A Solution for Automatically Malicious Web Shell and Web Application Vulnerability Detection

Taint analysis, a form of information-flow analysis, establishes whether values from untrusted methods and parameters may flow into security-sensitive operations. Taint analysis can detect many common vulnerabilities in Web applications, and so has attracted much attention from both the research community and industry. However, most static taint-analysis tools do not address critical requirements for an industrial-strength tool. Specifically, an industrial-strength tool must scale to large industrial Web applications, model essential Web-application code artifacts, and generate consumable reports for a wide range of attack vectors. We have designed and implemented a static Taint Analysis for Java (TAJ) that meets the requirements of industry-level applications. TAJ can analyze applications of virtually any size, as it employs a set of techniques designed to produce useful answers given limited time and space. TAJ addresses a wide variety of attack vectors, with techniques to handle reflective calls, flow through containers, nested taint, and issues in generating useful reports. This paper provides a description of the algorithms comprising TAJ, evaluates TAJ against production-level benchmarks, and compares it with alternative solutions.

Input sanitization and validation of user inputs are well-established protection mechanisms for microservice architectures against XML injection attacks (XMLi). The effectiveness of the protection mechanisms strongly depends on the quality of the sanitization and validation rule sets (e.g., regular expressions) and, therefore, security analysts have to test them thoroughly. In this demo, we introduce JCOMIX, a penetration testing tool that generates XMLi attacks (test cases) exposing XML vulnerabilities in front-end web applications. JCOMIX implements various search algorithms, including random search (traditional fuzzing), genetic algorithms (GAs), and the more recent co-operative, co-evolutionary algorithm designed explicitly for the XMLi testing (COMIX). We also show the results of an empirical study showing the effectiveness of JCOMIX in testing an open-source front-end web application.

Attackers can exploit vulnerabilities in web applications to commit malicious acts such as corrupting application functionality and Trojan horse implantation. For injection vulnerabilities in Web applications, existing methods are limited by the variety of programming languages and the difficulty in extracting semantic information to detect complex vulnerabilities. The paper proposes a pattern matching-based method for identifying injection vulnerabilities in Web applications, which transforms vulnerability identification into path matching in graphical databases by modifying the code property graph to enable it to handle more complex inter-functional relationships in Web applications. In this paper, we designed and implemented a prototype system, VulnFinder. By collecting 100 randomly selected Github high star open-source projects as the dataset for performance testing, we found 262 real vulnerabilities and conducted comparison experiments with static scanning tools RIPS and Cobra, VulnFinder far exceeded the comparison tools in terms of vulnerability determination accuracy, and in terms of the dataset VulnFinder was 94% accurate in determining vulnerabilities in the dataset. In scanning large projects, VulnFinder was approximately 21% more efficient than the comparable methodology tool RIPS.

Access control vulnerabilities that lead to elevated privileges are among the most dangerous vulnerabilities in Web applications. Most of the existing detection methods use dynamic or static analysis techniques alone, which suffer from high manual involvement, low automation, high leakage rate, low page coverage, and other deficiencies. To this end, this paper proposes a novel access control vulnerability detection method (DetAC) based on a sitemap model with global information representation. This method first constructs a static site-wide sitemap model based on the page link addresses in the Web application source code through static analysis techniques. After that, the application is logged in and executed dynamically with different role users. During this process, execution traces and request parameters are collected and converted into annotations to fill the corresponding edges of the static site-wide sitemap model. Then, the sitemap model with global information representation is obtained. This model can represent both the global control flow and data flow of the application. Then DetAC analyzes the role-based and user-based access control policies of the Web application based on the node reachability and annotated data features of the model. And according to the information such as role, user, and access resources, it generates attack vectors to achieve different roles and the same role of different users to access each other’s resources. Finally, access control vulnerabilities are detected based on the equivalence of the results obtained using attack vector access and normal access to the Web application server. DetAC was validated on five real open-source Web applications, and the results showed that DetAC successfully detected up to 12 access control vulnerabilities, which are more than those of the traditional seven tools. The dynamic analysis page coverage rate was significantly improved during the detection process, reaching an average of 91.37%.

Vulnerabilities in web applications are due to various factors. Failure to properly validated user input is one of the factors that led to run unauthorized code in these programs. Sqli and Xss are two common vulnerabilities in web applications, That is due to lack of proper input validation. Therefore, in this paper we study how to protect organizational websites of north khorasan in iran against Sqli and Xss vulnerabilities. We have analyzed eleven websites. Ten of which related to government organizations and one of them is from private organization. These Web sites have been tested with licenses taken from the relevant organizations.

Nowadays, software products are developed with security vulnerabilities due to bad coding. Vulnerability scanner tools automatically detect security vulnerabilities in web applications; thus, trustworthiness on the results of these tools is essential and, sometimes, the evaluation of their results is done manually or even empirically. This work presents a semi automated approach, based on fault injection techniques, to assess the efficacy of these tools. Three scanner tools were assessed with the presence of realistic software faults responsible for security vulnerabilities in web applications. Results show that the approach is effective and has the advantage of predicting security vulnerabilities through the fault injection techniques.

In most cases, web applications communicate with web services (SOAP and RESTful). The former act as a front-end to the latter, which contain the business logic. A hacker might not have direct access to those web services (e.g., they are not on public networks), but can still provide malicious inputs to the web application, thus potentially compromising related services. Typical examples are XML injection attacks that target SOAP communications. In this paper, we present a novel, search-based approach used to generate test data for a web application in an attempt to deliver malicious XML messages to web services. Our goal is thus to detect XML injection vulnerabilities in web applications. The proposed approach is evaluated on two studies, including an industrial web application with millions of users. Results show that we are able to effectively generate test data (e.g., input values in an HTML form) that detect such vulnerabilities.

According to OWASP top10 Application Security Risks [8, 9] SQL injection (SQLi) remains the most dangerous and most commonly exploited vulnerability in web applications. Thus, a lot of attentions are devoted by the scientific community for the development of SQLi verification tools. In this paper we focus on the development of an efficient, black box, SQLi vulnerability scanner to achieve an accurate detection. Our new approach is based on the use of structural similarity between rejection pages and their corresponding injection pages. A software prototype has been implemented and showed promising results as compared to well-known web application scanners.

To increase and maintain web application security, developers could use some different methods, one of them is static code analysis. This method could find security vulnerabilities inside a source code without the need of running the program. It could also be automated by using tools, which considered more efficient than manual reviews. One specific method which is commonly used in static code analysis is taint analysis. Taint analysis usually utilizes source code modeling to prepare the code for analysis process to detect any untrusted data flows into security sensitives computations. While this kind of analysis could be very helpful, static code analysis tool for Laravel-based web application is still quite rare, despite its popularity. Therefore, in this research, we want to know how static code (taint) analysis could be utilized to detect security vulnerabilities and how the projects (Laravel-based) should be modeled in order to facilitate this analysis. We then developed a static analysis tool, which models the application’s source code using AST and dictionary to be used as the base of the taint analysis. The tool first parsed the route file of Laravel project to get a list of controller files. Each file in that list would be parsed in order to build the source code representation, before actually being analyzed using taint analysis method. The experiments was done using this tool shows that the tools (with taint analysis) could detect 13 security vulnerabilities from 6 Laravel-based projects with one False Negative. An ineffective sanitizer was the suspected cause of this False Negative. This also shows that proposed modeling technique could be helpful in facilitating taint analysis in Laravel-based projects. For future development and studies, this tool should be tested with more Laravel and even other framework based web application with a wider range of security vulnerabilities.

As an important program analysis method, taint analysis technology has been widely used in the field of vulnerability detection. However, the existing dynamic taint analysis needs to design instruction-by-instruction taint propagation rules, which is expensive and has a high false positive rate for identifying the implicit control flow of the target program. In view of the above problems, this paper proposes a dynamic taint analysis method nnTaint based on neural network for the existing taint-guided fuzzing technology, which has the characteristics of low cost and low false positives. Comparing nnTaint with the existing taint analysis tools, the operation efficiency is improved by 10.9%-18.8% compared with the traditional taint analysis, which reduces the cost of the existing dynamic taint analysis technology to a certain extent. On the LAVA-M testing datasets and GNU binutils program, nnTaint improves the edge coverage by 4.7%-16% compared with the existing taint-guided fuzzing tools, effectively improving the implicit control flow recognition accuracy of the existing dynamic taint analysis. The results show that nnTaint can improve the efficiency and accuracy of existing dynamic taint analysis tools.

Application of pattern matching method for detecting faults in air handling unit system

In current information era, every small-scale industry, MNC's, Schools, and Colleges utilise Web Applications to promote their organizations and provide services to society. Web Applications have become a simple and vital medium for communicating with society. This leads to an increase in website utilization. Threats and cyber-attacks are increasing according to the rate of growth of Web Applications. Any organization must ensure the privacy and security of its users' data in order to maintain the company's and users' integrity. Web application vulnerabilities represent major security risks, making it critical to conduct extensive assessments and penetration testing to discover and remediate such flaws. The paper provides a complete overview of web application vulnerability assessment and penetration testing, emphasizing the need of proactive security measures in protecting sensitive data and preserving application integrity. This study aims to identify and categorize web application vulnerabilities (OWASPs) that may compromise the security of the application and result in user data breaches. The primary focus of this project is the security and privacy of user data. Integrating security practices into the software development life cycle through investigating real-world case studies and industry standards. Ultimately, our research adds to the growing body of knowledge in web application security, assisting organizations in creating resilient defenses against cyber threats.

This aim of this paper is to review the available literature on the recent trends in web application vulnerabilities. Google Scholar was used as the main search database for the review. Studies from the last few decades to analyse the trends in web application vulnerabilities over the years, especially since they are a relatively recent phenomenon. Using a set of criteria, approximately 400 scholarly works were initially selected and then reduced to a smaller number which was then explored in detail. It was found that there are a variety of forms in which attacks on web applications may take place. These attacks are becoming increasingly sophisticated with advancements in technology. Additionally, novel ways of attacking users have been developed in order to do so without tricking the user. Given the influx of technology and web applications in every aspect of life, this makes the user especially vulnerable. This means that web application developers must treat security as a priority rather than solely focus on usability.

Securing native XML database-driven web applications from XQuery injection vulnerabilities

Current web applications have many inherent vulnerabilities; in fact, in 2008, over 63% of all documented vulnerabilities are for web applications. While many approaches have been proposed to address various web application vulnerability issues, there has not been a study to investigate whether these vulnerabilities share any common properties. In this paper, we use an approach similar to the Goal-Question-Metric approach to empirically investigate four questions regarding open source web applications vulnerabilities: What proportion of security vulnerabilities in web applications can be considered as implementation vulnerabilities? Are these vulnerabilities the result of interactions between web applications and external systems? What is the proportion of vulnerable lines of code within a web application? Are implementation vulnerabilities caused by implicit or explicit data flows? The results from the investigation show that implementation vulnerabilities dominate. They are caused through interactions between web applications and external systems. Furthermore, these vulnerabilities only contain explicit data flows, and are limited to relatively small sections of the source code.