With the emergence of mobile application markets, there has been a dramatic increase in mobile malware. Mobile platform providers are constantly creating and refining their malware-detection techniques, including static analysis and behavioral monitoring. The goal of malware writers is to hide the malware payload from those analyzers. In parallel, security analysts want to quickly detect if any software is malware in order to prevent harm to users. This confrontation is pushing malware writers to develop new evasion techniques that prevent their malware from being detected or making analysis harder. This paper describes ARES, a system built on top of an existing behavioral analysis, based on static information-flow analysis, binary instrumentation, and multiexecution analysis, to detect and bypass many common evasive techniques used by mobile malware. Additionally, this paper presents our implementation of ARES, and shows that, when run against real-world software, ARES is able to reveal previously unknown malicious components. We also developed a test suite for evasion detection techniques: EVADROID, which we have made fully available to other researchers.