Biblio

The presence of software vulnerabilities is a serious threat to any software project. Exploiting them can compromise system availability, data integrity, and confidentiality. Unfortunately, many open source projects go for years with undetected ready-to-exploit critical vulnerabilities. In this study, we investigate the presence of software vulnerabilities in open source projects and the factors that influence this presence. We analyzed the top 100 open source PHP applications in GitHub using a static analysis vulnerability scanner to examine how common software vulnerabilities are. We also discussed which vulnerabilities are most present and what factors contribute to their presence. We found that 27% of these projects are insecure, with a median number of 3 vulnerabilities per vulnerable project. We found that the most common type is injection vulnerabilities, which made 58% of all detected vulnerabilities. Out of these, cross-site scripting (XSS) was the most common and made 43.5% of all vulnerabilities found. Statistical analysis revealed that project activities like branching, pulling, and committing have a moderate positive correlation with the number of vulnerabilities in the project. Other factors like project popularity, number of releases, and number of issues had almost no influence on the number of vulnerabilities. We recommend that open source project owners should set secure code development guidelines for their project members and establish secure code reviews as part of the project's development process.

We tackle the problem of automated exploit generation for web applications. In this regard, we present an approach that significantly improves the state-of-art in web injection vulnerability identification and exploit generation. Our approach for exploit generation tackles various challenges associated with typical web application characteristics: their multi-module nature, interposed user input, and multi-tier architectures using a database backend. Our approach develops precise models of application workflows, database schemas, and native functions to achieve high quality exploit generation. We implemented our approach in a tool called Chainsaw. Chainsaw was used to analyze 9 open source applications and generated over 199 first- and second-order injection exploits combined, significantly outperforming several related approaches.