# Revisiting Neural Program Smoothing for Fuzzing

arXiv: https://arxiv.org/abs/2309.16618 · HTML: https://mlsanity.com/paper/2309.16618/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Evaluating Fuzz Testing](https://mlsanity.com/paper/1808.09700/) (2018)
- [Learn&Fuzz: Machine Learning for Input Fuzzing](https://mlsanity.com/paper/1701.07232/) (2017)
- [NEUZZ: Efficient Fuzzing with Neural Program Smoothing](https://mlsanity.com/paper/1807.05620/) (2018)
- [Angora: Efficient Fuzzing by Principled Search](https://mlsanity.com/paper/1803.01307/) (2018)
- [Deep Reinforcement Fuzzing](https://mlsanity.com/paper/1801.04589/) (2018)
- [A systematic review of fuzzing based on machine learning techniques](https://mlsanity.com/paper/1908.01262/) (2019)
- [MTFuzz: Fuzzing with a Multi-Task Neural Network](https://mlsanity.com/paper/2005.12392/) (2020)
- [Not all bytes are equal: Neural byte sieve for fuzzing](https://mlsanity.com/paper/1711.04596/) (2017)
- [FuzzerGym: A Competitive Framework for Fuzzing and Learning](https://mlsanity.com/paper/1807.07490/) (2018)
- [A Review of Machine Learning Applications in Fuzzing](https://mlsanity.com/paper/1906.11133/) (2019)
- [Targeted Greybox Fuzzing with Static Lookahead Analysis](https://mlsanity.com/paper/1905.07147/) (2019)

## Abstract (arXiv)

Testing with randomly generated inputs (fuzzing) has gained significant traction due to its capacity to expose program vulnerabilities automatically. Fuzz testing campaigns generate large amounts of data, making them ideal for the application of machine learning (ML). Neural program smoothing (NPS), a specific family of ML-guided fuzzers, aims to use a neural network as a smooth approximation of the program target for new test case generation. In this paper, we conduct the most extensive evaluation of NPS fuzzers against standard gray-box fuzzers (>11 CPU years and >5.5 GPU years), and make the following contributions: (1) We find that the original performance claims for NPS fuzzers do not hold; a gap we relate to fundamental, implementation, and experimental limitations of prior works. (2) We contribute the first in-depth analysis of the contribution of machine learning and gradient-based mutations in NPS. (3) We implement Neuzz++, which shows that addressing the practical limitations of NPS fuzzers improves performance, but that standard gray-box fuzzers almost always surpass NPS-based fuzzers. (4) As a consequence, we propose new guidelines targeted at benchmarking fuzzing based on machine learning, and present MLFuzz, a platform with GPU access for easy and reproducible evaluation of ML-based fuzzers. Neuzz++, MLFuzz, and all our data are public.