Inter-Component Communication (ICC) enables useful interactions between mobile apps. However, misuse of ICC ex- poses users to serious threats such as intent hijacking/spoof- ing and app collusions, allowing malicious apps to access privileged user data via another app. Unfortunately, existing ICC analyses are largely incompetent in both accuracy and scale. This poster points out the need and technical challenges of prioritized analysis of inter-App ICC risks. We propose MR-Droid, a MapReduce-based computing frame- work for accurate and scalable inter-App ICC analysis in An- droid. MR-Droid extracts data-ow features between mul-Tiple communicating apps and the target apps to build a large-scale ICC graph. Our approach is to leverage the ICC graph to provide contexts for inter-App communications to produce precise alerts and prioritize risk assessments. This process requires large app-pair data, which is enabled by our MapReduce-based program analysis. Our initial exten- sive experiments on 11,996 apps from 24 app categories (13 million pairs) demonstrate the scalability of our approach.