April 8, 2024, 4:46 a.m. | Zahra Mousavi, Chadni Islam, Kristen Moore, Alsharif Abuadbba, Muhammad Ali Babar

cs.CL updates on arXiv.org arxiv.org

arXiv:2404.03823v1 Announce Type: cross
Abstract: The increasing trend of using Large Language Models (LLMs) for code generation raises the question of their capability to generate trustworthy code. While many researchers are exploring the utility of code generation for uncovering software vulnerabilities, one crucial but often overlooked aspect is the security Application Programming Interfaces (APIs). APIs play an integral role in upholding software security, yet effectively integrating security APIs presents substantial challenges. This leads to inadvertent misuse by developers, thereby exposing …

abstract apis arxiv capability code code generation cs.cl cs.cr cs.cy generate investigation java language language models large language large language models llms misuse question raises researchers security software trend trustworthy type utility vulnerabilities

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Software Engineer, Machine Learning (Tel Aviv)

@ Meta | Tel Aviv, Israel

Senior Data Scientist- Digital Government

@ Oracle | CASABLANCA, Morocco