A survey of security vulnerabilities in Android automotive apps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, we conduct an experiment to reproduce some of the existing vulnerabilities of Android platform in Android Auto and Android Automotive platform. Some vulnerabilities specific to automotive were also examined and verified in Android Automotive platform. In total, we examined 14 vulnerabilities out of which 11 were reproducible in Android Auto as these apps are basically Android mobile apps and run directly in user's smartphone device. Whereas for Android Automotive 9 vulnerabilities were reproducible, remaining 5 were not reproducible either due to permission restrictions or due to API deprecation in Android 9.0 (Pie). We also categorize these vulnerabilities as per their type and provided their severity levels. For some of the vulnerabilities we provided the compliant solution which can be useful to mitigate some vulnerabilities while others, like accessing precise location information and deriving speed from it, varies from app to app usage of that information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it