CrySL: An Extensible Approach to Validating the Correct Usage of Cryptographic APIs
Bibliographic record
Abstract
Various studies have empirically shown that the majority of Java and Android applications misuse cryptographic libraries, causing devastating breaches of data security. It is crucial to detect such misuses early in the development process. To detect cryptography misuses, one mustdefinesecure uses first, a process mastered primarily by cryptography experts but not by developers. In this paper, we presentCrySL, a specification language for bridging the cognitive gap between cryptography experts and developers.CrySLenables cryptography experts to specify the secure usage of the cryptographic libraries they provide. We have implemented a compiler that translates suchCrySLspecification into a context-sensitive and flow-sensitive demand-driven static analysis. The analysis then helps developers by automatically checking a given Java or Android app for compliance with theCrySL-encoded rules. We have designed an extensiveCrySLrule set for the Java Cryptography Architecture (JCA), and empirically evaluated it by analyzing 10,000 current Android apps and all 204,788 current Java software artefacts on Maven Central. Our results show that misuse of cryptographic APIs is still widespread, with 95 percent of apps and 63 percent of Maven artefacts containing at least one misuse. Our easily extensibleCrySLrule set covers more violations than previous special-purpose tools that contain hard-coded rules, while still offering a more precise analysis.
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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 | 0.042 | 0.084 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".