Kristopher Micinski (Haverford College), Thomas Gilray (University of Alabama, Birmingham), Daniel Votipka (University of Maryland), Michelle L. Mazurek (University of Maryland), Jeffrey S. Foster (Tufts University)

Understanding whether Android apps are safe requires, among other things, knowing what dynamically triggers an app to use its permissions, and under what conditions. For example, an app might access contacts after a button click, but only if a certain setting is enabled. Automatically inferring such conditional trigger information is difficult because Android is callback-oriented and reasoning about conditions requires analysis of program paths. To address these issues, we introduce Hogarth, an Android app analysis tool that constructs trigger diagrams, which show, post hoc, what sequence of callbacks, under what conditions, led to a permission use observed at run time. Hogarth works by instrumenting apps to produce a trace of relevant events. Then, given a trace, it performs symbolic path tracing—symbolic execution restricted to path segments from that trace—to infer path conditions at key program locations, and path splicing to combine the per-segment information into a trigger diagram. We validated Hogarth by showing its results match those of a manual reverse-engineering effort on five small apps. Then, in a case study, we applied Hogarth to 12 top apps from Google Play. We found that Hogarth provided more precise information about triggers than prior related work, and was able successfully generate a trigger diagram for all but one permission use in our case study. Hogarth’s performance was generally good, taking at most a few minutes on most of our subject apps. In sum,Hogarth provides a new approach to discovering conditional trigger information for permission uses on Android.

View More Papers

The hard things about analyzing 1’s and 0’s...

Dr. David Brumley, Carnegie Mellon University - ForAllSecure

Read More

RCABench: Open Benchmarking Platform for Root Cause Analysis

Keisuke Nishimura, Yuichi Sugiyama, Yuki Koike, Masaya Motoda, Tomoya Kitagawa, Toshiki Takatera, Yuma Kurogome (Ricerca Security, Inc.)

Read More

PISE: Protocol Inference using Symbolic Execution and Automata Learning

Ron Marcovich, Orna Grumberg, Gabi Nakibly (Technion, Israel Institute of Technology)

Read More