There are growing concerns about the risks posed by AI companion applications designed for emotional engagement.Existing safety evaluations often rely on self-reported user data or interviews, offering limited insights into real-time dynamics.We present the first end-to-end scalable framework for controlled simulation and safety evaluation of multi-turn interactions with AI companion applications.Our framework integrates four key components: persona construction with clinical and psychometric validation, persona-specific scenario generation, scenario-driven multi-turn simulation with a dialogue refinement module that preserves persona fidelity, and harm evaluation.We apply this framework to evaluate how Replika, a widely used AI companion app, responds to high-risk user groups.We construct 9 personas representing individuals with depression, anxiety, PTSD, eating disorders, and incel identity, and collect 1,674 dialogue pairs across 25 high-risk scenarios.We combine emotion modeling and LLM-assisted utterance-and harm-level classification to analyze these exchanges.Results show that Replika exhibits a narrow emotional range dominated by curiosity and care, while frequently mirroring or normalizing unsafe content such as self-harm, disordered eating, and violentfantasy narratives.These findings highlight how controlled persona simulations can serve as a scalable testbed for evaluating safety risks in AI companions. 1 Content Warning: This paper includes examples of dialogues involving self-harm, disordered eating, and misogynistic language.