Towards cross-site scripting vulnerability detection in mobile web applications

Cross-site scripting vulnerabilities are among the top ten security vulnerabilities affecting web applications for the past decade and mobile version web applications more recently. They can cause serious problems for web users such as loss of personal information to web attackers, including financial and health information, denial of service attacks, and exposure to malware and viruses. Most of the proposed solutions focused only on the Desktop versions of web applications and overlooked the mobile versions. Increasing use of mobile phones to access web applications increases the threat of cross-site scripting attacks on mobile phones. This paper presents work in progress on detecting cross-site scripting vulnerabilities in mobile versions of web applications. It proposes an enhanced genetic algorithm-based approach that detects cross-site scripting vulnerabilities in mobile versions of web applications. This approach has been used in our previous work and successfully detected the said vulnerabilities in Desktop web applications. It has been enhanced and is currently being tested in mobile versions of web applications. Preliminary results have indicated success in the mobile versions of web applications also. This approach will enable web developers find cross-site scripting vulnerabilities in the mobile versions of their web applications before their release

DISADVANTAGES PRESENTED BY HTML INLINE FRAMES IN INTEGRATION OF 3rd PARTY CONTENT

As new virtual structures emerge, new applications, new widgets, new services become available to embed in websites. One of the preferred solutions for embedding 3rd party content is the HTML Inline Frame or iFrame. In this context, the introduction establishes the importance of the matter: major market players like Facebook, Google and Microsoft decided to include this tag in their solutions, but is it safe? Also, what other problems might webmasters face by implementing it? The results of the research, problems and security threats, are classified in five categories: cross-domain communication, reflection attacks with XSS (cross-site scripting), CSS Overlay, URL Redirection and Host Content Dependence. For each group, examples and code samples are provided, where applicable.iFrame, security, cross-site scripting(XSS), cross domain, malware

Detecting Cross-Site Scripting Attacks Using Machine Learning

Cross-site scripting (XSS) is one of the most frequently occurring types of attacks on web applications, hence is of importance in information security. XSS is where the attacker injects malicious code, typically JavaScript, into the web application in order to be executed in the user’s browser. Identifying that a script is malicious is an important part of the defence of a web application. This paper investigates using SVM, k-NN and Random Forests to detect and limit these attacks, whether known or unknown, by building classifiers for JavaScript code. It demonstrated that using an interesting feature set combining language syntax and behavioural features results in classifiers that give high accuracy and precision on large real world data sets without restricting attention only to obfuscation

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

Removing cross-site scripting vulnerabilities from web applications using the OWASP ESAPI security guidelines

Software security vulnerabilities are present in many web applications and have led to many successful attacks on a daily basis. These attacks, including cross-site scripting, have caused damages for both web site owners and users. Cross-site scripting vulnerabilities are easy to exploit but difficult to eliminate. Most solutions provided only focus on preventing attacks or detecting the vulnerabilities. Very few research works have addressed eliminating these vulnerabilities from the web applications source codes. In this paper, we propose an approach to remove cross-site scripting vulnerabilities from the source code before an application is deployed. We make use of the OWASP cross-site scripting prevention rules as guideline in our approach. The proposed approach is, so far, only implemented and validated on Java-based Web applications, although it can be implemented in other programming languages with slight modifications. Initial evaluation results have indicated promising results

ImageSubXSS: an image substitute technique to prevent Cross-Site Scripting attacks

Cross-Site Scripting (XSS) is one of serious web application attack. Web applications are involved in every activity of human life. JavaScript plays a major role in these web applications. In XSS attacks hacker inject malicious JavaScript into a trusted web application, execution of that malicious script may steal sensitive information from the user. Previous solutions to prevent XSS attacks require a lot of effort to integrate into existing web applications, some solutions works at client-side and some solutions works based on filter list which needs to be updated regularly. In this paper, we propose an Image Substitute technique (ImageSubXSS) to prevent Cross-Site Scripting attacks which works at the server-side. The proposed solution is implemented and evaluated on a number of XSS attacks. With a single line, developers can integrate ImageSubXSS into their applications and the proposed solution is able to prevent XSS attacks effectively

Preventing Cross-Site Scripting Attacks by Combining Classifiers

Cross-Site Scripting (XSS) is one of the most popular attacks targeting web applications. Using XSS attackers can obtain sensitive information or obtain unauthorized privileges. This motivates building a system that can recognise a malicious script when the attacker attempts to store it on a server, preventing the XSS attack. This work uses machine learning to power such a system. The system is based on a combination of classifiers, using cascading to build a two phase classifier and the stacking ensemble technique to improve accuracy. The system is evaluated and shown to achieve high accuracy and high detection rate on a large real world dataset

Automatic Creation of SQL Injection and Cross-Site Scripting Attacks

We present a technique for finding security vulnerabilitiesin Web applications. SQL Injection (SQLI) and cross-sitescripting (XSS) attacks are widespread forms of attackin which the attacker crafts the input to the application toaccess or modify user data and execute malicious code. Inthe most serious attacks (called second-order, or persistent,XSS), an attacker can corrupt a database so as to causesubsequent users to execute malicious code.This paper presents an automatic technique for creatinginputs that expose SQLI and XSS vulnerabilities. The techniquegenerates sample inputs, symbolically tracks taintsthrough execution (including through database accesses),and mutates the inputs to produce concrete exploits. Oursis the first analysis of which we are aware that preciselyaddresses second-order XSS attacks.Our technique creates real attack vectors, has few falsepositives, incurs no runtime overhead for the deployed application,works without requiring modification of applicationcode, and handles dynamic programming-languageconstructs. We implemented the technique for PHP, in a toolArdilla. We evaluated Ardilla on five PHP applicationsand found 68 previously unknown vulnerabilities (23 SQLI,33 first-order XSS, and 12 second-order XSS)