Finding Security Vulnerabilities in Java Web Applications with Test Generation and Dynamic Taint Analysis

Author(s):  
Yu-Yu Huang ◽  
Kung Chen ◽  
Shang-Lung Chiang
2020 ◽  
Vol 10 (20) ◽  
pp. 7338
Author(s):  
Youn Kyu Lee ◽  
Dohoon Kim

Event-based system (EBS) is prevalent in various systems including mobile cyber physical systems (MCPSs), Internet of Things (IoT) applications, mobile applications, and web applications, because of its particular communication model that uses implicit invocation and concurrency between components. However, an EBS’s non-determinism in event processing can introduce inherent security vulnerabilities into the system. Multiple types of attacks can incapacitate and damage a target EBS by exploiting this event-based communication model. To minimize the risk of security threats in EBSs, security efforts are required by determining the types of security flaws in the system, the relationship between the flaws, and feasible techniques for dealing with each flaw. However, existing security flaw taxonomies do not appropriately reflect the security issues that originate from an EBS’s characteristics. In this paper, we introduce a new taxonomy that defines and classifies the particular types of inherent security flaws in an EBS, which can serve as a basis for resolving its specific security problems. We also correlate our taxonomy with security attacks that can exploit each flaw and identify existing solutions that can be applied to preventing such attacks. We demonstrate that our taxonomy handles particular aspects of EBSs not covered by existing taxonomies.


2018 ◽  
Vol 7 (4.1) ◽  
pp. 18
Author(s):  
Isatou Hydara ◽  
Abu Bakar Md Sultan ◽  
Hazura Zulzalil ◽  
Novia Admodisastro

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.  


2015 ◽  
Vol 25 (09n10) ◽  
pp. 1777-1782
Author(s):  
Frederik H. Nakstad ◽  
Hironori Washizaki ◽  
Yoshiaki Fukazawa

Existing techniques for crawling Javascript-heavy Rich Internet Applications tend to ignore user interactions beyond mouse clicking, and therefore often fail to consider potential mouse, keyboard and touch interactions. We propose a new technique for automatically finding and exercising such interactions by analyzing and exercising event handlers registered in the DOM. A basic form of gesture emulation is employed to find states accessible via swiping and tapping. Testing the tool against 6 well-known gesture libraries and 5 actual RIAs, we find that the technique discovers many states and transitions resulting from such interactions, and could be useful for cases such as automatic test generation and error discovery, especially for mobile web applications.


Author(s):  
Pietro Ferrara ◽  
Amit Kr Mandal ◽  
Agostino Cortesi ◽  
Fausto Spoto

AbstractThe Open Web Application Security Project (OWASP), released the “OWASP Top 10 Internet of Things 2018” list of the high-priority security vulnerabilities for IoT systems. The diversity of these vulnerabilities poses a great challenge toward development of a robust solution for their detection and mitigation. In this paper, we discuss the relationship between these vulnerabilities and the ones listed by OWASP Top 10 (focused on Web applications rather than IoT systems), how these vulnerabilities can actually be exploited, and in which cases static analysis can help in preventing them. Then, we present an extension of an industrial analyzer (Julia) that already covers five out of the top seven vulnerabilities of OWASP Top 10, and we discuss which IoT Top 10 vulnerabilities might be detected by the existing analyses or their extension. The experimental results present the application of some existing Julia’s analyses and their extension to IoT systems, showing its effectiveness of the analysis of some representative case studies.


2010 ◽  
Vol 36 (4) ◽  
pp. 474-494 ◽  
Author(s):  
S Artzi ◽  
A Kiezun ◽  
J Dolby ◽  
F Tip ◽  
D Dig ◽  
...  

Author(s):  
Suresh Thummalapenta ◽  
K. Vasanta Lakshmi ◽  
Saurabh Sinha ◽  
Nishant Sinha ◽  
Satish Chandra

2014 ◽  
Vol 678 ◽  
pp. 468-472 ◽  
Author(s):  
Cheng He ◽  
Yan Fei Liu

This paper combines an analysis of structural modeling on security vulnerabilities and a focused behavioral model examination to develop a vulnerability model to depict and reason about security vulnerabilities. An in-depth analysis of the structural models and the corresponding diagram of the applications come from the investigation of not only multiple vulnerable operations on multiple objects being involved in exploiting vulnerability but also the vulnerability data and corresponding data flow inspections deriving from behavioral modeling of the application. We also propose a vulnerability model-based security testing approach that automatically generates security test sequences from vulnerability model diagram and transforms them into executable tests on the basis of the vulnerable operations and vulnerability data.


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