A Modeling and Static Analysis Approach for the Verification of Privacy and Safety Properties in Kotlin Android Apps
Bibliographic record
Abstract
The safety and privacy of medical devices is critical, as they directly affect the health of users and handle sensitive personal data. Ensuring that these devices meet safety and security standards is essential, especially with the rise of do-it-yourself solutions such as open-source artificial pancreas systems (APSs) for insulin delivery. In this work, we study AndroidAPS, an APS controller written in Kotlin, and propose an approach to detect safety and security issues. We develop a modeling and analysis framework for Kotlin applications that extracts a structural model and supports detecting logging vulnerabilities and ensuring the application of safety constraints. We conduct two experiments. The first examines logging behavior to check for privacy risks. Out of $\mathbf{3, 0 5 9 ~ l o g g i n g}$ instances, our tool identified 48 sinks that received 144 sensitive flows, with $68 \%$ precision due to coarse-grained flagging. The second experiment verifies that calculation-related values are validated against safety constraints before being set to the profile. We show that AndroidAPS generally adheres to its safety design properties, but it has one calculation-related value that is not explicitly validated at the plugin level and only partially validated earlier in the flow.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".