Autonomous Vehicle Application Security

The OpenCAV project. Our team in U-M has open the development of the autonomous vehicle (AV) to faculties, researchers, students and industrial partners, so that they can test their self-driving functionalities on the road. To ensure the security and safety of the vehicle under the threat model of untrusted 3rd party code that can potentially be flawed, vulnerable or even malicious, we propose the AVGuard approach that brings the static and dynamic vetting of various self-driving functionalities into the testing of AV. More detail can be found in the paper.

The open autonomous vehicle we tested on An example path-following self-driving app The vehicle trajectory when testing the app

The TrafficNet project. The enormous effors spent on collecting naturalistic driving data in recent years have resulted in an expansion of publicly available traffic datasets. However, we found that many attempts to utilize these datasets have failed in practice due to a lack of usability concern. We propose TrafficNet, a large-scale and extensible library of naturalistic driving scenarios, aiming at bridging the gap between research datasets and practically usable information for vehicle engineers and researchers. TrafficNet opens not only the scenario library but also the source code of these categorization methods to the public, which will foster more spphisticated and accurate scenario-based categorization algorithms to advance the intelligent transportation research. More detail can be found in the paper

 

Open Port Security

Media coverage: Wired ADT Magzine , TechRepublic , NDTV , International Business Times , Mashable , Bleeping Computer , Digital Trends , Android Headlines , AXIOS , etc.

We design and implement a static analysis tool that can effectively identify and characterize vulnerable open port usage in Android applications. Using this tool, we discovered over 400 vulnerable apps, which can be exploited to cause highly-severe damage such as remotely stealing contacts, photos, and also performing sensitive actions such as malware installation and malicious code execution.

Please visit our website for more information.

Featured attack demo: Session hijacking of AirDroid (CVE-2016-5227).

 

SmartHome Application Security

We developed an app patching based mechanism to add security logic to commodity SmartApps, which enables the verification of an important property called "Contextual Integrity" in the app runtime. We demonstrate that it introduces only minimal performance overhead, and won't risk much user habituation or annoyance.

Please visit our website for some sample malicious SmartApps we created for testing purpose.

 

Android Local Socket Security

We propose a tool called SInspector to expose potential security vulnerabilities in using Unix domain socket. Our analysis revealed some serious vulnerabilities in popular apps and system daemons.

Please visit our website for more information.

Featured attack demo: Privilege Escalation exploiting KingRoot v4.8.2.

 

Off-path Packet Injection Vulnerability Detection

We develop a tool called PacketGuardian that supports implicit flow tainting for Linux kernel code base in C. We use PacketGuardian on 6 popular protocol implementations of TCP, SCTP, DCCP and RTP, and uncover new vulnerabilities in Linux kerneal TCP, as well as 2 out of 3 RTP implementations.

Please visit our website for more information

 

Cross-layer Diagnosis for Voice Call

Our research prototype of cross-layer diagnosis tool on mobile device based on my Mobicom 2015 paper was productized by T-Mobile to automate the problem diagnosis with crowd-sourced data.