A fine-grained approach for Android taint analysis based on labeled taint value graphs

Abstract

Static taint analysis is a widely used method to identify vulnerabilities in Android applications. However, the existing tools for static analysis often struggle with processing times, particularly when dealing with complex real-world programs. To reduce time consumption, some tools choose to sacrifice analytical precision, e.g., FastDroid sets an upper limit for analysis iterations in Android applications. In this paper, we propose a labeled taint value graph (LTVG) to store taint flows, and implement a fine-grained analysis tool called LabeledDroid. This graph is constructed based on the taint value graph (TVG) of FastDroid, and takes into account both precision and time consumption. That is, we decompile an Android app into Jimple statements, develop fine-grained propagation rules to handle List, and construct LTVGs according to these rules. Afterwards, we traverse LTVGs to obtain high-precision taint flows. An analysis of 39 apps from the TaintBench benchmark shows that LabeledDroid is 0.87 s faster than FastDroid on average. Furthermore, if some common accuracy parameters are adapted in both LabeledDroid and FastDroid, the experiment demonstrates that the former is more scalable. Moreover, the maximum analysis time of LabeledDroid is less than 200 s and its average time is 46.25 s, while FastDroid sometimes experiences timeouts with durations longer than 600 s. Additionally, LabeledDroid achieves a precision of 70% in handling lists, while FastDroid and TaintSA achieve precisions of 38.9% and 41.2%, respectively.