Biblio

Synthetic static code analysis test suites are important to test the basic functionality of tools. We present a framework that uses different source code patterns to generate Cross Site Scripting and SQL injection test cases. A decision tree is used to determine if the test cases are vulnerable. The test cases are split into two test suites. The first test suite contains 258,432 test cases that have influence on the decision trees. The second test suite contains 20 vulnerable test cases with different data flow patterns. The test cases are scanned with two commercial static code analysis tools to show that they can be used to benchmark and identify problems of static code analysis tools. Expert interviews confirm that the decision tree is a solid way to determine the vulnerable test cases and that the test suites are relevant.

Web server is an appealing target for attackers since it may be exploited to gain access to an organization’s internal network. After compromising a web server, the attacker can construct a webshell to maintain a long-term and stealthy access for further attacks. Among all webshell-based attacks, command injection is a powerful attack that can be launched to steal sensitive data from the web server or compromising other computers in the network. To monitor and analyze webshell-based command injection, we develop a hybrid webshell honeypot framework called HoneyBog, which intercepts and redirects malicious injected commands from the front-end honeypot to the high-fidelity back-end honeypot for execution. HoneyBog can achieve two advantages by using the client-server honeypot architecture. First, since the webshell-based injected commands are transferred from the compromised web server to a remote constrained execution environment, we can prevent the attacker from launching further attacks in the protected network. Second, it facilitates the centralized management of high-fidelity honeypots for remote honeypot service providers. Moreover, we increase the system fidelity of HoneyBog by synchronizing the website files between the front-end and back-end honeypots. We implement a prototype of HoneyBog using PHP and the Apache web server. Our experiments on 260 PHP webshells show that HoneyBog can effectively intercept and redirect injected commands with a low performance overhead.

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.

PHP is one of the most popular web development tools in use today. A major concern though is the improper and insecure uses of the language by application developers, motivating the development of various static analyses that detect security vulnerabilities in PHP programs. However, many of these approaches do not handle recent, important PHP features such as object orientation, which greatly limits the use of such approaches in practice. In this paper, we present OOPIXY, a security analysis tool that extends the PHP security analyzer PIXY to support reasoning about object-oriented features in PHP applications. Our empirical evaluation shows that OOPIXY detects 88% of security vulnerabilities found in micro benchmarks. When used on real-world PHP applications, OOPIXY detects security vulnerabilities that could not be detected using state-of-the-art tools, retaining a high level of precision. We have contacted the maintainers of those applications, and two applications' development teams verified the correctness of our findings. They are currently working on fixing the bugs that lead to those vulnerabilities.

The Web today is a growing universe of pages and applications teeming with interactive content. The security of such applications is of the utmost importance, as exploits can have a devastating impact on personal and economic levels. The number one programming language in Web applications is PHP, powering more than 80% of the top ten million websites. Yet it was not designed with security in mind and, today, bears a patchwork of fixes and inconsistently designed functions with often unexpected and hardly predictable behavior that typically yield a large attack surface. Consequently, it is prone to different types of vulnerabilities, such as SQL Injection or Cross-Site Scripting. In this paper, we present an interprocedural analysis technique for PHP applications based on code property graphs that scales well to large amounts of code and is highly adaptable in its nature. We implement our prototype using the latest features of PHP 7, leverage an efficient graph database to store code property graphs for PHP, and subsequently identify different types of Web application vulnerabilities by means of programmable graph traversals. We show the efficacy and the scalability of our approach by reporting on an analysis of 1,854 popular open-source projects, comprising almost 80 million lines of code.