RPG: Rust Library Fuzzing with Pool-based Fuzz Target Generation and Generic Support
Rust libraries are ubiquitous in Rust-based software development. Guaranteeing their correctness and reliability requires thorough analysis and testing. Fuzzing is a popular bug-finding solution, yet it requires writing fuzz targets for libraries. Recently, some automatic fuzz target generation methods have been proposed. However, two challenges remain: (1) how to generate diverse API sequences that prioritize unsafe code and interactions to reveal bugs in Rust libraries; (2) how to provide support for the generic APIs and verify both syntactic and semantic validity of the fuzz targets to enable more comprehensive testing of Rust libraries.
In this paper, we propose RPG, an automatic fuzz target synthesis technique to support Rust library fuzzing. RPG uses a pool-based search to generate diverse and unsafe API sequences, and synthesizes fuzz targets with generic support and validity check. The experimental results demonstrate that RPG enhances both the quality of the generated fuzz targets and the bug-finding ability through pool-based generation and generic support, substantially outperforming the state-of-the-art. Moreover, RPG has discovered 25 previously unknown bugs from 50 well-known Rust libraries available on Crates.io.
Thu 18 AprDisplayed time zone: Lisbon change
14:00 - 15:30 | Fuzzing 2Software Engineering in Practice / Research Track at Fernando Pessoa Chair(s): Thuan Pham The University of Melbourne | ||
14:00 15mTalk | Are We There Yet? Unraveling the State-of-the-Art Smart Contract Fuzzers Research Track Shuohan Wu Hong Kong Polytechnic University, Zihao Li The Hong Kong Polytechnic Universituy, Luyi Yan Hong Kong Polytechnic University, Weimin Chen The Hong Kong Polytechnic University, Muhui Jiang The Hong Kong Polytechnic University, Chenxu Wang Xi'an Jiaotong University, Xiapu Luo The Hong Kong Polytechnic University, Hao Zhou Hong Kong Polytechnic University | ||
14:15 15mTalk | RPG: Rust Library Fuzzing with Pool-based Fuzz Target Generation and Generic Support Research Track Zhiwu Xu Shenzhen University, Bohao Wu CSSE, Shenzhen University, Cheng Wen Guangzhou Institute of Technology, Xidian University, Bin Zhang Shenzhen University, Shengchao Qin Fermat Labs, Huawei, Mengda He Fermat Labs, Huawei DOI Pre-print | ||
14:30 15mTalk | Extrapolating Coverage Rate in Greybox Fuzzing Research Track Danushka Liyanage Monash University, Australia, Seongmin Lee Max Planck Institute for Security and Privacy (MPI-SP), Marcel Böhme MPI-SP, Bochum, Kla Tantithamthavorn Monash University DOI Pre-print | ||
14:45 15mTalk | FuzzInMem: Fuzzing Programs via In-memory Structures Research Track Xuwei Liu Purdue University, USA, Wei You Renmin University of China, Yapeng Ye Purdue University, Zhuo Zhang Purdue University, Jianjun Huang Renmin University of China, Xiangyu Zhang Purdue University | ||
15:00 15mTalk | Fuzz4All: Universal Fuzzing with Large Language Models Research Track Chunqiu Steven Xia University of Illinois at Urbana-Champaign, Matteo Paltenghi University of Stuttgart, Jia Le Tian UIUC, Michael Pradel University of Stuttgart, Lingming Zhang University of Illinois at Urbana-Champaign Pre-print | ||
15:15 15mTalk | MicroFuzz: An Efficient Fuzzing Framework for Microservices Software Engineering in Practice | ||