Measuring and Characterizing (Mis)compliance of the Android Permission System
Bibliographic record
Abstract
Within the Android mobile operating system, Android permissions act as a system of safeguards designed to restrict access to potentially sensitive data and privileged components. Multiple research studies indicate flaws and limitations of the Android permission system, prompting Google to implement a more regulated and fine-grained permission model. This newly-introduced complexity creates confusion for developers leading to incorrect permissions and a significant risk to users security and privacy. We present a systematic study of theoretical and practical misuse of permissions. For this analysis we derive the unified permissions and call mappings that represent theoretical requirements of permissions and calls. We develop PChecker, an approach that identifies the discrepancies between the official Android permissions documentation and permission implementation in the Android platform source code based on these mappings. We evaluate four versions of the Android Open Source Project code (major versions 10–13) and shed light on the prevalence of discrepancies between the official Android guidelines for permissions and their implementation in the Android platform source code. We further show that these discrepancies result in miscompliance in third-party Android apps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 source (direct Gemma or distilled Codex), 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".