In-group favoritism refers to the phenomenon of favoring members of one’s in-group over out-group members and is widely observed in numerous social cooperative behaviors. Recently, in-group favoritism biases have also been identified in generative language models. However, whether persona agents exhibit in-group favoritism when faced with conflicting information, including misinformation, and how to mitigate its adverse effects remain understudied. To address these problems, we propose a Truth or Tribe simulation framework to study agent cooperation in the spread of conflicting information through a triadic interaction paradigm, and conduct controlled trials to evaluate the primary moderating factors. Extensive results showcase that persona agents display strong in-group favoritism, accepting incorrect answers from identity-similar peers at much higher rates than from dissimilar peers. In-group favoritism continues to emerge in defeasible reasoning contexts where no absolute truth exists, and it intensifies as cognitive complexity increases. Furthermore, three intervention strategies—Identity-Blind Instruction, Structured Counterfactual Reasoning, and Heterogeneous Perspective Ensemble—are proposed to mitigate in-group favoritism.