An Approach for the Identification of Information Leakage in Automotive Infotainment systems
Bibliographic record
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 be hijacked or spoofed by malicious apps and user's sensitive information can be leaked to hacker's database. We investigate the attack surface and vulnerabilities in these apps and provide a static analysis approach and a tool to find data leakage vulnerabilities. The approach can also provide hints to mitigate these leaks. We evaluate our approach by analyzing a set of Android Auto apps downloaded from Google Play store, and we report our validated results on vulnerabilities identified on those apps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".