The rise of Large Language Models (LLMs) has revolutionized our comprehension of
intelligence bringing us closer to Artificial Intelligence. Since their introduction, researchers …

Building new, powerful data-driven defenses against prevalent software vulnerabilities
needs sizable, quality vulnerability datasets, so does large-scale benchmarking of existing …

The detection of vulnerabilities in source code is a critical task in software assurance. In this
work, we propose a semi-supervised learning approach that leverages pattern-exploiting …

Accompanying the successes of learning-based defensive software vulnerability analyses is
the lack of large and quality sets of labeled vulnerable program samples, which impedes …

A core problem of vulnerability detection is to detect multi-type vulnerabilities simultaneously
by characterizing vulnerabilities of high diversity and complexity in real program source …

Recently, deep learning techniques have garnered substantial attention for their ability to
identify vulnerable code patterns accurately. However, current state-of-the-art deep learning …

In the rapidly-evolving landscape of software development, the detection of vulnerabilities in
source code has become paramount. Our study introduces a novel knowledge distillation …

Recently, deep learning techniques have garnered substantial attention for their ability to
identify vulnerable code patterns accurately. However, current state-of-the-art deep learning …

Abstract Large Language Models (LLMs) have emerged as powerful tools in the domain of
software vulnerability and cybersecurity tasks, offering promising capabilities in detecting …

Vulnerabilities in software are like ticking time bombs, but it is difficult to completely eliminate
them. For example, buffer overflow is a quite common vulnerability that occurs when a …