Deep learning-based vulnerability detection models have received widespread attention; however, these models are susceptible to adversarial attack, and adversarial examples are a primary research direction to improve the robustness of the models. The...
Software vulnerabilities present a significant cybersecurity threat, particularly as software code grows in size and complexity. Traditional vulnerability-mining techniques face challenges in keeping pace with this complexity. Fuzzing, a key automate...
With the increasing complexity of software systems, the number of security vulnerabilities contained within software has risen accordingly. The existing shift-left security concept aims to detect and fix vulnerabilities during the software developmen...
Internet-based distributed systems dominate contemporary software applications. To enable these applications to operate securely, software developers must mitigate the threats posed by malicious actors. For instance, the developers must identify vuln...
Rust’s growing popularity in high-integrity systems requires automated vulnerability detection in order to maintain its strong safety guarantees. Although Rust’s ownership model and compile-time checks prevent many errors, sometimes unexp...
As software systems grow increasingly complex and interconnected, detecting vulnerabilities in source code has become a critical and challenging task. Traditional static analysis methods often fall short in capturing deep, context-dependent vulnerabi...
Recent advances in prompt learning have opened new avenues for enhancing natural language understanding in domain-specific tasks, including code vulnerability detection. Motivated by the limitations of conventional binary classification methods in ca...
With the rapid growth in the scale and complexity of software systems, automated vulnerability detection has become increasingly important. Although Large Language Models (LLMs) demonstrate strong code comprehension capabilities, their abilities in v...
The rapid development of J2EE (Java 2 Platform Enterprise Edition) has brought unprecedented severe challenges to vulnerability mining. The current abstract syntax tree-based source code vulnerability classification method does not eliminate irreleva...
Security defects in software code can lead to situations that compromise web-based systems, data security, service availability, and the reliability of functionality. Therefore, it is crucial to detect code vulnerabilities as early as possible. Durin...
Vulnerability detection in software source code is crucial in ensuring software security. Existing models face challenges with dataset class imbalance and long training times. To address these issues, this paper introduces a multi-feature screening a...
The widespread integration of third-party components (TPCs) in Internet of Things (IoT) firmware significantly increases the risk of software vulnerabilities, especially in resource-constrained devices deployed in sensitive environments. Binary Code...
Software vulnerabilities in web applications are one of the sensitive points in data and application security. Although closing a vulnerability after it is detected in web applications seems to be a solution, detecting vulnerabilities in the source c...
Software vulnerabilities are a primary cause of security breaches, and their automated detection at scale has therefore become a pressing concern for both industry and academia. Most Graph Neural Network (GNN) approaches to vulnerability detection tr...
Different Machine Learning techniques to detect software vulnerabilities have emerged in scientific and industrial scenarios. Different actors in these scenarios aim to develop algorithms for predicting security threats without requiring human interv...
In cybersecurity, identifying and addressing vulnerabilities in source code is essential for maintaining secure IT environments. Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevat...
Modern systems produce and handle a large volume of sensitive enterprise data. Therefore, security vulnerabilities in the software systems must be identified and resolved early to prevent security breaches and failures. Predicting security vulnerabil...
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 c...
Software vulnerabilities pose a severe threat to system security and stability. To reinforce the hierarchical structure of source code, enhance the distinctiveness of embedded features, and reduce feature confusion, we propose CADetect, a vulnerabili...
With the widespread application of the Go language, the demand for vulnerability detection in Go programs is increasing. Existing detection models and methods have deficiencies in extracting source code features of Go programs and mainly focus on det...
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...
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...
This study develops a comprehensive workflow for the analytical seismic vulnerability and structural performance assessment of a special-importance steel building located in a region of elevated seismic hazard in southern Spain. The work addresses th...
Large language models (LLMs) have gained popularity in automatic code vulnerability fixing tasks. However, the effectiveness of LLMs in this setting relies heavily on the quality and structure of the input context. Prior work has shown that LLM repai...
Fuzz testing technology is an important approach to detecting SQL injection vulnerabilities. Among them, coverage-guided gray-box fuzz testing technology is the current research focus, and has been proved to be an effective method. However, for SQL i...
Currently, enhancing the efficiency of vulnerability detection and assessment remains relevant. We investigate a new approach for the detection of vulnerabilities that can be used in cyber attacks and assess their severity for further effective respo...
This study investigates the efficacy of advanced large language models, specifically GPT-4o, Claude-3.5 Sonnet, and GPT-3.5 Turbo, in detecting software vulnerabilities. Our experiment utilized vulnerable and secure code samples from the NIST Softwar...
Common Weakness Enumerations (CWEs) and Common Vulnerabilities and Exposures (CVEs) are open knowledge bases that provide definitions, descriptions, and samples of code vulnerabilities. The combination of Large Language Models (LLMs) with vulnerabili...
Exploitable vulnerabilities in software systems are major security concerns. To date, machine learning (ML) based solutions have been proposed to automate and accelerate the detection of vulnerabilities. Most ML techniques aim to isolate a unit of so...
Deep-learning-based vulnerability detection has made substantial progress, but two limitations remain prominent. Sequence-based methods linearize source code and thus weaken the explicit modeling of control-flow and data-flow dependencies. Graph-base...
The largest number of cybersecurity attacks is on web applications, in which Cross-Site Scripting (XSS) is the most popular way. The code audit is the main method to avoid the damage of XSS at the source code level. However, there are numerous limits...
The emergence of security vulnerabilities and risks in software development assisted by self-generated tools, particularly with regard to the generation of code that lacks due consideration of security measures, could have significant consequences fo...
Due to the proliferation of large language models (LLMs) and their widespread use in applications such as ChatGPT, there has been a significant increase in interest in AI over the past year. Multiple researchers have raised the question: how will AI...
In the realm of software development, quality reigns supreme, but the ever-present danger of vulnerabilities threatens to undermine this fundamental principle. Insufficient early vulnerability identification is a key factor in releasing numerous apps...
The proliferation of binary vulnerabilities in the software supply chain has become a critical security challenge. Existing vulnerability detection approaches—including dynamic analysis, static analysis, and decompilation-assisted analysis&mdas...
Internet of Things (IoT), sensor-network, and cyber-physical system (CPS) software increasingly relies on large language models (LLMs) and autonomous agents for code generation, maintenance, and vulnerability repair. However, LLM-generated edge servi...
Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerabi...
Large language models (LLMs) increasingly repair software vulnerabilities, but most evaluations judge only similarity to a developer fix or removal of the weakness. Neither reveals whether working code was broken. We evaluate eight commercial and ope...
As the prevalence and sophistication of cyber threats continue to increase, the development of robust vulnerability detection techniques becomes paramount in ensuring the security of computer systems. Neural models have demonstrated significant poten...
The rise of large language models (LLMs), such as GPT-4, Codex, Code Llama, Claude 3, CodeGemma and DeepSeek, etc., is changing the way software development is approached. These models provide strong support for tasks like writing codes, analyzing bu...
The increasing complexity of web applications and systems, driven by ongoing digitalization, has made software security testing a necessary and critical activity in the software development lifecycle. This article compares the performance of open-sou...
This paper presents MultiGLICE (Multi class Graph Neural Network with Program Slice), a model for static code analysis to detect security vulnerabilities. MultiGLICE extends our previous GLICE model with multiclass detection for a large number of vul...
Writing desktop applications in JavaScript offers developers the opportunity to create cross-platform applications with cutting-edge capabilities. However, in doing so, they are potentially submitting their code to a number of unsanctioned modificati...
The challenge of software vulnerabilities persists globally, despite the widespread availability of advanced security tools and comprehensive developer guidelines. This issue is not the result of professional negligence, but rather the complex and no...
Binary code similarity detection (BCSD) plays an important role in software security. Recent deep learning-based methods have made great progress. Existing methods based on a single feature, such as semantics or graph structure, struggle to handle ch...
The majority of programming courses currently employ online judge systems as lesson materials. Online judge systems are becoming more common as the number of courses and persons studying computer science and information engineering grows. At the same...
Modern safety-critical systems depend heavily on communication networks while operating in increasingly polluted electromagnetic environments. Forward Error Correction codes are increasingly being used in safety-critical applications; however, vulner...
In the current study, an observational procedure, recorded in video, was used to evaluate the quality of parent-child interactions in a sample of vulnerable Portuguese families (n = 47) with school-aged children followed by Child Protective Services...
Web-based systems are used extensively in Korea because web standards have been adapted by the law (e.g., Electronic Government Act). Users can easily access web-based systems if they are connected to the Internet. However, distinguishing between mal...
This paper addresses the problem of IoT security caused by code cloning when developing a massive variety of different smart devices. A clone detection method is proposed to identify clone-caused vulnerabilities in IoT software. A hybrid solution com...
of 19