The ISC International Journal of Information Security

Abstract Detecting security vulnerabilities in open-source software is a critical task that is highly regarded in the related research communities. Several approaches have been proposed in the literature for detecting vulnerable code and identifying classes of vulnerabilities. However, there is still room to improve the explanation of the root causes of detected vulnerabilities by locating vulnerable statements and discovering the paths that lead to the activation of the vulnerability. While frameworks like SliceLocator offer explanations by identifying vulnerable paths, they rely on rule-based sink identification that limits their generalisation. In this paper, we introduce VulPathFinder, an explainable vulnerability path discovery framework that enhances SliceLocator’s methodology by utilising a novel Graph Neural Network (GNN) model for detecting sink statements, rather than relying on predefined rules. The proposed GNN captures semantic and syntactic dependencies to find potential sink points (PSPs), which are candidate statements where vulnerable paths end. After detecting PSPs, program slicing can be used to extract potentially vulnerable paths, which are then ranked by feeding them back into the target graph-based detector. Ultimately, the most probable path is returned, explaining the root cause of the detected vulnerability. We demonstrate the effectiveness of the proposed approach by performing evaluations on a benchmark of the buffer overflow CWEs from the SARD dataset, providing explanations for the corresponding detected vulnerabilities. The results show that VulPathFinder outperforms both the original SliceLocator and GNNExplainer (as a general GNN explainability tool) in discovering vulnerability paths to identified PSPs.

Abstract One of the challenging issues for software developers is detecting vulnerabilities at different development stages. Security researchers are always seeking new methods to detect vulnerabilities more precisely in a short time. While there are many static and dynamic methods for detecting and discovering vulnerabilities, many of these approaches come with a high computational cost, which leads to inefficiencies, particularly in large-scale codebases. In recent years, deep learning has gained prominence in extracting vulnerability features from code without requiring direct intervention from cybersecurity experts. This paper proposes a multi-class vulnerability detection scheme at both the line-level (LLVD) and function-level (FLVD) using graph neural networks, based on node-level and graph-level prediction models, respectively. Moreover, by combining LLVD as a fine-grained approach with FLVD as a coarse-grained one, we propose a multi-granularity scheme called Function/Line-Level Vulnerability Detection (FLLVD) scheme. More specifically, it uses FLVD to detect the type of vulnerability while employing LLVD to identify its location in the source code. Our scheme’s variants work with any abstraction graph extracted from incoming source code, such as Data Dependency Graph (DDG) and Program Dependency Graph (PDG). We evaluate our schemes using both man-made and real-world datasets: SARD and BigVul. Particularly, LLVD and FLLVD achieve performance gains of 0.90 and 0.94, respectively, in terms of F1 metrics for a subset of SARD with 20 vulnerability types. In contrast, for the combination of SARD and BigVul with 6 vulnerability types, LLVD and FLLVD have F1 scores of approximately 0.76 and 0.82, respectively.

Abstract In recent years, artificial intelligence has had a conspicuous growth in almost every aspect of life. One of the most applicable areas is security code review, in which a lot of AI-based tools and approaches have been proposed. Recently, ChatGPT has caught a huge amount of attention with its remarkable performance in following instructions and providing a detailed response. Regarding the similarities between natural language and code, in this paper, we study the feasibility of using ChatGPT for vulnerability detection in Python source code. Toward this goal, we feed an appropriate prompt along with
vulnerable data to ChatGPT and compare its results on two datasets with the results of three widely used Static Application Security Testing tools (Bandit, Semgrep, and SonarQube). We implement different kinds of experiments with ChatGPT and the results indicate that ChatGPT reduces the false positive and false negative rates and has the potential to be used for Python source code vulnerability detection.

Abstract At the present period of time, web applications are growing constantly in the whole society with the development of communication technology. Since the utilization of WWW (World Wide Web) expanded and increased since it provides many services, such as sharing data, stay connected and other services. As a consequence, these numerous numbers of web application users susceptible to cybersecurity breaches in order to steal sensitive information or crashing the users’ systems, etc. Particularly, the most common vulnerability todays in web applications are the Cross-Site Scripting (XSS) attack.
Furthermore, online cyber attacks utilizing cross-site scripting were responsible for 40% of the attack instances that struck enterprises in North America and Europe in the 2019. Therefore, cross-site scripting is a form of an injection that targets both vulnerable and non-vulnerable websites, for the injection of malicious scripts. Cross-site scripting XSS operates by directing users to a vulnerable website that contains malicious JavaScript. Then, when malicious code runs in a victim’s browser, the attacker has complete control over how they interact with the application. In order to protect website or prevent the XSS, must know the application complexity and the way it handles data must be known so it could be controlled by the user. However, Detecting XSS effectively is still a work in progress and XSS is considered a gateway for various attacks. However in this paper, we will introduce the XSS attack and the forms of XSS as review paper. In addition, the methods and techniques that help to detect cross site scripting (XSS) attacks.

Abstract This paper explores the algebraic matching approach for detection of vulnerabilities in binary codes. The algebraic programming system is used for implementing this method. It is anticipated that models of vulnerabilities and programs to be verified are presented as behavior algebra and action language specifications. The methods of algebraic matching are based on rewriting rules and techniques with usage of conditional rewriting. This process is combined with symbolic modeling that gives a possibility to provide accurate detection of vulnerabilities. The paper provides examples of formalization of vulnerability models and translation of binary codes to behavior algebra expressions.