Search for: All records

Cryptographic (crypto) API misuses often cause security vulnerabilities, so static and dynamic analyzers were recently proposed to detect such misuses. These analyzers differ in strengths and weaknesses, and they can miss bugs. Motivated by the inherent limitations of existing analyzers, we study runtime verification (RV) as an alternative for crypto API misuse detection. RV monitors program runs against formal specifications and was shown to be effective and efficient for amplifying the bug-finding ability of software tests. We focus on the popular JCA crypto API and write 22 RV specifications based on expert-validated rules in a static analyzer. We monitor these specifications while running tests in five benchmarks. Lastly, we compare the accuracy of our RV-based approach, RVSec, with those of three state-of-the-art crypto API misuses detectors: CogniCrypt, CryptoGuard, and CryLogger. RVSec has higher accuracy in four benchmarks and is on par with CryptoGuard in the fifth. Overall, RVSec achieves an average F1 measure of 95%, compared with 83%, 78%, and 86% for CogniCrypt, CryptoGuard, and CryLogger, respectively. We show that RV is effective for detecting crypto API misuses and highlight the strengths and limitations of these tools. We also discuss how static and dynamic analysis can complement each other for detecting crypto API misuses. 

Abstract Many critical codebases are written in C, and most of them use preprocessor directives to encode variability, effectively encoding software product lines. These preprocessor directives, however, challenge any static code analysis. SPLlift, a previously presented approach for analyzing software product lines, is limited to Java programs that use a rather simple feature encoding and to analysis problems with a finite and ideally small domain. Other approaches that allow the analysis of real-world C software product lines use special-purpose analyses, preventing the reuse of existing analysis infrastructures and ignoring the progress made by the static analysis community. This work presents VarAlyzer , a novel static analysis approach for software product lines. VarAlyzer first transforms preprocessor constructs to plain C while preserving their variability and semantics. It then solves any given distributive analysis problem on transformed product lines in a variability-aware manner. VarAlyzer ’s analysis results are annotated with feature constraints that encode in which configurations each result holds. Our experiments with 95 compilation units of OpenSSL show that applying VarAlyzer enables one to conduct inter-procedural, flow-, field- and context-sensitive data-flow analyses on entire product lines for the first time, outperforming the product-based approach for highly-configurable systems.