Dynamic analysis and UI automation are essential for scalable detection of privacy leaks, vulnerabilities, and malicious code in mobile apps. While the Android ecosystem offers a variety of tools, options for iOS apps are limited and require either access to the app source code or jailbreaking the test device. To address this gap, we introduce WhisperTest, an open-source iOS UI automation library that operates without jailbreaking. WhisperTest is based on a newly designed approach that leverages Apple’s Voice Control accessibility feature to interact with app or system UIs via text-to-speech. During interactions, WhisperTest monitors the device system logs in real time and scrapes the UI via screenshots and accessibility audits to recover app state changes. We demonstrate WhisperTest’s capabilities through a diverse set of tasks, including a web privacy measurement and a fully-automated dynamic analysis of 200 child-directed iOS apps. To overcome the challenges of automating apps with diverse UI designs, WhisperTest optionally integrates multi-modal large language models to reason about context and interact with system permission prompts, consent dialogs, subscription prompts, and age gates. Our exploratory analysis of children’s apps uncovers widespread use of third-party tracking, limited recognition of user consent, and unencrypted HTTP requests. Overall, we show that WhisperTest enables scalable dynamic analysis of iOS applications across diverse tasks, contributing to a safer and more transparent mobile ecosystem.