Search-based Multi-Vulnerability Testing of XML Injections in Web Applications

Modern web applications often interact with internal web services, which are not directly accessible to users. However, malicious user inputs can be used to exploit security vulnerabilities in web services through the application front-ends. Therefore, testing techniques have been proposed to reveal security flaws in the interactions with back-end web services, e.g., XML Injections (XMLi). Given a potentially malicious message between a web application and web services, search-based techniques have been used to find input data to mislead the web application into sending such a message, possibly compromising the target web service. However, state-of-the-art techniques focus on (search for) one single malicious message at a time. Since, in practice, there can be many different kinds of malicious messages, with only a few of them which can possibly be generated by a given front-end, searching for one single message at a time is ineffective and may not scale. To overcome these limitations, we propose a novel co-evolutionary algorithm (COMIX) that is tailored to our problem and uncover multiple vulnerabilities at the same time. Our experiments show that COMIX outperforms a single-target search approach for XMLi and other multi-target search algorithms originally defined for white-box unit testing

Monitoring web applications for vulnerability discovery and removal under attack

Tese de mestrado, Engenharia Informática (Arquitetura, Sistemas e Redes de Computadores) Universidade de Lisboa, Faculdade de Ciências, 2018Web applications are ubiquitous in our everyday lives, as they are deployed in the most diverse contexts and support a variety of services. The correctness of these applications, however, can be compromised by vulnerabilities left in their source code, often incurring in nefarious consequences, such as the theft of private data and the adulteration of information. This dissertation proposes a solution for the automatic detection and removal of vulnerabilities in web applications programmed in the PHP language. By monitoring the user interactions with the web applications with traditional attack discovery tools, it is possible to identify malicious inputs that are eventually provided by attackers. These in- puts are then explored by a directed static analysis approach, allowing for the discovery of potential security issues and the correction of bugs in the program. The solution was implemented and validated with a set of vulnerable web applications. The experimental results demonstrate that the tool is capable of detecting and correcting SQL Injection and XSS vulnerabilities. In total 174 vulnerabilities were found in 5 web applications, where 2 of these were previously unknown by the research community(i.e., they were ”zero-day” vulnerabilities)

Web application penetration testing: an analysis of a corporate application according to OWASP guidelines

During the past decade, web applications have become the most prevalent way for service delivery over the Internet. As they get deeply embedded in business activities and required to support sophisticated functionalities, the design and implementation are becoming more and more complicated. The increasing popularity and complexity make web applications a primary target for hackers on the Internet. According to Internet Live Stats up to February 2019, there is an enormous amount of websites being attacked every day, causing both direct and significant impact on huge amount of people. Even with support from security specialist, they continue having troubles due to the complexity of penetration procedures and the vast amount of testing case in both penetration testing and code reviewing. As a result, the number of hacked websites per day is increasing. The goal of this thesis is to summarize the most common and critical vulnerabilities that can be found in a web application, provide a detailed description of them, how they could be exploited and how a cybersecurity tester can find them through the process of penetration testing. To better understand the concepts exposed, there will be also a description of a case of study: a penetration test performed over a company's web application

Automated and Effective Security Testing for XML-based Vulnerabilities

Nowadays, the External Markup Language (XML) is the most commonly used technology in web services for enabling service providers and consumers to exchange data. XML is also widely used to store data and configuration files that control the operation of software systems. Nevertheless, XML suffers from several well-known vulnerabilities such as XML Injections (XMLi). Any exploitation of these vulnerabilities might cause serious and undesirable consequences, e.g., denial of service and accessing or modifying highly-confidential data. Fuzz testing techniques have been investigated in the literature to detect XMLi vulnerabilities. However, their success rate tends to be very low since they cannot generate complex test inputs required for the detection of these vulnerabilities. Furthermore, these approaches are not effective for real-world complex XML-based enterprise systems, which are composed of several components including front-end web applications, XML gateway/firewall, and back-end web services. In this dissertation, we propose several automated security testing strategies for detecting XML-based vulnerabilities. In particular, we tackle the challenges of security testing in an industrial context. Our proposed strategies, target various and complementary aspects of security testing for XML-based systems, e.g., test case generation for XML gateway/firewall. The development and evaluation of these strategies have been done in close collaboration with a leading financial service provider in Luxembourg/Switzerland, namely SIX Payment Services (formerly known as CETREL S.A.). SIX Payment Services processes several thousand financial transactions daily, providing a range of financial services, e.g., online payments, issuing of credit and debit cards. The main research contributions of this dissertation are: -A large-scale and systematic experimental assessment for detecting vulnerabilities in numerous widely-used XML parsers and the underlying systems using them. In particular, we targeted two common XML parser’s vulnerabilities: (i) XML Billion Laughs (BIL), and (ii) XML External Entities (XXE). - A novel automated testing approach, that is based on constraint-solving and input mutation techniques, to detect XMLi vulnerabilities in XML gateway/firewall and back-end web services. - A black-box search-based testing approach to detect XMLi vulnerabilities in front-end web applications. Genetic algorithms are used to search for inputs that can manipulate the application to generate malicious XML messages. - An in-depth analysis of various search algorithms and fitness functions, to improve the search-based testing approach for front-end web applications. - Extensive evaluations of our proposed testing strategies on numerous real-world industrial web services, XML gateway/firewall, and web applications as well as several open-source systems

SQL Injection Detection Using Machine Learning Techniques and Multiple Data Sources

SQL Injection continues to be one of the most damaging security exploits in terms of personal information exposure as well as monetary loss. Injection attacks are the number one vulnerability in the most recent OWASP Top 10 report, and the number of these attacks continues to increase. Traditional defense strategies often involve static, signature-based IDS (Intrusion Detection System) rules which are mostly effective only against previously observed attacks but not unknown, or zero-day, attacks. Much current research involves the use of machine learning techniques, which are able to detect unknown attacks, but depending on the algorithm can be costly in terms of performance. In addition, most current intrusion detection strategies involve collection of traffic coming into the web application either from a network device or from the web application host, while other strategies collect data from the database server logs. In this project, we are collecting traffic from two points: the web application host, and a Datiphy appliance node located between the webapp host and the associated MySQL database server. In our analysis of these two datasets, and another dataset that is correlated between the two, we have been able to demonstrate that accuracy obtained with the correlated dataset using algorithms such as rule-based and decision tree are nearly the same as those with a neural network algorithm, but with greatly improved performance

Evaluation of Web vulnerability scanners based on OWASP benchmark

Web applications have become an integral part of everyday life, but many of these applications are deployed with critical vulnerabilities that can be fatally exploited. Web Vulnerability scanners have been widely adopted for the detection of vulnerabilities in web applications by checking through the applications with the attackers’ perspectives. However, studies have shown that vulnerability scanners perform differently on detection of vulnerabilities. Furthermore, the effectiveness of some of these scanners has become questionable due to the ever-growing cyber-attacks that have been exploiting undetected vulnerabilities in some web applications. To evaluate the effectiveness of these scanners, people often run these scanners against a benchmark web application with known vulnerabilities. This thesis first presents our results on the effectiveness of two popular web vulnerability scanners based on the OWASP benchmark, which is a benchmark developed by OWASP (Open Web Application Security Project), a prestigious non-profit web security organization. The two scanners chosen in this thesis are OWASP Zed Attack Proxy (OWASP ZAP) and Arachni. As there are many categories of web vulnerabilities and we cannot evaluate the scanner performance on all of them due to time limitation, we pick the following four major vulnerability categories in our thesis: Command Injection, Cross-Site Scripting (XSS), Light Weight Access Protocol (LDAP) Injection, and SQL Injection. Moreover, we compare our results on scanner effectiveness from the OWASP benchmark with the existing results from Web Application Vulnerability Security Evaluation Project (WAVSEP) benchmark, another popular benchmark used to evaluate scanner effectiveness. We are the first to make this comparison between these two benchmarks in literature. The results mainly show that: - Scanners perform differently in different vulnerability categories. That is, no scanner can serve as the all-rounder in scanning web vulnerabilities. - The benchmarks also demonstrate different capabilities in reflecting the effectiveness of scanners in different vulnerability categories. It is recommended to combine the results from different benchmarks to determine the effectiveness of a scanner. - Regarding scanner effectiveness, OWASP ZAP performs the best in CMDI, SQLI, and XSS; Arachni performs the best in LDAP. - Regarding benchmark capability, OWASP benchmark outperforms WAVSEP benchmark in all the examined categories

Automatic Generation of Tests to Exploit XML Injection Vulnerabilities in Web Applications

Modern enterprise systems can be composed of many web services (e.g., SOAP and RESTful). Users of such systems might not have direct access to those services, and rather interact with them through a single-entry point which provides a GUI (e.g., a web page or a mobile app). Although the interactions with such entry point might be secure, a hacker could trick such systems to send malicious inputs to those internal web services. A typical example is XML injection targeting SOAP communications. Previous work has shown that it is possible to automatically generate such kind of attacks using search-based techniques. In this paper, we improve upon previous results by providing more efficient techniques to generate such attacks. In particular, we investigate four different algorithms and two different fitness functions. A large empirical study, involving also two industrial systems, shows that our technique is effective at automatically generating XML injection attacks