English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

To avoid potential risks posed by vulnerabilities in third-party libraries,security researchers maintain vulnerability databases (e.g., NVD) containingvulnerability reports, each of which records the description of a vulnerabilityand the name list of libraries affected by the vulnerability (a.k.a. vulnerablelibraries). However, recent studies on about 200,000 vulnerability reports inNVD show that 53.3% of these reports do not include the name list of vulnerablelibraries, and 59.82% of the included name lists of vulnerable libraries areincomplete or incorrect.  To address the preceding issue, in this paper, we propose the firstgenerative approach named VulLibGen to generate the name list of vulnerablelibraries (out of all the existing libraries) for the given vulnerability byutilizing recent enormous advances in Large Language Models (LLMs), in order toachieve high accuracy. VulLibGen takes only the description of a vulnerabilityas input and achieves high identification accuracy based on LLMs' priorknowledge of all the existing libraries. VulLibGen also includes the inputaugmentation technique to help identify zero-shot vulnerable libraries (thosenot occurring during training) and the post-processing technique to helpaddress VulLibGen's hallucinations. We evaluate VulLibGen using threestate-of-the-art/practice approaches (LightXML, Chronos, and VulLibMiner) thatidentify vulnerable libraries on an open-source dataset (VulLib). Ourevaluation results show that VulLibGen can accurately identify vulnerablelibraries with an average F1 score of 0.626 while the state-of-the-art/practiceapproaches achieve only 0.561. The post-processing technique helps VulLibGenachieve an average improvement of F1@1 by 9.3%. The input augmentationtechnique helps VulLibGen achieve an average improvement of F1@1 by 39% inidentifying zero-shot libraries.

VulLibGen: Identifying Vulnerable Third-Party Libraries via Generative Pre-Trained Model