An Approach for the Identification of Information Leakage in Automotive Infotainment systems
Abdul Moiz, Manar H. Alalfi

TL;DR
This paper presents a static analysis method and tool to detect data leakage vulnerabilities in Android Auto infotainment apps, addressing security concerns related to inter-component communication and malicious app hijacking.
Contribution
It introduces a novel static analysis approach and tool specifically designed to identify information leakage vulnerabilities in automotive infotainment applications.
Findings
Identified multiple data leakage vulnerabilities in real Android Auto apps
Demonstrated effectiveness of the approach in analyzing apps from Google Play
Provided mitigation hints for security improvements
Abstract
The advancements in the digitization world has revolutionized the automotive industry. Today's modern cars are equipped with internet, computers that can provide autonomous driving functionalities as well as infotainment systems that can run mobile operating systems, like Android Auto and Apple CarPlay. Android Automotive is Google's android operating system tailored to run natively on vehicle's infotainment systems, it allows third party apps to be installed and run on vehicle's infotainment systems. Such apps may raise security concerns related to user's safety, security and privacy. This paper investigates security concerns of in-vehicle apps, specifically, those related to inter component communication (ICC) among these apps. ICC allows apps to share information via inter or intra apps components through a messaging object called intent. In case of insecure communication, Intent can…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Vehicular Ad Hoc Networks (VANETs) · Privacy, Security, and Data Protection
