Automated Security Testing Framework for Detecting SQL Injection Vulnerability in Web Application

Day after day, SQL Injection (SQLI) attack is consistently proliferating across the globe. According to Open Web Application Security Project (OWASP) Top Ten Cheat Sheet-2014, SQLI is at top in the list of online attacks. The cause of spread of SQLI is thought to be Unsecure Software Engineering. The Software Development process itself appears to look at security as an add-on to be checked and deployed towards the end of the software development lifecycle which leads to vulnerabilities in web applications. This paper is an attempt to integrate security in early stages of SDLC i.e. in design phase to mitigate SQLI vulnerability. How SQLI attack happens is illustrated. Threat Modeling is performed to mitigate the SQLI vulnerability.

Based on OWASP, code injection is one of the top lists of security risks. Structured Query Language (SQL) Injection is one of these types of attacks. SQL injection attack is an attack by spoofing the server to execute malicious code. The main object of this paper is to identify relevant works about deep learning methods to detect SQL-Injection on web applications. To achieve that, we conduct a survey review of the literature. In this study, we provide a review of 14 studies using deep learning algorithms to detect SQL Injection on web applications and it provides a comparison between them. Deep learning has great potential in threat intelligence detection.

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.

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.

Web Application now days are an important part of our daily life. Attackers try to attack these web applications to gain valuable information about the users using these web applications. Attackers exploit vulnerabilities in a web application to gain user data. This paper describes in detail the most harmful attacks that occurs on vulnerable web applications that are: SQL Injection Attack, Cross-Site Scripting Attack (XSS) and Command Injection Attack and helps you understand mechanism for 3 different types of attacks how they take place, what threats they possess and what could be done to avoid them.

Web applications have become central in the digital landscape, providing users instant access to information and allowing businesses to expand their reach. Injection attacks, such as SQL injection (SQLi), are prominent attacks on web applications, given that most web applications integrate a database system. While there have been solutions proposed in the literature for SQLi attack detection using learning-based frameworks, the problem is often formulated as a binary, single-attack vector problem without considering the prioritization and prevention component of the attack. In this work, we propose a holistic solution, SQLR34P3R, that formulates the SQLi attack as a multi-class, multi-attack vector, prioritization, and prevention problem. For attack detection and classification, we gathered 457,233 samples of benign and malicious network traffic, as well as 70,023 samples that had SQLi and benign payloads. After evaluating several machine-learning-based algorithms, the hybrid CNN-LSTM models achieve an average F1-Score of 97% in web and network traffic filtering. Furthermore, by using CVEs of SQLi vulnerabilities, SQLR34P3R incorporates a novel risk analysis approach which reduces additional effort while maintaining reasonable coverage to assist businesses in allocating resources effectively by focusing on patching vulnerabilities with high exploitability. We also present an in-the-wild evaluation of the proposed solution by integrating SQLR34P3R into the pipeline of known vulnerable web applications such as Damn Vulnerable Web Application (DVWA) and Vulnado and via network traffic captured using Wireshark from SQLi DNS exfiltration conducted with SQLMap for real-time detection. Finally, we provide a comparative analysis with state-of-the-art SQLi attack detection and risk ratings solutions.

Web application is the base of online businesses through the Internet. The emergence of COVID 19 forced almost every job to operate online so as to bridge the distance amongst individuals. The rapid increment in the needs of web application increases security threats on information and data. According to the Open Web Application Security Project, Structured Query Language Injection Attack (SQLIA) is a top security threat for web application. SQLIA inserts malicious code to gain access or to manipulate database information by cheating the server to bypass the code to the database, thereby causing a severe impact on web application. In this paper, permutation encoding method has been proposed to prevent SQLIA, which is based on encoding all database information using the proposed method. Initially, a special character is inserted to restrict the method from reversing. Subsequently, permutation encoding method is applied. Permutation refers to the method wherein the bit location is changed within three characters and then radix encoding is applied. Permutation is based on the primitive root value. Encoding has been used to hide permutation. The proposed method is implemented and tested using PHP and MySQL databases, where the proposal result has been compared with those of other proposal methods. The results with security analysis prove that the proposal method prevents SQLIA and protects database information.

SummaryThis work presents an extended and enhanced gray‐box combinatorial security testing methodology for SQL injection vulnerabilities in web applications. We propose multiple new attack grammars modelling SQLi attacks against MySQL‐compatible databases, each one targeting a different injection context. Additionally, these grammars are also dynamically refined at the beginning of each attack against an endpoint of a web application, as a further optimization of the used attack model by taking into account the specifics of the generated query of that endpoint. Our goal is to enhance existing combinatorial approaches for detecting SQL injection vulnerabilities. The newly developed methodology is implemented in a prototype security testing tool called SQLInjector+, which is an extension of an earlier prototype developed by us in prior work. This improved tool can attack (i.e. test) any web application that uses a MySQL‐compatible database management system. We evaluate our revised approach and improved prototype tool in a case study comprising of different kinds of web applications to which SQLi is a potential security threat. The case study contains the well‐known verification framework WAVSEP among other five real‐world web applications and one web application firewall. Our generated attack vectors, constructed via combinatorial methods applied to our improved and dynamically optimized attack grammars, are capable of injecting every known vulnerable endpoint in WAVSEP and also of finding new vulnerable parameters in some of the real‐world applications investigated in this paper. Our approach performs equally well or better when compared with existing state‐of‐art of SQL injection security testing tools (sqlmap, w3af, wapiti and fuzzdb) across all tested web applications in the case study.

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.

Structured Query Language injection (SQLi) attack is a code injection technique where malicious SQL statements are inserted into a given SQL database by simply using a web browser. Injected SQL commands can alter the database and thus compromise the security of a web application. In our previous work, we proposed an effective pattern recognition Neural Network (NN) model for detection and classification of the SQLi attacks. Our proposed model was built from: a Uniform Resource Locator (URL) generator, a URL classifier, and a NN model. The URL generator was implemented in order to generate thousands of malicious and benign URLs. The URL classifier was employed in order to identify each URL, which was generated by the URL generator, as either a benign URL or a malicious URL. The URL classifier also pigeonholed the malicious URLs into seven popular SQLi attack categories. The NN model includes n hidden layers with x input and y output nodes where the benign and malicious URLs were employed for training, validating, and testing phases. Addressing our previous captured results, our proposed pattern recognition NN model for the detection and classification of the SQLi attacks demonstrated a good performance in terms of accuracy, true-positive rate, and false-positive rate. In this paper, we stress test our previous proposal in order to prove the effectiveness of our proposed approach.

Internet has been dominating the world nearly a decade. Web application is known to be the most widespread platform of the internet especially when it comes to share resources, e-commerce services, education and business platforms. Since the usage of web applications are increasing dramatically, it’s becoming more vulnerable for security attacks. Each year, organizations facing many security attacks towards their web applications. Although many security practices and mitigations have been applying in web application, however there are still some security loophole issues can be found in web application. For instance, these loopholes can be referred as lack of secure coding (standards) implemented in web application, lack of formal security training approach for web developers and improper security testing for their web application. Besides, social engineering attacks also tremendously increasing each year. Many organizations were compromised through phishing attacks due to lack of awareness among users (employees). As a solution to overcome the issues, a research project will be carried out to implement a system called Nscanner to detect Structured Query Language injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities for web application. Moreover, the developer also will design a malware detection feature based on machine learning approach to detect malware found in attachments from emails in order to prevent malware phishing attacks.

In SQLIA, attacker injects an input in the query in order to change the structure of the query intended by the programmer and therefore, gain access to the data in the underlying database. Due to the significance of the stored data, web application's security against SQLIA is vital. In this paper we propose a tool that is capable of reporting the transformations needed to reinforce the security of a Java-based web application and its database against SQLIAs. This tool which is based on static analysis and runtime validation uses our new technique for detection and prevention of SQLIAs. In our technique user inputs in SQL queries are removed and some information is gathered in order to make the detection easier and faster at runtime. According to these information the tool reports the transformations needed and the location of the transformations in source code and therefore after applying the transformations the result would be a reinforced web application against SQLIAs.

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.

The powerful Open Web Application Security Project (OWASP)-Zed Attack Proxy (ZAP) tool secures web applications with extensive security testing. Its main goal is to find and fix web application vulnerabilities before they can be exploited. The goal is to improve web application security using OWASP ZAP scans and inspections. Simulating SQL injection and cross-site scripting attacks using the tool reveals an application's security flaws. OWASP ZAP automates testing to protect sensitive data and web application integrity. The purpose is to protect online applications from attacks to reduce security breaches and ensure industry compliance. Modern online applications' security and dependability depend on OWASP ZAP's systematic vulnerability discovery and mitigation. By enhancing scan performance and providing actionable security information, the experimental results show that the OWASP ZAP helps safeguard online applications and reduce the danger of cyberattacks. The objective is to enhance its skills to provide comprehensive, prompt, dependable security evaluations across various situations.

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%.