Related papers: SliceLocator: Locating Vulnerable Statements with …
In modern software development, vulnerability detection is crucial due to the inevitability of bugs and vulnerabilities in complex software systems. Effective detection and elimination of these vulnerabilities during the testing phase are…
This study explores the effectiveness of graph neural networks (GNNs) for vulnerability detection in software code, utilizing a real-world dataset of Java vulnerability-fixing commits. The dataset's structure, based on the number of…
Software vulnerabilities can pose severe harms to a computing system. They can lead to system crash, privacy leakage, or even physical damage. Correctly identifying vulnerabilities among enormous software codes in a timely manner is so far…
1-day vulnerabilities in binaries have become a major threat to software security. Patch presence test is one of the effective ways to detect the vulnerability. However, existing patch presence test works do not perform well in practical…
The application of language models to project-level vulnerability detection remains challenging, owing to the dual requirement of accurately localizing security-sensitive code and correctly correlating and reasoning over complex program…
Automated detection of software vulnerabilities is critical for enhancing security, yet existing methods often struggle with the complexity and diversity of modern codebases. In this paper, we introduce EnStack, a novel ensemble stacking…
Most vulnerability detection studies focus on datasets of vulnerabilities in C/C++ code, offering limited language diversity. Thus, the effectiveness of deep learning methods, including large language models (LLMs), in detecting software…
Despite various approaches being employed to detect vulnerabilities, the number of reported vulnerabilities shows an upward trend over the years. This suggests the problems are not caught before the code is released, which could be caused…
As the development of large language models (LLMs) rapidly advances, securing these models effectively without compromising their utility has become a pivotal area of research. However, current defense strategies against jailbreak attacks…
With the continuous extension of the Industrial Internet, cyber incidents caused by software vulnerabilities have been increasing in recent years. However, software vulnerabilities detection is still heavily relying on code review done by…
Over the years, open-source software systems have become prey to threat actors. Even as open-source communities act quickly to patch the breach, code vulnerability screening should be an integral part of agile software development from the…
As control-flow protection gets widely deployed, it is difficult for attackers to corrupt control-data and achieve control-flow hijacking. Instead, data-oriented attacks, which manipulate non-control data, have been demonstrated to be…
Source code vulnerability detection aims to identify inherent vulnerabilities to safeguard software systems from potential attacks. Many prior studies overlook diverse vulnerability characteristics, simplifying the problem into a binary…
Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures to be implemented before they may be exploited. Automatic…
Our work explores the utilization of deep learning, specifically leveraging the CodeBERT model, to enhance code security testing for Python applications by detecting SQL injection vulnerabilities. Unlike traditional security testing methods…
Currently, Large Language Models (LLMs) feature a diversified architectural landscape, including traditional Transformer, GateDeltaNet, and Mamba. However, the evolutionary laws of hierarchical representations, task knowledge formation…
Program slicing has been widely applied in a variety of software engineering tasks. However, existing program slicing techniques only deal with traditional programs that are constructed with instructions and variables, rather than neural…
This paper presents an approach for identification of vulnerable IoT applications. The approach focuses on a category of vulnerabilities that leads to sensitive information leakage which can be identified by using taint flow analysis.…
Vulnerability identification constitutes a task of high importance for cyber security. It is quite helpful for locating and fixing vulnerable functions in large applications. However, this task is rather challenging owing to the absence of…
The increasing virtualization of fifth generation (5G) networks expands the attack surface of the user plane, making spoofing a persistent threat to slice integrity and service reliability. This study presents a slice-aware lightweight…