Faculty Publications

Now showing 1 - 3 of 3

Securing native XML database-driven web applications from XQuery injection vulnerabilities
(Elsevier Inc. usjcs@elsevier.com, 2016) Palsetia, N.; Deepa, G.; Ahmed Khan, F.; Santhi Thilagam, P.S.; Pais, A.R.
Database-driven web applications today are XML-based as they handle highly diverse information and favor integration of data with other applications. Web applications have become the most popular way to deliver essential services to customers, and the increasing dependency of individuals on web applications makes them an attractive target for adversaries. The adversaries exploit vulnerabilities in the database-driven applications to craft injection attacks which include SQL, XQuery and XPath injections. A large amount of work has been done on identification of SQL injection vulnerabilities resulting in several tools available for the purpose. However, a limited work has been done so far for the identification of XML injection vulnerabilities and the existing tools only identify XML injection vulnerabilities which could lead to a specific type of attack. Hence, this work proposes a black-box fuzzing approach to detect different types of XQuery injection vulnerabilities in web applications driven by native XML databases. A prototype XQueryFuzzer is developed and tested on various vulnerable applications developed with BaseX as the native XML database. An experimental evaluation demonstrates that the prototype is effective against detection of XQuery injection vulnerabilities. Three new categories of attacks specific to XQuery, but not listed in OWASP are identified during testing. © 2016 Elsevier Inc.
Black-box detection of XQuery injection and parameter tampering vulnerabilities in web applications
(Springer Verlag service@springer.de, 2018) Deepa, G.; Santhi Thilagam, P.S.; Ahmed Khan, F.A.; Praseed, A.; Pais, A.R.; Palsetia, N.
As web applications become the most popular way to deliver essential services to customers, they also become attractive targets for attackers. The attackers craft injection attacks in database-driven applications through the user-input fields intended for interacting with the applications. Even though precautionary measures such as user-input sanitization is employed at the client side of the application, the attackers can disable the JavaScript at client side and still inject attacks through HTTP parameters. The injected parameters result in attacks due to improper server-side validation of user input. The injected parameters may either contain malicious SQL/XML commands leading to SQL/XPath/XQuery injection or be invalid input that intend to violate the expected behavior of the web application. The former is known as an injection attack, while the latter is called a parameter tampering attack. While SQL injection has been intensively examined by the research community, limited work has been done so far for identifying XML injection and parameter tampering vulnerabilities. Database-driven web applications today rely on XML databases, as XML has gained rapid acceptance due to the fact that it favors integration of data with other applications and handles diverse information. Hence, this work proposes a black-box fuzzing approach to detect XQuery injection and parameter tampering vulnerabilities in web applications driven by native XML databases. A prototype XiParam is developed and tested on vulnerable applications developed with a native XML database, BaseX, as the backend. The experimental evaluation clearly demonstrates that the prototype is effective against detection of both XQuery injection and parameter tampering vulnerabilities. © 2017, Springer-Verlag Berlin Heidelberg.
DetLogic: A black-box approach for detecting logic vulnerabilities in web applications
(Academic Press, 2018) Deepa, G.; Santhi Thilagam, P.S.; Praseed, A.; Pais, A.R.
Web applications are subject to attacks by malicious users owing to the fact that the applications are implemented by software developers with insufficient knowledge about secure programming. The implementation flaws arising due to insecure coding practices allow attackers to exploit the application in order to perform adverse actions leading to undesirable consequences. These flaws can be categorized into injection and logic flaws. As large number of tools and solutions are available for addressing injection flaws, the focus of the attackers is shifting towards exploitation of logic flaws. The logic flaws allow attackers to compromise the application-specific functionality against the expectations of the stakeholders, and hence it is important to identify these flaws in order to avoid exploitation. Therefore, a prototype called DetLogic is developed for detecting different types of logic vulnerabilities such as parameter manipulation, access-control, and workflow bypass vulnerabilities in web applications. DetLogic employs black-box approach, and models the intended behavior of the application as an annotated finite state machine, which is subsequently used for deriving constraints related to input parameters, access-control, and workflows. The derived constraints are violated for simulating attack vectors to identify the vulnerabilities. DetLogic is evaluated against benchmark applications and is found to work effectively. © 2018 Elsevier Ltd

Faculty Publications

Browse

Filters

Settings

Sort By

Results per page

Search Results