On automated prepared statement generation to remove SQL injection vulnerabilities

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.

With1 the rapid development of computer network technology, people's life and work have become more and more dependent on the Internet. Consequent network security issues have also received much attention. At present, SQL injection attacks have become the main method of hacking. SQL injection vulnerabilities seriously threaten the security of WEB application systems. This article takes the PHP language as an example, introduces the reasons for the SQL injection in detail, conducts in-depth research on the common SQL injection attack methods. Based on the practical penetration testing practice, puts forward the SQL injection detection technology and how to avoid SQL injection vulnerability when writing WEB program code. This article provides detailed technical support for testing SQL injection and provides a powerful guarantee for WEB information system in SQL injection defense.

Web sites are dynamic, static, and most of the time a combination of both. Web sites need protection in their database to assure security. An SQL injection attacks interactive web applications that provide database services. These applications take user inputs and use them to create an SQL query at run time. In an SQL injection attack, an attacker might insert a malicious SQL query as input to perform an unauthorized database operation. Using SQL injection attacks, an attacker can retrieve or modify confidential and sensitive information from the database. It may jeopardize the confidentiality and security of Web sites which totally depends on databases. This report presents a “code reengineering” that implicitly protects the applications which are written in PHP from SQL injection attacks. It uses an original approach that combines static as well as dynamic analysis. [2] In this report, I mentioned an automated technique for moving out SQL injection vulnerabilities from Java code by converting plain text inputs received from users into prepared statements. [3]

With the development of web technology, the web application has become an important part of our lives. And because of the widely exposed feature of web application/services, any web security vulnerability will mostly be observed and be exploited by hackers. Many the traditional web security scanners [10, 13, 24] have low pages coverage and can't detect the SQL injection vulnerabilities exist in hidden web pages automatically. In this paper, we propose a mechanism of SQL injection vulnerability detection based on hidden web[16, 18] crawling and implement a detecting system with the purpose of raising the web page coverage and enhancing the SQL injection vulnerability detecting ability of web scanner. We combine authentication with the crawler model, and find SQL injection vulnerability by simulating web attacking and analyzing the data of response. In addition, we also did two experiments, one is to compare the coverage of our tool with other three tradition scanners [10, 13, 24] by detecting three common public web sites, and the result shows that the system we implemented can retrieve hidden web pages and its page coverage is larger than other three scanners; Another experiment shows that the ability to find SQL injection vulnerability in hidden web pages is enhanced. And the result of experiment 2 verified that our detecting system can find SQL injection vulnerabilities in hidden web pages automatically and have lower false positive.

Web applications are becoming an essential part of our everyday lives. Many\nof our activities are dependent on the functionality and security of these\napplications. As the scale of these applications grows, injection\nvulnerabilities such as SQL injection are major security challenges for\ndevelopers today. This paper presents the technique of automatic query\nsanitization to automatically remove SQL injection vulnerabilities in code. In\nour technique, a combination of static analysis and program transformation are\nused to automatically instrument web applications with sanitization code. We\nhave implemented this technique in a tool named ASSIST (Automatic and Static\nSQL Injection Sanitization Tool) for protecting Java-based web applications.\nOur experimental evaluation showed that our technique is effective against SQL\ninjection vulnerabilities and has a low overhead.\n

Web applications are an integral part of today's digital landscape, serving various functions from e-commerce to social networking. However, they are also prime targets for cyber-attacks, with SQL-Injection vulnerabilities posing a significant threat to their security. This project addresses the critical issue of SQL-Injection vulnerabilities in web applications by offering a comprehensive analysis, leveraging Python and classical machine learning algorithms such as Naïve Bayes. The research method employed in this project involves procuring real-world datasets, conducting data pre-processing, and using decision tree classifiers. These steps collectively provide an automated and scalable solution for identifying, understanding, and mitigating SQL-Injection riskslearning methods like Deep Neural Networks (DNN). A comprehensive comparative analysis of these algorithms has been carried out, assessing their performance based on accuracy metrics.

Web applications have become an integral part of the daily lives of millions of users. Unfortunately, web applications are also frequently targeted by attackers, and critical vulnerabilities such as cross-site scripting and SQL injection are still common. As a consequence, much effort in the past decade has been spent on mitigating web application vulnerabilities. Current techniques focus mainly on sanitization: either on automated sanitization, the detection of missing sanitizers, the correctness of sanitizers, or the correct placement of sanitizers. However, these techniques are either not able to prevent new forms of input validation vulnerabilities such as HTTP Parameter Pollution, come with large runtime overhead, lack precision, or require significant modifications to the client and/or server infrastructure. In this paper, we present IPAAS, a novel technique for preventing the exploitation of cross-site scripting and SQL injection vulnerabilities based on automated data type detection of input parameters. IPAAS automatically and transparently augments otherwise insecure web application development environments with input validators that result in significant and tangible security improvements for real systems. We implemented IPAAS for PHP and evaluated it on five real-world web applications with known cross-site scripting and SQL injection vulnerabilities. Our evaluation demonstrates that IPAAS would have prevented 83% of SQL injection vulnerabilities and 65% of cross-site scripting vulnerabilities while incurring no developer burden.

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.

Dependence on web applications is increasing very rapidly in recent time for social communications, health problem, financial transaction and many other purposes. Unfortunately, the presence of security weaknesses in web applications allows malicious user's to exploit various security vulnerabilities and become the reason of their failure. Currently, SQL Injection (SQLI) attacks exploit most dangerous security vulnerabilities in various popular web applications i.e. eBay, Google, Facebook, Twitter etc. Research on taint based vulnerability detection has been quite intensive in the past decade. However, these techniques are not free from false positive and false negative results. In this paper, we propose an approach to minimize false positive in SQLI vulnerability detection techniques using data mining concepts. We have implemented a prototype tool for PHP, MySQL technologies and evaluated it on six real world applications and NIST Benchmarks. Our evaluation and comparison results show that proposed technique detects SQLI vulnerabilities with low percentage of false positives.

In today’s present time, SQL injection has become a significant security threat over the web for diverse dynamic web applications and websites. SQL Injection may be a sort of associate injection attack that produces it doable to execute malicious SQL statements into an online application consisting of SQL information. Attackers use these SQL Injection Queries or Statements specified if an Internet site or an application hosted on web contain SQL vulnerabilities to bypass application security measures. The Attacker will even go around authentication associated with authorization of an online page or Internet application and might bypass these methods and retrieve the content of the whole SQL information of an online application. The purpose of the proposed system is to predict the occurrence of a SQL injection attack on a particular server where an application is deployed from a given supply at a particular point in time. This predictive experiment is managed using the JMeter tool. From network logs, you can now pre-measure, exclude choices, analyze, and feed machine learning models to predict SQLIA.KeywordsSQL injectionWeb applicationClassificationPredictionJMeter

A Tool Design for SQL injection vulnerability detection based on improved crawler

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.

Web applications have been widely adopted to access a myriad of services, regardless of their criticality and context. Applications developers have accelerated their efforts to meet the demands of a competitive and dynamic market for innovative products. Despite considerable efforts to detect and mitigate vulnerabilities in applications, their prevalence continues to increase, primarily due to the rapid pace of software development, which often prioritizes deployment speed, compromising security. This paper presents KAVe, a static analysis tool that leverages a multi-layer knowledge graph and a multi-agent system to detect web application vulnerabilities with high precision. This paper showcases KAVe's implementation and ability to identify SQL injection (SQLi) and cross-site scripting (XSS) vulnerabilities in real-world PHP applications.

Java applications have proliferated rapidly in recent years, and the security issue in the source code seems to bring a great threat which cannot be ignored. SQL injection and cross-site scripting vulnerabilities, path traversal, and so on are common Java security vulnerabilities. These vulnerabilities are usually caused by improper handling of user input and we call them input validation vulnerabilities [1].In this paper, we propose a Java source code analysis method based on the taint tracking. By means of vulnerability pattern match, we keep track of the introduction of tainted data and its propagation in different contexts, leading to comprehensive and accurate test results. Actually, we have applied this method to some of the open-source Java projects. Compared to other similar software, our technique presents good feasibility and superiority.

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.