Modular analysis of polyglot applications is challenging because flows of heap objects must be resolved across language boundaries. The state-of-the-art analyses for polyglot applications have two fundamental limitations. First, they assume explicit boundaries between the guest and the host language to determine inter-language dataflows. Second, they rely on specific analyses of the host and guest languages. The former assumption is impractical concerning recent advancements in polyglot programming techniques, while the latter disregards advances in pointer analysis of the underlying languages. In this work, we propose to extend existing pointer analyses with a novel summary specialization technique that unifies points-to sets across language boundaries. Our novel technique leverages combinations of host and guest analyses with minor modifications. We demonstrate the efficacy and generalizability of our approach by evaluating it with two polyglot language models: Java-C communication via Android's NDK and Java-Python communication in GraalVM.

Abstract Android’s intents provide a form of inter-app communication with implicit, capability-based matching of senders and receivers. Such kind of implicit addressing provides some much-needed flexibility but also increases the risk of introducing information flow security bugs and vulnerabilities—as there is no standard way to specify what permissions are required to access the data sent through intents, so that it is handled properly. To mitigate such risks of intent-based communication, this paper introduces IntentRepair, an automated technique to detect such information flow security leaks and to automatically repair them. IntentRepair first finds sender and receiver modules that may communicate via intents, and such that the sender sends sensitive information that the receiver forwards to a public channel. To prevent this flow, IntentRepair patches the sender so that it also includes information about the permissions needed to access the data; and the receiver so that it will only disclose the sensitive information if it possesses the required permissions. We evaluated a prototype implementation of IntentRepair on 869 Android open-source apps, showing that it is effective in automatically detecting and repairing information flow security bugs that originate in implicit intent-based communication, introducing only a modest overhead in terms of patch size.

Android hybrid apps augment native apps with web and inter-language communication capabilities. These apps facilitate the integration of web components, including JavaScript, into native apps. Besides, they allow a two-way communication where JavaScript can utilize functionality shared by the native side (Java). However, due to operational differences between Java and JavaScript, the semantics of this communication are complex. Tracking information flows via this communication channel, i.e., between these heterogeneous platforms, becomes intricate.Multiple approaches have been proposed to analyze hybrid apps. However, most of them focus on specific classes of web-induced vulnerabilities or provide rudimentary tracking of specific information flows via this communication channel. This work proposes a demand-driven analysis to comprehensively track information flow violations from the native side to JavaScript and vice-versa. To this end, our framework selectively creates data flow summaries of the shared native-side code based on its usage in the corresponding JavaScript code. We demonstrate the efficacy of our approach by applying it to various benchmarks and large-scale apps.

Abstract Static analysis frameworks, such as Soot and Wala, are used by researchers to prototype and compare program analyses. These frameworks vary on heap abstraction, modeling library classes, and underlying intermediate program representation (IR). Often, these variations pose a threat to the validity of the results as the implications of comparing the same analysis implementation in different frameworks are still unexplored. Earlier studies have focused on the precision, soundness, and recall of the algorithms implemented in these frameworks; however, little to no work has been done to evaluate the effects of program representation. In this work, we fill this gap and study the impact of program representation on pointer analysis. Unfortunately, existing metrics are insufficient for such a comparison due to their inability to isolate each aspect of the program representation. Therefore, we define two novel metrics that measure these analyses’ precision after isolating the influence of class-hierarchy and intermediate representation. Our results establish that the minor differences in the class hierarchy and IR do not impact program analysis significantly. Besides, they reveal the sources of unsoundness that aid researchers in developing program analysis.

Many Android applications embed webpages via WebView components and execute JavaScript code within Android. Hybrid applications leverage dedicated APIs to load a resource and render it in WebView. Furthermore, Android objects can be shared with the JavaScript world. However, bridging the interfaces of the Android and JavaScript world might also incur severe security threats: Potentially untrusted webpages and their JavaScript might interfere with the Android environment and its access to native features. No general analysis is currently available to assess the implications of such hybrid apps bridging the two worlds. To understand the semantics and effects of hybrid apps, we perform a large-scale study on the usage of the hybridization APIs in the wild. We analyze and categorize the parameters to hybridization APIs for 7,500 randomly selected applications from the Google Playstore. Our results advance the general understanding of hybrid applications, as well as implications for potential program analyses, and the current security situation: We discover 6,375 flows of sensitive data from Android to JavaScript, out of which 82% could flow to potentially untrustworthy code. Our analysis identified 365 web pages embedding vulnerabilities and we exemplarily exploit them. Additionally, we discover 653 applications in which potentially untrusted Javascript code may interfere with (trusted) Android objects.