# FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time Analysis and Machine Learning

arXiv: https://arxiv.org/abs/2412.08100 · HTML: https://mlsanity.com/paper/2412.08100/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2024.

## Abstract (arXiv)

Fuzz testing is a fundamental technique employed to identify vulnerabilities within software systems. However, the process can be protracted and resource-intensive, especially when confronted with extensive codebases. In this work, I present FuzzDistill, an approach that harnesses compile-time data and machine learning to refine fuzzing targets. By analyzing compile-time information, such as function call graphs' features, loop information, and memory operations, FuzzDistill identifies high-priority areas of the codebase that are more probable to contain vulnerabilities. I demonstrate the efficacy of my approach through experiments conducted on real-world software, demonstrating substantial reductions in testing time.