Junzhe Li

R1-Fuzz: Specializing Language Models for Textual Fuzzing via Reinforcement Learning

2025 · arXiv (Cornell University)

Fuzzing is effective for vulnerability discovery but struggles with complex targets such as compilers, interpreters, and database engines, which accept textual input that must satisfy intricate syntactic and semantic constraints. Although language models (LMs) have …