Security vulnerabilities are increasingly prevalent in modern software and they are widely
consequential to our society. Various approaches to defending against these vulnerabilities …

Large Language Models (LLMs) are increasingly becoming fundamental in supporting
software developers in coding tasks. The massive datasets used for training LLMs are often …

Vulnerability datasets have become an important instrument in software security research,
being used to develop automated, machine learning-based vulnerability detection and …

Timely and effective vulnerability patching is essential for cybersecurity defense, for which
various approaches have been proposed yet still struggle to generate valid and correct …

Increasing studies have shown bugs in multi-language software as a critical loophole in
modern software quality assurance, especially those induced by language interactions (ie …

The proliferation of vulnerable code poses a significant threat to software system security
and user privacy. Given the inefficiency inherent in manual vulnerability analysis, there has …

Detecting vulnerabilities is a crucial task for maintaining the integrity, availability, and
security of software systems. Utilizing DL-based models for vulnerability detection has …

In current software applications, numerous vulnerabilities may be present. Attackers attempt
to exploit these vulnerabilities, leading to security breaches, unauthorized entry, data theft …

With the rapid development and widespread use of advanced network systems, software
vulnerabilities pose a significant threat to secure communications and networking. Learning …

The ability of automatic vulnerability detection models largely depends on the dataset used
for training. However, annotating these datasets is costly and time-consuming, leading to a …