As　acquiring　manual　labels　on　data　could　be　costly,　unsupervised　domain　adaptation　(UDA),　which　transfers　knowledge　learned　from　a　rich-label　dataset　to　the　unlabeled　target　dataset,　is　gaining　increasingly　more　popularity.　While　extensive　studies　have　been　devoted　to　improving　the　model　accuracy　on　target　domain,　an　important　issue　of　model　robustness　is　neglected.　To　make　things　worse,　conventional　adversarial　training　(AT)　methods　for　improving　model　robustness　are　inapplicable　under　UDA　scenario　since　they　train　models　on　adversarial　examples　that　are　generated　by　supervised　loss　function.　In　this　paper,　we　present　a　new　meta　selftraining　pipeline,　named　SRoUDA,　for　improving　adversarial　robustness　of　UDA　models.　Based　on　self-training　paradigm,　SRoUDA　starts　with　pre-training　a　source　model　by　applying　UDA　baseline　on　source　labeled　data　and　taraget　unlabeled　data　with　a　developed　random　masked　augmentation　(RMA),　and　then　alternates　between　adversarial　target　model　training　on　pseudo-labeled　target　data　and　fine-tuning　source　model　by　a　meta　step.　While　self-training　allows　the　direct　incorporation　of　AT　in　UDA,　the　meta　step　in　SRoUDA　further　helps　in　mitigating　error　propagation　from　noisy　pseudo　labels.　Extensive　experiments　on　various　benchmark　datasets　demonstrate　the　state-of-the-art　performance　of　SRoUDA　where　it　achieves　significant　model　robustness　improvement　without　harming　clean　accuracy.　　Copyright　©　2023,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.