Application Vulnerabilities Research Articles

Artemis: Toward Accurate Detection of Server-Side Request Forgeries through LLM-Assisted Inter-procedural Path-Sensitive Taint Analysis

Server-side request forgery (SSRF) vulnerabilities are inevitable in PHP web applications. Existing static tools in detecting vulnerabilities in PHP web applications neither contain SSRF-related features to enhance detection accuracy nor consider PHP’s dynamic type features. In this paper, we present Artemis, a static taint analysis tool for detecting SSRF vulnerabilities in PHP web applications. First, Artemis extracts both PHP built-in and third-party functions as candidate source and sink functions. Second, Artemis constructs both explicit and implicit call graphs to infer functions’ relationships. Third, Artemis performs taint analysis based on a set of rules that prevent over-tainting and pauses when SSRF exploitation is impossible. Fourth, Artemis analyzes the compatibility of path conditions to prune false positives. We have implemented a prototype of Artemis and evaluated it on 250 PHP web applications. Artemis reports 207 true vulnerable paths (106 true SSRFs) with 15 false positives. Of the 106 detected SSRFs, 35 are newly found and reported to developers, with 24 confirmed and assigned CVE IDs.

THE PIVOTAL ROLE OF INFORMATION GATHERING IN WEB APPLICATION TESTING

Information gathering is a fundamental aspect of web application testing, ensuring security, functionality, and compliance with industry standards. This research delves into the methodologies employed in information gathering and their pivotal role in identifying vulnerabilities in web applications. Using both automated tools and manual techniques, this study highlights how structured reconnaissance enhances penetration testing, aids in security assessments, and fortifies application robustness. The results indicate that a well-executed information-gathering phase significantly improves the efficiency of web application security testing, reducing vulnerabilities by over 60% in controlled environments. Furthermore, this research provides a comparative analysis of traditional security testing methods versus structured reconnaissance approaches, demonstrating how proactive intelligence gathering enhances web application resilience against potential cyber threats. By adopting a systematic approach, security professionals can identify hidden attack surfaces, reduce misconfigurations, and enhance security controls, ultimately leading to more resilient web applications.

CipherScan: An Advanced Web Application Vulnerability Scanner for Enhanced Cybersecurity

The rapid development of cyber threats such as zero-day attacks and ransomware attacks has placed increasing pressure on proactive security practices. Traditional security products such as firewalls and antivirus software are not sufficient for efficient detection and neutralization of modern attacks. Intelligent, automated, and scalable security solutions need to be deployed by organizations to secure sensitive data and IT infrastructure. CipherSite has been engineered to integrate vulnerability scanning with real-time dynamic threat detection through artificial intelligence-powered analytics and ongoing security monitoring. This research provides an in-depth analysis of CipherSite, including its key features, deployment scenarios, and how it surpasses existing security solutions in detecting and preventing vulnerabilities. Keywords—Vulnerability scanner, threat detection, cybersecurity, SIEM integration, machine learning, automated risk assessment, CVE identification, network security, penetration testing, malware detection, compliance management, threat intelligence

Quantitative WEB Application Vulnerability Assessment using SAST Methodology

This paper presents a study on Static Application Security Testing (SAST) with a focus on the Snyk Code tool. SAST enables early detection and remediation of security vulnerabilities during software development, improving overall system security. The research introduces the General Application Vulnerability Rate (GAVR) model, which quantifies vulnerability risks based on the CVSS 3.1 framework. A case study using Snyk Code demonstrates the identification and assessment of security flaws, such as XSS and certificate validation issues. The study highlights the need for an integrated approach to security testing, emphasizing automation and structured vulnerability assessment to enhance software security. The GAVR model enhances traditional security evaluations by incorporating exploitability probabilities, offering a more dynamic risk assessment. The findings suggest that integrating SAST within the software development lifecycle significantly reduces security risks and improves remediation efficiency. By leveraging automation and systematic vulnerability quantification, this study underscores the importance of proactive security strategies to safeguard web applications against evolving threats.

"Advancements in Ethical Hacking Techniques and Tools: Strengthening Cybersecurity Resilience"

Ethical hacking, or white-hat hacking, has become a cornerstone of modern cybersecurity, offering proactive measures to identify and mitigate vulnerabilities in systems, networks, and applications. This comprehensive review explores the advancements in ethical hacking techniques and tools, emphasizing their critical role in bolstering cybersecurity defences. Key phases of ethical hacking include network reconnaissance, vulnerability assessment, exploitation, social engineering, web application testing, and wireless security testing. Each phase employs specialized techniques to uncover weaknesses, such as port scanning, penetration testing, and phishing simulations, which help organizations understand their security posture. It discusses the techniques used in each phase of ethical hacking and provides an overview of the tools employed by ethical hackers to assess and enhance the security posture of organizations. By leveraging these techniques and tools, ethical hackers play a crucial role in proactively identifying and addressing security weaknesses, thereby mitigating the risk of cyber threats and enhancing overall cybersecurity resilience.

Access Restricted: A Study of Broken Access Control Vulnerabilities

Broken access control vulnerability is ranked No.1 in OWASP Top 10 list in 2021. This means that it is the most commonly used exploited weakness in the web applications by attackers today. Because if an attacker can exploit this vulnerability, they can gain control rights and potentially compromise the entire web application. From that point forward, the attacker can execute various attacks depending on their objectives. That’s what makes it the most purposeful. In this research, we will reveal the security vulnerability of the access control in a web applications system. We will explore case studies of real-world attacks that leverage broken access control, providing a contextual understanding of the impact and implications of these vulnerabilities. Through this research, we aim to contribute to the ongoing efforts to enhance web application security and mitigate the risks associated with broken access control vulnerabilities. We will examine vulnerabilities in web applications that attackers can use to compromise access. Finally, we discuss the protection and security measures that should be taken against attackers who use this vulnerability. Received: 2 August 2024 | Revised: 15 January 2025 | Accepted: 18 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Sadeeq Jan: Methodology, Formal analysis, Supervision. Safi Ullah Khan: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration. Abdul Wahab: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration. Dr. Mohammad: Investigation, Project administration.

Design and Implementation of Distributed Web Application Vulnerability Assessment Tools for Securing Complex Microservices Environment

Design and Implementation of Distributed Web Application Vulnerability Assessment Tools for Securing Complex Microservices Environment

WEB VULNERABILITIES DETECTION USING A HYBRID MODEL OF CNN, GRU AND ATTENTION MECHANISM

The frequency of cyber-attacks has been rising in recent years due to the fact that startup developers have failed to overlook security issues in the core web services. This stated serious concerns about the security of the web. Therefore, this paper proposes a hybrid model built on the base of Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU) and an attention mechanism to detect vulnerabilities in application code. Particularly, the model can help detect attacks based on Structured Query Language Injection (SQLi), Cross-Site Scripting (XSS), and command injection. When using the dataset SXCM1, our model achieved 99.77%, 99.66% and 99.63% for training, validation and testing, respectively. The results obtained on data from the DPU-WVD dataset are even better because it was 99.97%, 99.98% and 99.99% for training, validation and testing, respectively. These results significantly outperform the state-of-the-art models and can strongly identify vulnerabilities in web applications. Through training, on both the SXCM1 and DPU-WVD datasets, the model achieved an accuracy rate of 99.99%. The results show that this combination model is highly effective at recognizing three vulnerability categories and surpasses cutting-edge models that usually specialize in just one type of vulnerability detection.

UniEmbed: A Novel Approach to Detect XSS and SQL Injection Attacks Leveraging Multiple Feature Fusion with Machine Learning Techniques

Abstract Web applications are essential in the digital age, but their security vulnerabilities expose sensitive data and organizational integrity to sophisticated attacks. Among the most prevalent and damaging vulnerabilities in web applications are cross-site scripting (XSS) and SQL injection attacks. In this paper, we introduce UniEmbed, a unified approach for detecting XSS and SQL injection attacks using machine learning classifiers. This novel approach leverages natural language processing techniques, combining features from Word2Vec, the Universal Sentence Encoder (USE), and FastText to extract meaningful data from web applications. Extensive experiments were conducted using various machine learning classifiers on three benchmark datasets to evaluate the performance of the unified detection approach, demonstrating exceptional results. Experimental results demonstrate the superior performance of the MLP classifier. For the XSS attack dataset, the MLP classifier achieved an accuracy of 0.9982 and an F1-score of 0.9983, with minimal false positives and false negatives. Similarly, the hard voting classifier yielded the same outstanding results. For SQL injection attacks, the MLP classifier maintained exceptional performance, achieving an F1-score of 0.9980 and accuracy rates exceeding 0.9980 across two datasets. The classifier effectively minimized false positives and false negatives. The ROC curves further corroborate the effectiveness of the proposed method, indicating high true positive rates and low false positive rates. Furthermore, comparative analysis showed that the UniEmbed method consistently outperformed individual feature extraction methods across all classifiers. These findings indicate that the proposed UniEmbed method, particularly when combined with the MLP classifier, is highly effective in detecting both XSS and SQL injection attacks, making it a promising approach for enhancing web application security.

SQL-Injection Vulnerability Scanning Tool for Automatic Creation of SQL-Injection Attacks

This research introduces an advanced automated scanning tool for detecting and analyzing SQL injection vulnerabilities in web applications, addressing the critical need for robust security testing mechanisms in modern web development. The proposed tool employs sophisticated dynamic analysis techniques combined with machine learning algorithms to automatically generate, execute, and validate SQL injection attack vectors. By implementing a multilayered detection approach, the system first identifies potential injection points through comprehensive input parameter analysis, followed by intelligent payload generation based on database fingerprinting and contextual analysis. The tool incorporates both syntactic and semantic analysis of database responses to effectively distinguish between successful and failed injection attempts, significantly reducing false positives. Advanced features include automated bypass techniques for common defensive mechanisms, support for multiple database management systems (MySQL, PostgreSQL, Oracle, and MS-SQL), and intelligent error pattern recognition. Experimental evaluation conducted across 100 diverse web applications demonstrated a 95% detection rate for known vulnerabilities and an 85% success rate in identifying previously undiscovered SQL injection vulnerabilities. The tool's automated approach significantly reduces the time and expertise required for security testing, making it valuable for both security professionals and development teams implementing secure coding practices. Additionally, the system generates detailed vulnerability reports with remediation recommendations, facilitating efficient security patch implementation. Performance analysis shows that the tool can scan complex web applications with minimal impact on system resources while maintaining high accuracy in vulnerability detection.

Blending Static and Dynamic Analysis for Web Application Vulnerability Detection: Methodology and Case Study

Blending Static and Dynamic Analysis for Web Application Vulnerability Detection: Methodology and Case Study

Detecting Security Vulnerabilities in Web Applications: A Proposed System

Detecting Security Vulnerabilities in Web Applications: A Proposed System

Intelligent Platform for Automating Vulnerability Detection in Web Applications

In a world increasingly dependent on technology and in an era where connectivity is omnipresent, Web applications have become an essential part of our everyday life. The evolution of these applications, combined with the exponential increase in the number of users, has brought with it not only convenience but also significant challenges in terms of security. Ensuring the security of Web applications and their data is increasingly a priority for companies, although many companies lack the know-how, time, and money to do so. This research project studied and developed a system with the aim of automating the process of detecting vulnerabilities in Web applications by exploiting the benefits of the interoperability of the two forms of automation of the tool selected to carry out this analysis. The developed solution is low-cost and requires very little user intervention. In order to validate and evaluate the developed platform, experiments were carried out on applications with different types of vulnerabilities known in advance and on real applications. It is essential to guarantee the security of Web applications, and the developed system proved capable of automating the detection of vulnerability risks and returning the results in a relatively simple way for the user.

Web-based Vulnerability Analysis and Detection

Background: In today’s digital world, web-based applications are widely used across organizations, making them a primary target for breaches, hacking attempts, data theft, and unauthorized access. Ensuring robust security through vulnerability analysis and detection is critical to mitigate emerging threats and prevent potential exploits. Objective: This research aims to develop and demonstrate a novel approach to web-based vulnerability assessment by combining advanced automated scanning tools with human expertise. The study introduces a modular, flexible, and efficient scanning tool capable of detecting critical vulnerabilities in web applications. Methods: The proposed tool is built using Python and Selenium and employs a modular architecture with regular expression-based detection techniques. The scanner identifies vulnerabilities such as SQL injection, cross-site scripting (XSS), file inclusions, command injection, and more. A ranking framework is introduced to prioritize fixes based on exploiting potential, severity, and patch availability. The tool's performance was evaluated on real-world web applications to assess its accuracy, detection speed, and effectiveness compared to traditional methods. Results: Experimental results highlight significant improvements in vulnerability detection accuracy and scanning speed. The tool successfully identified a broad range of vulnerabilities, including complex and dynamic ones, outperforming traditional scanning techniques. The modular design and automation enabled faster and more flexible vulnerability detection across diverse web platforms. Conclusion: This research provides a holistic and practical approach to web-based vulnerability assessment, combining advanced automated scanning with human analysis for optimal results. The introduced tool offers a reliable solution for detecting and prioritizing vulnerabilities, empowering organizations to enhance their cybersecurity defenses. By improving detection accuracy and efficiency, this study contributes to advancing web security practices and provides actionable insights for organizations navigating the evolving digital landscape.

A method for finding web application vulnerabilities using the ChatGPT API

This paper presents a method for automating web application testing using the ChatGPT API, designed to help ethical hackers identify vulnerabilities. The goal of the research is to develop an approach that improves the efficiency and accuracy of pentesting, focusing on the automation of processes that are traditionally performed manually. The proposed method is based on the capabilities of the GPT model to generate test requests and analyze server responses, which allows detecting potential vulnerabilities without the need for detailed analysis of the source code. The presented results demonstrate the advantages of using GPT models for generating complex test scenarios and analyzing web application responses, which helps identify potential threats. The results of the experiments showed an increase in the accuracy of vulnerability detection by 15-20% and a reduction in testing time by 35% compared to traditional methods. The proposed approach is promising for implementation in the practice of ethical hacking and cyber security.

Identifying and Mitigating Web Application Vulnerabilities: A Comparative Study of Countermeasures and Tools

In the current age of technology, web applications and websites have experienced significant growth. This expansion has made their security a critical area of research. Web applications offer benefits, which makes user’s lives easier. In this paper, common web application vulnerabilities and effective strategies to mitigate the vulnerabilities are identified using a comparative study of countermeasures and open-source web application vulnerability assessment tools. Specifically, the top ten web application vulnerabilities and their countermeasures are investigated. Accordingly, several open-source vulnerability assessment tools are also introduced. The review highlights that with the developments and deployments of web applications on the internet, users are chased by a remarkable number of cyber-attacks. Attackers take advantage of available vulnerabilities in a web application or website, such as SQL injections, cross-site scripting, and broken authentications. This paper concludes by providing the best practices to mitigate cyber-attacks on web applications and suggests future directions for enhancing vulnerability assessment through machine learning techniques

Using Generative AI Models to Support Cybersecurity Analysts

One of the tasks of security analysts is to detect security vulnerabilities and ongoing attacks. There is already a large number of software tools that can help to collect security-relevant data, such as event logs, security settings, application manifests, and even the (decompiled) source code of potentially malicious applications. The analyst must study these data, evaluate them, and properly identify and classify suspicious activities and applications. Fast advances in the area of Artificial Intelligence have produced large language models that can perform a variety of tasks, including generating text summaries and reports. In this article, we study the potential black-box use of LLM chatbots as a support tool for security analysts. We provide two case studies: the first is concerned with the identification of vulnerabilities in Android applications, and the second one is concerned with the analysis of security logs. We show how LLM chatbots can help security analysts in their work, but point out specific limitations and security concerns related to this approach.

Comparative Analysis of Machine Learning Algorithms for Cross-Site Scripting (XSS) Attack Detection

Cross-Site Scripting (XSS) attacks pose a significant cybersecurity threat by exploiting vulnerabilities in web applications to inject malicious scripts, enabling unauthorized access and execution of malicious code. Traditional XSS detection systems often struggle to identify increasingly complex XSS payloads. To address this issue, this research evaluated the efficacy of Machine Learning algorithms in detecting XSS threats within online web applications. The study conducts a comprehensive comparative analysis of XSS attack detection using four prominent Machine Learning algorithms, which consist of Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). This research utilizes a comparative methodology to assess the selected Machine Learning algorithms by analyzing their performance metrics, including confusion matrix, 10-fold cross-validation, and assessment of training time to thoroughly evaluate the models. By exploring dataset characteristics and evaluating the performance metrics of each selected algorithm, the study determined the most robust Machine Learning solution for XSS detection. Results indicate that Random Forest is the top performer, achieving 99.93% accuracy and balanced metrics across all criteria evaluated. These findings will significantly enhance web application security by providing reliable defenses against evolving XSS threats.

Identification of SQL Injection Security Vulnerabilities in Web applications Based on Binary Code Similarity

The existing SQL injection security vulnerability identification technology for Web applications has inherent flaws, which are relatively passive in defense methods, and cannot deal with increasingly changeable attack methods. In order to improve the accuracy of SQL injection security vulnerability identification of Web applications, this paper uses an improved skip-gram model to realize unsupervised learning of the embedding process, converts the information related to program functions contained in the vertices of the basic block into feature vectors to obtain the ACFG vector of the basic block, and measures the similarity of binary functions by evaluating the similarity of feature vectors. The experimental results show that the technical processing route proposed in this paper can effectively compare binary functions with different architectures and optimization levels, and use the advantages of neural networks to obtain higher accuracy and better analysis efficiency, thereby effectively improving the identification effect of SQL injection security vulnerabilities in Web applications. Therefore, it can play a certain role in the security management of subsequent Web applications.

Timing Side-Channel Mitigation via Automated Program Repair

Side-channel vulnerability detection has gained prominence recently due to Spectre and Meltdown attacks. Techniques for side-channel detection range from fuzz testing to program analysis and program composition. Existing side-channel mitigation techniques repair the vulnerability at the IR/binary level or use runtime monitoring solutions. In both cases, the source code itself is not modified, can evolve while keeping the vulnerability, and the developer would get no feedback on how to develop secure applications in the first place. Thus, these solutions do not help the developer understand the side-channel risks in her code and do not provide guidance to avoid code patterns with side-channel risks. In this article, we present Pendulum , the first approach for automatically locating and repairing side-channel vulnerabilities in the source code, specifically for timing side channels. Our approach uses a quantitative estimation of found vulnerabilities to guide the fix localization, which goes hand-in-hand with a pattern-guided repair. Our evaluation shows that Pendulum can repair a large number of side-channel vulnerabilities in real-world applications. Overall, our approach integrates vulnerability detection, quantization, localization, and repair into one unified process. This also enhances the possibility of our side-channel mitigation approach being adopted into programmingenvironments.