This research explores how Large Language Models can transform fuzz testing for software security, while addressing critical reliability issues in current implementations.

• LLMs show promising potential for automating fuzz driver and seed generation
• Current LLM4Fuzz solutions struggle with low driver validity rates and seed quality
• Researchers identify reliability as the key challenge hindering practical adoption
• The paper outlines a roadmap for developing more dependable LLM-driven fuzzing tools

For security professionals, this research offers valuable insights into how AI can enhance vulnerability detection while highlighting the importance of reliability in automated security testing tools.

Towards Reliable LLM-Driven Fuzz Testing: Vision and Road Ahead