Cross-site scripting (XSS) vulnerabilities are specific flaws related to web applications, in which missing input validation can be exploited by attackers to inject malicious code into the application under attack. To guarantee high quality of web applications in terms of security, we propose a structured approach, inspired by software testing. In this paper we present our research plan and ongoing work to use security testing to address problems of potentially attackable code. Static analysis is used to reveal candidate vulnerabilities as a set of execution conditions that could lead to an attack. We then resort to automatic test case generation to obtain those input values that make the application execution satisfy such conditions. Eventually, we propose a security oracle to assess whether such test cases are instances of successful attacks.

More and more web applications suffer the presence of cross-site scripting vulnerabilities that could be exploited by attackers to access sensitive information (such as credentials or credit card numbers). Hence proper tests are required to assess the security of web applications. In this paper, we resort to a search based approach for security testing web applications. We take advantage of static analysis to detect candidate cross-site scripting vulnerabilities. Input values that expose these vulnerabilities are searched by a genetic algorithm and, to help the genetic algorithm escape local optima, symbolic constraints are collected at run-time and passed to a solver. Search results represent test cases to be used by software developers to understand and fix security problems. We implemented this approach in a prototype and evaluated it on real world PHP code.