We present a framework for computing context-free language reachability properties when parts of the program are missing. Our framework infers candidate specifications for missing program pieces that are needed for verifying a property of interest, and presents these specifications to a human auditor for validation. We have implemented this framework for a taint analysis of Android apps that relies on specifications for Android library methods. In an extensive experimental study on 179 apps, our tool performs verification with only a small number of queries to a human auditor.

App stores are increasingly the preferred mechanism for distributing software, including mobile apps (Google Play), desktop apps (Mac App Store and Ubuntu Software Center), computer games (the Steam Store), and browser extensions (Chrome Web Store). The centralized nature of these stores has important implications for security. While app stores have unprecedented ability to audit apps, users now trust hosted apps, making them more vulnerable to malware that evades detection and finds its way onto the app store. Sound static explicit information flow analysis has the potential to significantly aid human auditors, but it is handicapped by high false positive rates. Instead, auditors currently rely on a combination of dynamic analysis (which is unsound) and lightweight static analysis (which cannot identify information flows) to help detect malicious behaviors. We propose a process for producing apps certified to be free of malicious explicit information flows. In practice, imprecision in the reachability analysis is a major source of false positive information flows that are difficult to understand and discharge. In our approach, the developer provides tests that specify what code is reachable, allowing the static analysis to restrict its search to tested code. The app hosted on the store is instrumented to enforce the provided specification (i.e., executing untested code terminates the app). We use abductive inference to minimize the necessary instrumentation, and then interact with the developer to ensure that the instrumentation only cuts unreachable code. We demonstrate the effectiveness of our approach in verifying a corpus of 77 Android apps—our interactive verification process successfully discharges 11 out of the 12 false positives.