover the target domain performance with few source domain data. Specifically, by performing a group of different perturbations on the few source domain data, we obtain diverse third-party domains that evoke the same impairment patterns as the unavailable target domain. Then, we fine-tune the NTL model under an impairment-repair self-distillation framework, where the source-domain predictions are used to teach the model itself how to predict on third-party domains, thus repairing the impaired generalization. Empirically, experiments on standard NTL benchmarks show that the proposed TransNTL reaches up to $\sim$72\% target-domain improvements by using only 10\% source domain data. Finally, we also explore a feasible defense method and empirically demonstrate its effectiveness.