sql injection vulnerability Research Articles (Page 1)

This paper investigates the use of reinforcement learning algorithms for automated detection of SQL injection vulnerabilities in conversational AI agents that use APIs and databases. It was developed a Gymnasium-compatible environment called SQLiChatbotEnv and implemented a system based on Proximal Policy Optimisation (PPO), Advantage Actor-Critic (A2C) and REINFORCE methods to train an intelligent agent to detect and exploit various types of SQL injections in an automated manner. The created environment simulates realistic scenarios of interaction with vulnerable chatbots, including a multi-component action space, a reward system, and mechanisms for tracking the progress of vulnerability detection. A specialised reinforcement learning environment, SQLiChatbotEnv, simulates real-world scenarios of interaction with vulnerable chatbots and allows you to configure a chatbot environment with one of 5 major database management systems to choose from (MySQL, PostgreSQL, Microsoft SQL Server, Oracle, and SQLite). SQLiChatbotEnv also supports several key types of SQL injections, such as union-based attacks and error-based exploitation, and allows you to explore the database schema – to identify table and column names. The system allows you to use SQL query obfuscation in a message to a chatbot, which allows you to bypass basic security checks that may be present in a real system. Contextual framing allows you to use the natural integration of SQL injection into the conversation, for example, masking with phrases such as ‘I'm trying to understand...’, imitating the behaviour of a regular user. To encourage the agent to search for vulnerabilities efficiently, the system allows you to configure rewards and penalties for typical actions, such as discovering new information, data leakage, or using a SQL injection template that is incompatible with the actual database type. A comparative analysis of the performance of the three reinforcement learning algorithms over 2500 training episodes is conducted. The experimental results show that A2C demonstrates the best combination of convergence speed and learning stability, reaching a reward of 100 points in 30 episodes and a final performance of 232.82 ± 16.44 with the lowest coefficient of variation of 16.5%. PPO is characterised by the slowest convergence to high thresholds (221 episodes to a score of 150) and the highest variability of results (35.6%), but demonstrates the best ability to fully detect all types of vulnerabilities (87.4% of episodes). REINFORCE shows balanced intermediate results with a moderate convergence rate (145 episodes to a reward of 100), stability (coefficient of variation 21.4%) and high efficiency of vulnerability research (78.0% of episodes with all types of attacks). The practical significance of the work is to create an automated tool for testing the security of conversational AI agents. The results of the study demonstrate the prospects of using reinforcement learning for cybersecurity tasks and automating penetration testing processes.

Web applications and databases continue to face grave danger from SQL injection attacks, which can result in unauthorized access, data modification, and system compromise.This report discusses the methods attackers use to exploit SQL injection vulnerabilities and emphasizes the dangers of successful attacks, such as data leaks and system compromise.This research proposes a comprehensive system for detecting SQL injection attacks using concatenated Input Validation and Parsing Method (CIVP).The site used as experimental material is the Multimedia and Journal Services Site.Based on the results of forensic analysis on the Journal Services Site, there were several attacks in cyberspace, including using SQLMAP and Python.The system created has successfully detected SQL injection attacks.Based on the test results, it was found that the use of the method proposed in this study succeeded in making processing time 15.2% more efficient.Experiments carried out with the method proposed in this study succeeded in increasing the attack detection accuracy from 96-97% to 99.5% with a p-value of 0.008446.

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.

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

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.

SQL injection attack (SQLIA) is considered one of most threats used to attack web applications. Therefore, attackers used SQL injection vulnerability to gain ultimate access to databases that belong to applications and expose their sensitive information. Thus, attackers use SQL injections vulnerability to manipulate data also it could be used to take full control of the target machine. Accordingly, several methods were proposed in the literature to address this vulnerability widely because of its importance and high impact on the security of web applications. Thus, we propose a model to detect and prevent SQL injection attacks, which uses runtime validation to detect the occurrence of such attacks, our proposed model is adaptable to any existing system, with no need to modify the client or server and either no need to know web application source code. Furthermore, the modification independence is done by adding additional middleware between client and server. Thus, any check process is done on this middleware, and it is represented as a proxy that can do sanitize the inputs for detecting and preventing SQLIA. Furthermore, our proposed model accuracy reaches 86:6% for detecting and preventing SQLIA.

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.

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.

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

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.

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 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

On automated prepared statement generation to remove SQL injection vulnerabilities