Fuzzing JavaScript Engines with Aspect-preserving Mutation
Soyeon Park, Wen Xu, Insu Yun, Daehee Jang, Taesoo Kim
摘要
Fuzzing is a practical, widely-deployed technique to find bugs in complex, real-world programs like JavaScript engines. We observed, however, that existing fuzzing approaches, either generative or mutational, fall short in fully harvesting high-quality input corpora such as known proof of concept (PoC) exploits or unit tests. Existing fuzzers tend to destruct subtle semantics or conditions encoded in the input corpus in order to generate new test cases because this approach helps in discovering new code paths of the program. Nevertheless, for JavaScript-like complex programs, such a conventional design leads to test cases that tackle only shallow parts of the complex codebase and fails to reach deep bugs effectively due to the huge input space.In this paper, we advocate a new technique, called an aspect-preserving mutation, that stochastically preserves the desirable properties, called aspects, that we prefer to be maintained across mutation. We demonstrate the aspect preservation with two mutation strategies, namely, structure and type preservation, in our fully-fledged JavaScript fuzzer, called Die. Our evaluation shows that Die’s aspect-preserving mutation is more effective in discovering new bugs (5.7× more unique crashes) and producing valid test cases (2.4× fewer runtime errors) than the state-of-the-art JavaScript fuzzers. Die newly discovered 48 high-impact bugs in ChakraCore, JavaScriptCore, and V8 (38 fixed with 12 CVEs assigned as of today). The source code of Die is publicly available as an open-source project.1
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper62
- Nyx: Greybox Hypervisor Fuzzing using Fast Snapshots and Affine TypesSergej Schumilo, Cornelius Aschermann, Ali Abbasi, Simon Wörner 等USENIX Security 2021 · 被引用 102 次
- Automated conformance testing for JavaScript engines via deep compiler fuzzingGuixin Ye, Zhanyong Tang, Shin Hwei Tan, Songfang Huang 等PLDI 2021 · 被引用 75 次
- One Engine to Fuzz 'em All: Generic Language Processor Testing with Semantic ValidationYongheng Chen, Rui Zhong, Hong Hu, Hangfan Zhang 等S&P 2021 · 被引用 70 次
- SoK: Prudent Evaluation Practices for FuzzingMoritz Schloegel, Nils Bars, Nico Schiller, Lukas Bernhard 等S&P 2024 · 被引用 69 次
- Gramatron: effective grammar-aware fuzzingPrashast Srivastava, Mathias PayerISSTA 2021 · 被引用 51 次
它引用的顶会 Paper5
- Evaluating Fuzz TestingGeorge Klees, Andrew Ruef, Benji Cooper, Shiyi Wei 等CCS 2018 · 被引用 753 次
- Skyfire: Data-Driven Seed Generation for FuzzingJunjie Wang, Bihuan Chen, Lei Wei, Yang LiuS&P 2017 · 被引用 382 次
- NAUTILUS: Fishing for Deep Bugs with GrammarsCornelius Aschermann, Tommaso Frassetto, Thorsten Holz, Patrick Jauernig 等NDSS 2019 · 被引用 291 次
- CodeAlchemist: Semantics-Aware Code Generation to Find Vulnerabilities in JavaScript EnginesHyungSeok Han, DongHyeon Oh, Sang Kil ChaNDSS 2019 · 被引用 178 次
- EnFuzz: Ensemble Fuzzing with Seed Synchronization among Diverse FuzzersYuanliang Chen, Yu Jiang, Fuchen Ma, Jie Liang 等USENIX Security 2019 · 被引用 139 次
相关 Paper
- BCFuzz: Bytecode-Driven Fuzzing for JavaScript EnginesJiming Wang, Chenggang Wu, Jikai Ren, Yuhao Hu 等ASE 2025 · 被引用 1 次
- SoFi: Reflection-Augmented Fuzzing for JavaScript EnginesXiaoyu He, Xiaofei Xie, Yuekang Li, Jianwen Sun 等CCS 2021 · 被引用 34 次
- FuzzJIT: Oracle-Enhanced Fuzzing for JavaScript Engine JIT CompilerJunjie Wang, Zhiyi Zhang, Shuang Liu, Xiaoning Du 等USENIX Security 2023
- Extraction and Mutation at a High Level: Template-Based Fuzzing for JavaScript EnginesWai Kin Wong, Dongwei Xiao, Anthony Cheuk Tung Lai, Yiteng Peng 等OOPSLA 2025 · 被引用 4 次
- Token-Level FuzzingChristopher Salls, Chani Jindal, Jake Corina, Christopher Kruegel 等USENIX Security 2021
