Evaluating State-of-the-Art Free and Open Source Static Analysis Tools Against Buffer Errors in Android Apps
Bibliographic record
Abstract
Modern mobile apps incorporate rich and complex features, opening the doors for different security concerns. Android is the dominant platform in mobile app markets, and enhancing its apps security is a considerable area of research. Android malware (introduced intentionally by developers) has been well studied and many tools are available to detect them. However, little attention has been directed to address vulnerabilities caused unintentionally by developers in Android apps. Static analysis has been one way to detect such vulnerabilities in traditional desktop and server side desktop. Therefore, our research aims at assessing static analysis tools that could be used by Android developers. Our preliminary analysis revealed that Buffer Errors are the most frequent type of vulnerabilities that threaten Android apps. Also, we found that Buffer Errors in Android apps have the highest risk on Android that affects data integrity, confidentiality, and availability. Our main study therefore tested whether state-of-the-art static analysis tools could detect Buffer Errors in Android apps. We investigated 6 static analysis tools that are designed to detect Buffer Errors. The study shows that the free and open source state-of-the-art static analysis tools do not efficiently discover Buffer Error vulnerabilities in Android apps. We analyzed the tools carefully to see why they could not discover Buffer Errors and found that the lack of semantic analysis capabilities, inapplicability to Android apps, and the gap between native code and other contexts were some of the reasons. Thus, we concluded that there is a need to build better free and open source static analysis tools for detecting Buffer Errors in 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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".