# Smart Fuzzing of 5G Wireless Software Implementation

arXiv: https://arxiv.org/abs/2309.12994 · HTML: https://mlsanity.com/paper/2309.12994/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2023.

## Built on by (most cited first, 3 of 3)

- [`From Prompt to Perturbation': An Adaptive Framework for Voice-Based Jailbreaks on Audio LLMs](https://mlsanity.com/paper/2502.00735/) (2025)
- [On the Challenges of Fuzzing Techniques via Large Language Models](https://mlsanity.com/paper/2402.00350/) (2024)
- [LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models](https://mlsanity.com/paper/2510.10179/) (2025)

## Abstract (arXiv)

In this paper, we introduce a comprehensive approach to bolstering the security, reliability, and comprehensibility of OpenAirInterface5G (OAI5G), an open-source software framework for the exploration, development, and testing of 5G wireless communication systems. Firstly, we employ AFL++, a powerful fuzzing tool, to fuzzy-test OAI5G with respect to its configuration files rigorously. This extensive testing process helps identify errors, defects, and security vulnerabilities that may evade conventional testing methods. Secondly, we harness the capabilities of Large Language Models such as Google Bard to automatically decipher and document the meanings of parameters within the OAI5G codebase that are used in fuzzing. This automated parameter interpretation streamlines subsequent analyses and facilitates more informed decision-making. Together, these two techniques contribute to fortifying the OAI5G system, making it more robust, secure, and understandable for developers and analysts alike.