The Experts below are selected from a list of 168 Experts worldwide ranked by ideXlab platform

Dimitris Antonaropoulos - One of the best experts on this subject based on the ideXlab platform.

  • nodexp node js server side javascript Injection Vulnerability detection and exploitation
    Workshop on Information Security Applications, 2021
    Co-Authors: Christoforos Ntantogian, Dimitris Antonaropoulos, Panagiotis Bountakas, Constantinos Patsakis, Christos Xenakis
    Abstract:

    Abstract Web applications are widely used, and new ways for easier and cost-effective methods to develop them are constantly introduced. A common omission among the new development and implementation techniques when designing them is security; Node.js is no exception, as Server-Side JavaScript Injection ( SSJI ) attacks are possible due to the use of vulnerable functions and neglecting to sanitize data input provided by untrusted sources. This specific kind of Injection attack stands out because it has the potential to compromise servers, where the JavaScript code is executed. In this work, we fill a significant gap in the literature by introducing NodeXP , which, to the best of our knowledge, is the first methodology (presented as a software tool) that detects and automatically exploits SSJI vulnerabilities. Beyond the capabilities of the current state-of-the-art tools, NodeXP uses obfuscation methods, making it more stealth and adaptive to the current needs of red teaming. To this end, we provide a thorough analysis of SSJI attacks and the foundation upon which they rely on, along with concrete examples to facilitate the reader to comprehend the underlying concepts. Finally, we evaluate NodeXP , compare it to its peers, and discuss its efficacy.

  • NodeXP - An automated and integrated tool for detecting and exploiting Server Side JavaScript Injection Vulnerability on Node.js services
    2018
    Co-Authors: Dimitris Antonaropoulos
    Abstract:

    The intent of this thesis was to develop a tool (referred as NodeXP) capable of detecting possible vulnerabilities on Node.js services and exploiting them in order to create proof-of-concept (PoC). The above processes are making use of Server Side JavaScript Injection (SSJI) Vulnerability and its attack methods and are completely separated, yet integrated on the same tool and interacting with each other with minimum user insertion. The detection process is done through dynamic analysis using two different Injection techniques (Blind Based Injection Technique and Results Based Injection Technique). Through the execution of any of the Injection techniques, payloads listed on a certain text file are parsed and injected, through HTTP requests (wordlist method). The exploitation process aims to create a Meterpreter session between the user and the vulnerable service which is done through interacting with Metasploit framework. When detection process is successfully done then the exploitation process is taking place based on detection’s findings. During both the detection and the exploitation processes, only one GET or POST parameter could be injected at a time. The tool’s intention is to point those security issues out through accuracy and mitigation of false positives and false negatives. The above requirement might lead to some time and performance penalty. Thus, some helpful flags provided are able to handle this ratio depending on user’s need. Through the thesis are presented real-world and custom-made examples on Node.js services, demonstrating the detection as well as the exploitation of the vulnerabilities found. The tool’s purpose is strictly informational and educational, and the tool could also be very helpful during the process of a penetration test. Any other malicious or illegal usage of the tool is strongly not recommended and is clearly not a part of the purpose of this research.

Muhammad Saidu Aliero - One of the best experts on this subject based on the ideXlab platform.

  • sql Injection Vulnerability scanner
    2019
    Co-Authors: Muhammad Saidu Aliero
    Abstract:

    SQL Injection Attack (SQLIA) is one of the most severe attacks that can be used against web database driven applications. Attackers’ use SQLIA to get unauthorized access and perform un-authorize data modification as result of improper input validation by web application developer. Various studies have shown that average of 64% of web application of worldwide are vulnerable to SQLIA attack as result of their Vulnerability. To mitigate the devastating problem of SQLIA, this research proposed automatic dynamic SQL Injection Vulnerability scanner (SQLIVS) that automates SQLI Vulnerability assessment that results in SQLIA. Recent study shows that there is need for improving effectiveness of existing SQLIVS to reduce the cost of manual inspection of vulnerabilities and risk of being attack as result of false negative and false positive result reported by the SQLIV scanner. The research focus on improving effectiveness of SQLIVS by proposing object oriented approach in development of SQLIVS in order to help controlling of false positive an false negative result as well as to provide the room for improving proposed scanner by the potential researchers.

  • An algorithm for detecting SQL Injection Vulnerability using black-box testing
    Journal of Ambient Intelligence and Humanized Computing, 2019
    Co-Authors: Muhammad Saidu Aliero, Imran Ghani, Kashif Naseer Qureshi, Mohd Fo’ad Rohani
    Abstract:

    SQL Injection Attack (SQLIA) is one of the most severe attack that can be used against web database-driven applications. Attackers use SQLIA to obtain unauthorized access and perform unauthorized data modifications due to initial improper input validation by the web application developer. Various studies have shown that, on average, 64% of web applications worldwide are vulnerable to SQLIA due to improper input. To mitigate the devastating problem of SQLIA, this research proposes an automatic black box testing for SQL Injection Vulnerability (SQLIV). This acts to automate an SQLIV assessment in SQLIA. In addition, recent studies have shown that there is a need for improving the effectiveness of existing SQLIVS in order to reduce the cost of manual inspection of vulnerabilities and the risk of being attacked due to inaccurate false negative and false positive results. This research focuses on improving the effectiveness of SQLIVS by proposing an object-oriented approach in its development in order to help and minimize the incidence of false positive and false negative results, as well as to provide room for improving a proposed scanner by potential researchers. To test and validate the accuracy of research work, three vulnerable web applications were developed. Each possesses a different type of vulnerabilities and an experimental evaluation was used to validate the proposed scanner. In addition, an analytical evaluation is used to compare the proposed scanner with the existing academic scanners. The result of the experimental analysis shows significant improvement by achieving high accuracy compared to existing studies. Similarly, the analytical evaluations showed that the proposed scanner is capable of analyzing attacked page response using four different techniques.

  • A component based SQL Injection Vulnerability detection tool
    2015 9th Malaysian Software Engineering Conference (MySEC), 2015
    Co-Authors: Muhammad Saidu Aliero, Imran Ghani
    Abstract:

    SQL Injection attack (SQLIA) is one of the most severe attacks that can be used against web database driving applications. Attackers' use SQLIA to get unauthorized access to and perform unauthorized data modification. To mitigate the devastating problem of SQLIA, different researchers proposed variety of web penetration testing tools that automation of SQLI Vulnerability assessment that result in SQLIA. Recent study shows that there is need for adaptation of object orienting approach in development of application program in order to reduce the cost of integration and maintenance, as well as improve the efficiency of application programs. Most of the proposed SQLIV (SQL Injection Vulnerability) detection tools by academic researchers seem to focus on improving efficiency or effectiveness of SQLIV detection tool thereby paying less attention to advantage of adopting reusable component. Therefore, this paper propose component based (CBC) SQLIV detection tool that has the potential to enable developer to reuse component where necessary and allow integration and maintenance fast and in less cost. The proposed tool was tested on three different vulnerable web applications after which its effectiveness was compared against seven(7) different SQLIV detection tool accordingly, the result of evaluation proves that the tool has all the potential to detect SQLIV vulnerabilities on different scenarios that other of scanners ware unable to detect.

Yang Jufeng - One of the best experts on this subject based on the ideXlab platform.

  • A Dynamic SQL Injection Vulnerability Test Case Generation Model Based on the Multiple Phases Detection Approach
    2013 IEEE 37th Annual Computer Software and Applications Conference, 2013
    Co-Authors: Liu Lei, Xu Jing, Li Minglei, Yang Jufeng
    Abstract:

    SQL Injection Vulnerability (SQLIV) is one of the topmost serious threats to web applications. Penetration test is one of the most important approaches to detect SQLIV. The test case generation issue critically affects the effectiveness of penetration test. Thus, research on the approaches to improve coverage and efficiency of test case generation process in SQLIV penetration test is of great importance. This paper proposes a formalized SQLIV test case generation model. i) We propose Global Test Rule (GTR), which is used to generate test cases in the process of SQLIV detection. ii) We present SQL Injection Vulnerability Test Matrix (SQLTM) model, which is a three dimensional matrix, to generate the set of GTR. iii) Based on the GTR generated by the above steps, we propose a Multiple Phases Detection Approach (MPDA) to implement the dynamic generation of test cases and detection procedure control, and then we give its algorithms in detail. Experiment results show that our approach can improve the coverage, precision and efficiency of SQLIV detection by a comparison with two real products for enterprise projects.

Shi Fan - One of the best experts on this subject based on the ideXlab platform.

  • sql Injection Vulnerability detection based on webpage dom tree comparison
    Computer Engineering, 2012
    Co-Authors: Zhang Chen, Wang Yongyi, Wang Xiong, Shi Fan
    Abstract:

    (Abstract )Aiming at the problem that traditional SQL Injection Vulnerability detection results have low accurateness, this paper presents a detection method based on the comparison results of the Webpage's Document Object Model(DOM) tree. By simplifying the Web comparison algorithm based on node sequences, it reduces the number of node compare times, and accelerates the detection. A corresponding SQL Injection Vulnerability detection prototype system is designed and implied. Experimental results indicate that the system has high detection efficiency and

Zhenyu Zhang - One of the best experts on this subject based on the ideXlab platform.

  • ART4SQLi: The ART of SQL Injection Vulnerability Discovery
    IEEE Transactions on Reliability, 2019
    Co-Authors: Long Zhang, Donghong Zhang, Chenghong Wang, Jing Zhao, Zhenyu Zhang
    Abstract:

    SQL Injection (SQLi) is one of the chief threats to the security of database-driven Web applications. It can cause serious security issues such as authentication bypassing, privacy leakage, and arbitrary code execution. Dynamic testing techniques are used in SQLi Vulnerability discovery, which de-facto approach is to maintain a collection of elaborately designed user inputs (aka. attack payloads) and based on it to compose malicious SQL queries to Web applications. Such techniques are effective to reveal SQLi threats before an application is released, thus reducing the cost of manual analysis, monitoring or postdeployment of other defensive mechanisms. However, because of the diversity of SQLi attacks and the difficulty of SQLi discovery, the process to execute payloads can be costly, time-consuming, and even risky. In this paper, we approach from a test case prioritization perspective to give a more effective SQLi discovery proposal, which is based on adaptive random testing with the aim to successfully trigger an SQLi within limited attempts. To evaluate our method, we conduct an experiment using three extensively adopted open source vulnerable benchmarks. The experiment results indicate that our method ART4SQLi can effectively improve the conventional random testing approach on three common benchmarks by more than 26% in reducing the number of SQLi attempts before accomplishing a successful Injection.