The Java platform provides various cryptographic APIs to facilitate secure coding. However, correctly using these APIs is challenging for developers who lack cybersecurity training. Prior work shows that many developers misused APIs and consequently introduced vulnerabilities into their software. To eliminate such vulnerabilities, people created tools to detect and/or fix cryptographic API misuses. However, it is still unknown (1) how current tools are designed to detect cryptographic API misuses, (2) how effectively the tools work to locate API misuses, and (3) how developers perceive the usefulness of tools’ outputs. For this paper, we conducted an empirical study to investigate the research questions mentioned above. Specifically, we first conducted a literature survey on existing tools and compared their approach design from different angles. Then we applied six of the tools to three popularly used benchmarks to measure tools’ effectiveness of API-misuse detection. Next, we applied the tools to 200 Apache projects and sent 57 vulnerability reports to developers for their feedback. Our study revealed interesting phenomena. For instance, none of the six tools was found universally better than the others; however, CogniCrypt, CogniGuard, and Xanitizer outperformed SonarQube. More developers rejected tools’ reports than those who accepted reports (30 versus 9) due to their concerns on tools’ capabilities, the correctness of suggested fixes, and the exploitability of reported issues. This study reveals a significant gap between the state-of-the-art tools and developers’ expectations; it sheds light on future research in vulnerability detection.

The Java libraries JCA and JSSE offer cryptographic APIs to facilitate secure coding. When developers misuse some of the APIs, their code becomes vulnerable to cyber-attacks. To eliminate such vulnerabilities, people built tools to detect security-API misuses via pattern matching. However, most tools do not (1) fix misuses or (2) allow users to extend tools' pattern sets. To overcome both limitations, we created Seader---an example-based approach to detect and repair security-API misuses. Given an exemplar (insecure, secure) code pair, Seader compares the snippets to infer any API-misuse template and corresponding fixing edit. Based on the inferred info, given a program, Seader performs inter-procedural static analysis to search for security-API misuses and to propose customized fixes.