Assessing LLMs' One-Shot Vulnerability Patching Performance
Analysis
This ArXiv article explores the application of Large Language Models (LLMs) in automatically patching software vulnerabilities. It assesses their capabilities in a one-shot learning scenario, patching both real-world and synthetic flaws.
Key Takeaways
- •Investigates the potential of LLMs to automatically patch software vulnerabilities.
- •Focuses on a one-shot learning approach, indicating efficiency is a goal.
- •Tests the LLMs on both real and artificial vulnerabilities for a broader evaluation.
Reference / Citation
View Original"The study evaluates LLMs for patching real and artificial vulnerabilities."