Research on Vulnerability Detection for Software Based on Taint Analysis

With the rapid development of the Internet, many things of people can be done online, and the Internet has been integrated into people's daily life. Web applications widely used in all walks of life are convenient for people to use, but also bring some security problems. Cross-Site Scripting(XSS) vulnerability detection method based on dynamic analysis is an effective XSS vulnerability detection method, which simulates real attack behavior to conduct penetration test on Web applications. However, there are still some problems such as low detection coverage, low detection efficiency and low detection accuracy. Based on this, this paper expounds the research status and background of XSS attack detection, puts forward the necessity of using machine learning algorithm for XSS attack detection, applies SVM algorithm to XSS detection, analyzes and extracts the features of XSS samples, selects radial basis kernel function for nonlinear SVM training, and verifies the generated detection model. It is found that the SVM model in this paper is also superior to the comparison algorithm in accuracy and false alarm rate.

At present, Web application vulnerability safety problems widely exist because of the lack of effective validation and filtering mechanism for untrusted user data. This paper shows the design of the prototype system against SQL injection and cross-site scripting vulnerability. The main steps of the detection are as follows: construct the taint dependency graph for the program by the static analysis of source code; use finite state automata to represent the value of tainted string; verify whether the program has effective safe handling for the user input by matching with the attack pattern and then implement the prototype system for automatic detection on the Java Web program vulnerability based on taint dependency analysis. The experimental results show that the system is comprehensive and accurate for the detection of related vulnerabilities.

While because the range of web users have increased exponentially, thus has the quantity of attacks that decide to use it for malicious functions. The vulnerability that has become usually exploited is thought as cross-site (XSS). Cross-site Scripting (XSS) refers to client-side code injection attack whereby a malicious user will execute malicious scripts (also usually stated as a malicious payload) into a legitimate web site or web based application. XSS is amongst the foremost rampant of web based application vulnerabilities and happens once an internet based application makes use of un-validated or un-encoded user input at intervals the output it generates. In such instances, the victim is unaware that their data is being transferred from a website that he/she trusts to a different site controlled by the malicious user. In this paper we shall focus on type 1 or cross-site scripting. With non-persistent cross-site scripting, malicious code or script is embedded in a Web request, and then partially or entirely echoed (or reflected) by the Web server without encoding or validation in the Web response. The malicious code or script is then executed in the client’s Web browser which could lead to several negative outcomes, such as the theft of session data and accessing sensitive data within cookies. In order for this type of cross-site to be successful, a malicious user must coerce a user into clicking a link that triggers the non-persistent cross-site attack. This is usually done through an email that encourages the user to click on a provided malicious link, or to visit a web site that is fraught with malicious links. In this paper it will be discussed and elaborated as to how attack surfaces related to type 1 or cross-site scripting attack shall be reduced using secure development life cycle practices and techniques

Vulnerability detection through cross-modal feature enhancement and fusion

BinAIV: Semantic-enhanced vulnerability detection for Linux x86 binaries

Cross-program propagation of tainted data (such as sensitive information or user input) in an interactive IoT system is listed among the OWASP IoT top 10 most critical security risks. When programs run on distinct devices, as it occurs in IoT systems, they communicate through different channels in order to implement some functionality. Hence, in order to prove the overall system secure, an analysis must consider how these components interact. Standard taint analyses detect if a value coming from a source (such as methods that retrieve user input or sensitive data) flows into a sink (typically, methods that execute SQL queries or send data into the Internet), unsanitized (that is, not properly escaped). This work devises a cross-program taint analysis that leverages an existing intra-program taint analysis to detect security vulnerabilities in multiple communicating programs. The proposed framework has been implemented above the intra-program taint analysis of the Julia static analyzer. Preliminary experimental results on multi-program IoT systems, publicly available on GitHub, show that the technique is effective and detects inter-program flows of tainted data that could not be discovered by analyzing each program in isolation.

The explosive growth of vulnerabilities poses a significant threat to the security of software systems. While various deep-learning-based vulnerability detection methods have emerged, they primarily rely on semantic features extracted from a single code representation structure, which limits their ability to detect vulnerabilities hidden deep within the code. To address this limitation, we propose S2FVD, short for Sequence and Structure Fusion-based Vulnerability Detector, which fuses vulnerability-indicative features learned from the multiple views of the code for more accurate vulnerability detection. Specifically, S2FVD employs either well-matched or carefully extended neural network models to extract vulnerability-indicative semantic features from the token sequence, attributed control flow graph (ACFG) and abstract syntax tree (AST) representations of a function, respectively. These features capture different perspectives of the code, which are then fused to enable S2FVD to accurately detect vulnerabilities that are well-hidden within a function. The experiments conducted on two large vulnerability datasets demonstrated the superior performance of S2FVD against state-of-the-art approaches, with its accuracy and F1 scores reaching 98.07% and 98.14% respectively in detecting the presence of vulnerabilities, and 97.93% and 97.94%, respectively, in pinpointing specific vulnerability types. Furthermore, with regard to the real-world dataset D2A, S2FVD achieved average performance gains of 6.86% and 14.84% in terms of accuracy and F1 metrics, respectively, over the state-of-the-art baselines. This ablation study also confirms the superiority of fusing the semantics implied in multiple distinct code views to further enhance vulnerability detection performance.

Vulnerability is one of the root causes of network intrusion. An effective way to mitigate security threats is to discover and patch vulnerabilities before an attack. Traditional vulnerability detection methods rely on manual participation and incur a high false positive rate. The intelligent vulnerability detection methods suffer from the problems of long-term dependence, out of vocabulary, coarse detection granularity and lack of vulnerable samples. This paper proposes an automated and intelligent vulnerability detection method in source code based on the minimum intermediate representation learning. First, the sample in the form of source code is transformed into a minimum intermediate representation to exclude the irrelevant items and reduce the length of the dependency. Next, the intermediate representation is transformed into a real value vector through pre-training on an extended corpus, and the structure and semantic information are retained. Then, the vector is fed to three concatenated convolutional neural networks to obtain high-level features of vulnerability. Last, a classifier is trained using the learned features. To validate this vulnerability detection method, an experiment was performed. The empirical results confirmed that compared with the traditional methods and the state-of-the-art intelligent methods, our method has a better performance with fine granularity.

Web technologies are typically built with time constraints and security vulnerabilities. Automatic software vulnerability scanners are common tools for detecting such vulnerabilities among software developers. It helps to illustrate the program for the attacker by creating a great deal of engagement within the program. SQL Injection and Cross-Site Scripting (XSS) are two of the most commonly spread and dangerous vulnerabilities in web apps that cause to the user. It is very important to trust the findings of the site vulnerability scanning software. Without a clear idea of the accuracy and the coverage of the open-source tools, it is difficult to analyze the result from the automatic vulnerability scanner that provides. The important to do a comparison on the key figure on the automated vulnerability scanners because there are many kinds of a scanner on the market and this comparison can be useful to decide which scanner has better performance in term of SQL Injection and Cross-Site Scripting (XSS) vulnerabilities. In this paper, a method by Jose Fonseca et al, is used to compare open-source automated vulnerability scanners based on detection coverage and a method by Yuki Makino and Vitaly Klyuev for precision rate. The criteria vulnerabilities will be injected into the web applications which then be scanned by the scanners. The results then are compared by analyzing the precision rate and detection coverage of vulnerability detection. Two leading open source automated vulnerability scanners will be evaluated. In this paper, the scanner that being utilizes is OW ASP ZAP and Skipfish for comparison. The results show that from precision rate and detection rate scope, OW ASP ZAP has better performance than Skipfish by two times for precision rate and have almost the same result for detection coverage where OW ASP ZAP has a higher number in high vulnerabilities.

Code vulnerability detection is a crucial approach to ensuring software security, aiming to automatically identify potential security vulnerabilities in source code. However, existing static vulnerability detection methods often face challenges such as feature loss and an insufficient expressive power when extracting program features. To address these issues, in this paper, we propose a source code vulnerability detection method based on joint graph and multimodal feature fusion. Innovatively, we construct a joint graph that integrates multiple program dependencies and semantic edges to achieve more comprehensive feature extraction. Additionally, by combining Graph Attention Networks (GATs) with the Transformer architecture, both structural and sequential features of code snippets are captured, further enhancing the model’s expressive capabilities. Finally, we introduce pre-fusion and post-fusion strategies to fully integrate multimodal features, thereby improving the accuracy and performance of vulnerability detection. Experimental results on the SARD dataset demonstrate the method’s excellent performance in detecting various types of vulnerabilities, achieving an F1 score of 85.20% and an accuracy of 86.50%. On the Real-Vul real-world dataset, the method achieves an F1 score of 73.9% and an accuracy of 86.50%. The detection results exhibit remarkable stability, ensuring a reliable and consistent performance. Overall, the proposed method surpasses the performance of existing mainstream detection approaches.

Cross-site scripting (XSS) vulnerabilities are significant threats to web applications. The number of XSS vulnerabilities reported has increased annually for the past three years, posing a considerable challenge to web application maintainers. Black-box scanners are mainstream tools for security engineers to perform penetration testing and detect XSS vulnerabilities. Unfortunately, black-box scanners rely on crawlers to find input points of web applications and cannot guarantee all input points are tested. To this end, we propose a grey-box fuzzing method based on reinforcement learning, which can detect reflected and stored XSS vulnerabilities for Java web applications. We first use static analysis to identify potential input points from components (i.e., Java code, configuration files, and HTML files) of the Java web application. Then, an XSS vulnerability payload generation method is proposed, which is used together with the reinforcement learning model. We define the state, action, and reward functions of three reinforcement learning models for XSS vulnerability detection scenarios so that the fuzz loop can be performed automatically. To demonstrate the effectiveness of the proposed method, we compare it against four state-of-the-art web scanners. Experimental results show that our method finds all XSS vulnerabilities and has no false positives.

To facilitate the detection of various vulnerabilities, there are many different tools (scanners) that can help analyze the security of web applications and facilitate the development of their protection. But these tools for the most part can only identify problems, and they are not capable of fixing them. Therefore, the knowledge of the security developer is a key factor in building a secure Web resource. To resolve application security problems, developers must know all the ways and vectors of various attacks in order to be able to develop various protection mechanisms. This review discusses two of the most dangerous vulnerabilities in the field of Web technologies: SQL injections and XSS attacks (cross-site scripting – XSS), as well as specific cases and examples of their application, as well as various approaches to identifying vulnerabilities in applications and threat prevention. Cross-site scripting as well as SQL-injection attacks are related to validating input data. The mechanisms of these attacks are very similar, but in the XSS attacks the user is the victim, and in the SQL injection attacks, the database server of the Web application. In XSS attacks, malicious content is delivered to users by means of a client-side programming language such as JavaScript, while using SQL injection, the SQL database query language is used. At the same time, XSS attacks, unlike SQL injections, harm only the client side leaving the application server operational. Developers should develop security for both server components and the client part of the web application.

The most severe problem in cross-programming languages is feature extraction due to different tokens in different programming languages. To solve this problem, we propose a cross-programming-language vulnerability detection method in this paper, IRC-CLVul, based on intermediate representation and combined features. Specifically, we first converted programs in different programming languages into a unified LLVM intermediate representation (LLVM-IR) to provide a classification basis for different programming languages. Afterwards, we extracted the code sequences and control flow graphs of the samples, used the semantic model to extract the program semantic information and graph structure information, and concatenated them into semantic vectors. Finally, we used Random Forest to learn the concatenated semantic vectors and obtained the classification results. We conducted experiments on 85,811 samples from the Juliet test suite in C, C++, and Java. The results show that our method improved the accuracy by 7% compared with the two baseline algorithms, and the F1 score showed a 12% increase.

CERT issues ‘cross-site scripting’ warning

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.