Automated detection of software vulnerabilities is a fundamental problem in software
security. Existing program analysis techniques either suffer from high false positives or false …

Blockchain technology is believed by many to be a game changer in many application
domains. While the first generation of blockchain technology (ie, Blockchain 1.0) is almost …

Software vulnerabilities are prevalent in software systems, causing a variety of problems
including deadlock, information loss, or system failures. Thus, early predictions of software …

Current machine-learning based software vulnerability detection methods are primarily
conducted at the function-level. However, a key limitation of these methods is that they do …

Despite the successes of machine learning (ML) and deep learning (DL)-based vulnerability
detectors (VD), they are limited to providing only the decision on whether a given code is …

The constantly increasing number of disclosed security vulnerabilities have become an
important concern in the software industry and in the field of cybersecurity, suggesting that …

We propose and release a new vulnerable source code dataset. We curate the dataset by
crawling security issue websites, extracting vulnerability-fixing commits and source codes …

This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel
learning framework for building vulnerability detection models. Funded leverages the …

Large language models (large LMs) are increasingly trained on massive codebases and
used to generate code. However, LMs lack awareness of security and are found to …

Context: Previous studies have shown that existing deep learning-based approaches can
significantly improve the performance of vulnerability detection. They represent code in …