LLM-based fuzz driver generation
As software complexity continues to escalate, traditional fuzzing methodologies encounter limitations in efficiently discovering vulnerabilities, underscoring the need for innovative approaches. This thesis investigates the innovative integration of Large Language Models (LLMs) into the process of f...
Saved in:
Main Author: | Chai, Wen Xuan |
---|---|
Other Authors: | Liu Yang |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/175404 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Transforming assessment with LLM and generative AI: impacts and challenges
by: Hao, Jiangang
Published: (2024) -
A survey of protocol fuzzing
by: ZHANG, Xiaohan, et al.
Published: (2024) -
Probabilistic path prioritization for hybrid fuzzing
by: ZHAO, Lei, et al.
Published: (2022) -
A web system for LLM-based Q&A
by: Banerjee, Tanya
Published: (2024) -
LLM-based column lineage for relational databases
by: Tan, Yu Ling
Published: (2024)