Analyzing Access Control logic in the Android Automotive Framework

Abstract

The Android Automotive Operating System (AAOS) is a specialized version of the Android OS designed specifically for in-vehicle hardware. Prominent car manufacturers, including Honda, General Motors (GM), Volvo, and Ford have already adopted it, with Porsche planning to follow soon. Despite its popularity, little has been done to evaluate the security of AAOS integration, particularly at the framework layer where access control vulnerabilities are likely to arise. To bridge the gap, we perform the first security evaluation of automotive APIs in AAOS. Our study is enabled by AutoAcRaptor, an automated tool that identifies automotive-specific entry points, generates their access control specifications, and analyzes them for potential security risks. AutoAcRaptor leverages static analysis and NLP to perform a three-staged analysis pipeline: 1) Convergence Analysis, 2) Similarity Analysis, and 3) Cross-Image Analysis. Our evaluation demonstrates that the tool is able to efficiently focus the security analysis on auto-specific functionality and pinpoint automotive APIs with likely anomalous access control.