Understanding and improving app installation security mechanisms through empirical analysis of android
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
We provide a detailed analysis of two largely unexplored aspects of the security decisions made by the Android operating system during the app installation process: update integrity and UID assignment. To inform our analysis, we collect a dataset of Android application metadata and extract features from these binaries to gain a better understanding of how developers interact with the security mechanisms invoked during installation. Using the dataset, we find empirical evidence that Android's current signing architecture does not encourage best security practices. We also find that limitations of Android's UID sharing method force developers to write custom code rather than rely on OS-level mechanisms for secure data transfer between apps. As a result of our analysis, we recommend incrementally deployable improvements, including a novel UID sharing mechanism with applicability to signature-level permissions. We additionally discuss mitigation options for a security bug in Google's Play store, which allows apps to transparently obtain more privileges than those requested in the manifest.
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 | 0.000 | 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 | 0.001 |
| 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 it