Understanding emerging client-Side web vulnerabilities using dynamic program analysis

Today's Web heavily relies on JavaScript as it is the main driving force behind the plethora of Web applications that we enjoy daily. The complexity and amount of this client-side code have been steadily increasing over the years. At the same time, new vulnerabilities keep being uncovered, for which we mostly rely on manual analysis of security experts. Unfortunately, such manual efforts do not scale to the problem space at hand. Therefore in this thesis, we present techniques capable of finding vulnerabilities automatically and at scale that originate from malicious inputs to postMessage handlers, polluted prototypes, and client-side storage mechanisms. Our results highlight that the investigated vulnerabilities are prevalent even among the most popular sites, showing the need for automated systems that help developers uncover them in a timely manner. Using the insights gained during our empirical studies, we provide recommendations for developers and browser vendors to tackle the underlying problems in the future. Furthermore, we show that security mechanisms designed to mitigate such and similar issues cannot currently be deployed by first-party applications due to their reliance on third-party functionality. This leaves developers in a no-win situation, in which either functionality can be preserved or security enforced.JavaScript ist die treibende Kraft hinter all den Web Applikationen, die wir heutzutage täglich nutzen. Allerdings ist über die Zeit hinweg gesehen die Masse, aber auch die Komplexität, von Client-seitigem JavaScript Code stetig gestiegen. Außerdem finden Sicherheitsexperten immer wieder neue Arten von Verwundbarkeiten, meistens durch manuelle Analyse des Codes. In diesem Werk untersuchen wir deshalb Methodiken, mit denen wir automatisch Verwundbarkeiten finden können, die von postMessages, veränderten Prototypen, oder Werten aus Client-seitigen Persistenzmechnanismen stammen. Unsere Ergebnisse zeigen, dass die untersuchten Schwachstellen selbst unter den populärsten Websites weit verbreitet sind, was den Bedarf an automatisierten Systemen zeigt, die Entwickler bei der rechtzeitigen Aufdeckung dieser Schwachstellen unterstützen. Anhand der in unseren empirischen Studien gewonnenen Erkenntnissen geben wir Empfehlungen für Entwickler und Browser-Anbieter, um die zugrunde liegenden Probleme in Zukunft anzugehen. Zudem zeigen wir auf, dass Sicherheitsmechanismen, die solche und ähnliche Probleme mitigieren sollen, derzeit nicht von Seitenbetreibern eingesetzt werden können, da sie auf die Funktionalität von Drittanbietern angewiesen sind. Dies zwingt den Seitenbetreiber dazu, zwischen Funktionalität und Sicherheit zu wählen

Cross Site Scripting Attacks in Web-Based Applications

Web-based applications has turn out to be very prevalent due to the ubiquity of web browsers to deliver service oriented application on-demand to diverse client over the Internet and cross site scripting (XSS) attack is a foremost security risk that has continuously ravage the web applications over the years. This paper critically examines the concept of XSS and some recent approaches for detecting and preventing XSS attacks in terms of architectural framework, algorithm used, solution location, and so on. The techniques were analysed and results showed that most of the available recognition and avoidance solutions to XSS attacks are more on the client end than the server end because of the peculiar nature of web application vulnerability and they also lack support for self-learning ability in order to detect new XSS attacks. Few researchers as cited in this paper inculcated the self-learning ability to detect and prevent XSS attacks in their design architecture using artificial neural networks and soft computing approach; a lot of improvement is still needed to effectively and efficiently handle the web application security menace as recommended

Mining input sanitization patterns for predicting SQL injection and cross site scripting vulnerabilities

Static code attributes such as lines of code and cyclomatic complexity have been shown to be useful indicators of defects in software modules. As web applications adopt input sanitization routines to prevent web security risks, static code attributes that represent the characteristics of these routines may be useful for predicting web application vulnerabilities. In this paper, we classify various input sanitization methods into different types and propose a set of static code attributes that represent these types. Then we use data mining methods to predict SQL injection and cross site scripting vulnerabilities in web applications. Preliminary experiments show that our proposed attributes are important indicators of such vulnerabilities

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)

Automated removal of cross site scripting vulnerabilities in web applications

Cross site scripting (XSS) vulnerability is among the top web application vulnerabilities according to recent surveys. This vulnerability occurs when a web application uses inputs received from users in web pages without properly checking them. This allows an attacker to inject malicious scripts in web pages via such inputs such that the scripts perform malicious actions when a client visits the exploited web pages. Such an attack may cause serious security violations such as account hijacking and cookie theft. Current approaches to mitigate this problem mainly focus on effective detection of XSS vulnerabilities in the programs or prevention of real time XSS attacks. As more sophisticated attack vectors are being discovered, vulnerabilities if not removed could be exploited anytime. To address this issue, this paper presents an approach for removing XSS vulnerabilities in web applications. Based on static analysis and pattern matching techniques, our approach identifies potential XSS vulnerabilities in program source code and secures them with appropriate escaping mechanisms which prevent input values from causing any script execution. We developed a tool, saferXSS, to implement the proposed approach. Using the tool, we evaluated the applicability and effectiveness of the proposed approach based on the experiments on five Java-based web applications. Our evaluation has shown that the tool can be applied to real-world web applications and it automatically removed all the real XSS vulnerabilities in the test subjects

A New View on Classification of Software Vulnerability Mitigation Methods

Software vulnerability mitigation is a well-known research area and many methods have been proposed for it Some papers try to classify these methods from different specific points of views In this paper we aggregate all proposed classifications and present a comprehensive classification of vulnerability mitigation methods We define software vulnerability as a kind of software fault and correspond the classes of software vulnerability mitigation methods accordingly In this paper the software vulnerability mitigation methods are classified into vulnerability prevention vulnerability tolerance vulnerability removal and vulnerability forecasting We define each vulnerability mitigation method in our new point of view and indicate some methods for each class Our general point of view helps to consider all of the proposed methods in this review We also identify the fault mitigation methods that might be effective in mitigating the software vulnerabilities but are not yet applied in this area Based on that new directions are suggested for the future researc

IMPLEMENTATION OF CONCOLIC UNIT TESTING IN TESTING BINARY SEARCH ALGORITHM USING JCUTE

Testing is a process of implementing a program with the aim of finding an error. A good test case is one of the tests that have the possibility of finding an undisclosed error. One of the existing types of testing is the Concolic Unit Testing Engine. In this research, testing is applied using the Jcute tool, which is a tool to systematically and automatically test sequential C programs (including instructions) and Java programs together. This test is carried out on the search function of an element of data in the Binary Search Search Algorithm. However, to check whether concolic testing can detect bugs in the software practically through case studies. This research describes a case study of the application of a test tool to a Java application. Through this research, we tested the path coverage and Branches Covered. We can also find out the details of total branches covered; total functions invoked, percentage of branches covered and the number of iterations. JCUTE can also find an interleaving of two sequences or circuits that results in an infinite loop

HAMPI: A Solver for String Constraints

Many automatic testing, analysis, and verification techniques for programs can be effectively reduced to a constraint-generation phase followed by a constraint-solving phase. This separation of concerns often leads to more effective and maintainable tools. The increasing efficiency of off-the-shelf constraint solvers makes this approach even more compelling. However, there are few, if any, effective and sufficiently expressive off-the-shelf solvers for string constraints generated by analysis techniques for string-manipulating programs. We designed and implemented Hampi, a solver for string constraints over bounded string variables. Hampi constraints express membership in regular languages and bounded context-free languages. Hampi constraints may contain context-free-language definitions, regular-language definitions and operations, and the membership predicate. Given a set of constraints, Hampi outputs a string that satisfies all the constraints, or reports that the constraints are unsatisfiable. Hampi is expressive and efficient, and can be successfully applied to testing and analysis of real programs. Our experiments use Hampi in: static and dynamic analyses for finding SQL injection vulnerabilities in Web applications; automated bug finding in C programs using systematic testing; and compare Hampi with another string solver. Hampi's source code, documentation, and the experimental data are available at http://people.csail.mit.edu/akiezun/hampi