The rise of malicious mobile applications poses a significant threat to users and app stores. While iOS apps have generally been considered more secure due to strict review process and limited distribution avenues, developers have found ways to evade scrutiny by disguising malicious apps as benign “Mask Apps”. Mask Apps activate hidden functionalities after user downloads or with a trigger event. The malicious and potentially illegal hidden function within Mask Apps poses significant risks, including privacy breaches, security vulnerabilities, and harm to legitimate businesses. However, existing detection methods are ineffectively against Mask Apps developed in web or hybrid models. To address this issue, we propose Mask-Catcher, an automated approach that uses four filtering mechanisms to detect Mask Apps. Mask-Catcher leverages inconsistencies between app descriptions and user reviews, inter-app recommendation relationships, and code similarity analysis to discover and identify Mask Apps. Experimental results demonstrate that Mask-Catcher achieves high recall and precision when applied to labeled and unlabeled datasets from the Apple App Store.