FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework
Modern fuzzers increasingly use Large Language Models (LLMs) to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initialization and sampling variance, which can reduce exploration efficiency and lead to redundant inputs. We present FunFuzz, a multi-island evolutionary fuzzing framework that runs several isolated searches in parallel and periodically migrates high-value candidates to maintain diversity. FunFuzz derives initial generation prompts from documentation and initializes islands with topic-specific instructions, then continuously adapts prompts using feedback-guided selection. During fuzzing, candidates are prioritized by incremental compiler coverage, whi...
| Authors | Mario Rodríguez Béjar, B. Romera-Paredes, Jose L. Hernández-Ramos |
|---|---|
| Primary category | cs.CR |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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