One approach to defending against malicious Android applications has been to analyze them to detect potential information leaks. This paper describes a new static taint analysis for Android that combines and augments the FlowDroid and Epicc analyses to precisely track both inter-component and intra-component data flow in a set of Android applications. The analysis takes place in two phases: given a set of applications, we first determine the data flows enabled individually by each application, and the conditions under which these are possible; we then build on these results to enumerate the potentially dangerous data flows enabled by the set of applications as a whole. This paper describes our analysis method, implementation, and experimental results.

Abstract-HTML5 changes many aspects in the browser world by introducing numerous new concepts; in particular, the new HTML5 screen sharing API impacts the security implications of browsers tremendously. One of the core assumptions on which browser security is built is that there is no cross-origin feedback loop from the client to the server. However, the screen sharing API allows creating a cross-origin feedback loop. Consequently, websites will potentially be able to see all visible content from the user's screen, irrespective of its origin. This cross-origin feedback loop, when combined with human vision limitations, can introduce new vulnerabilities. An attacker can capture sensitive information from victim's screen using the new API without the consensus of the victim. We investigate the security implications of the screen sharing API and discuss how existing defenses against traditional web attacks fail during screen sharing. We show that several attacks are possible with the help of the screen sharing API: cross-site request forgery, history sniffing, and information stealing. We discuss how popular websites such as Amazon and Wells Fargo can be attacked using this API and demonstrate the consequences of the attacks such as economic losses, compromised account and information disclosure. The objective of this paper is to present the attacks using the screen sharing API, analyze the fundamental cause and motivate potential defenses to design a more secure screen sharing API.

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