Various studies have empirically shown that the majority of Java and Android apps misuse cryptographic libraries, causing devastating breaches of data security. erefore, it is crucial to detect such misuses early in the development process. e fact that insecure usages are not the exception but the norm precludes approaches based on property inference and anomaly detection.In this paper, we present C SL, a definition language that enables cryptography experts to specify the secure usage of the cryptographic libraries that they provide. C SL combines the generic concepts of method-call sequences and data-flow constraints with domain-specific constraints related to cryptographic algorithms and their parameters. We have implemented a compiler that translates a C SL ruleset into a context-and flow-sensitive demanddriven static analysis. e analysis automatically checks a given Java or Android app for violations of the C SL-encoded rules.We empirically evaluated our ruleset through analyzing 10,001 Android apps. Our results show that misuse of cryptographic APIs is still widespread, with 96% of apps containing at least one misuse. However, we observed fewer of the misuses that were reported in previous work.

Precise static analyses are context-, field-and flow-sensitive. Context-and field-sensitivity are both expressible as context-free language (CFL) reachability problems. Solving both CFL problems along the same data-flow path is undecidable, which is why most flow-sensitive data-flow analyses over-approximate field-sensitivity through k-limited access-path, or through access graphs. Unfortunately, as our experience and this paper show, both representations do not scale very well when used to analyze programs with recursive data structures. Any single CFL-reachability problem is efficiently solvable, by means of a pushdown system. This work thus introduces the concept of synchronized pushdown systems (SPDS). SPDS encode both procedure calls/returns and field stores/loads as separate but "synchronized" CFL reachability problems. An SPDS solves both individual problems precisely, and approximation occurs only in corner cases that are apparently rare in practice: at statements where both problems are satisfied but not along the same data-flow path. SPDS are also efficient: formal complexity analysis shows that SPDS shift the complexity from ⋃︀F⋃︀ 3k under k-limiting to ⋃︀S⋃︀⋃︀F⋃︀ 2 , where F is the set of fields and S the set of statements involved in a data-flow. Our evaluation using DaCapo shows this shift to pay off in practice: SPDS are almost as efficient as k-limiting with k ≙ 1 although their precision equals k ≙ ∞. For a typestate analysis SPDS accelerate the analysis up to 83× for data-flows of objects that involve many field accesses but span rather few methods. We conclude that SPDS can provide high precision and further improve scalability, in particularly when used in analyses that expose rather local data flows.

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