Metamong: Detecting Render-Update Bugs in Web Browsers through Fuzzing
Suhwan Song, Byoungyoung Lee
摘要
A render-update bug arises when a web browser produces an erroneous rendering output due to incorrect rendering updates. Such render-update bugs seriously harm the usability and reliability of web browsers. However, we find that detecting render-update bugs is challenging because the render-update bug is a semantic buggiven a rendering result, it is difficult to determine if it is correct due to the complex rendering specification of DOM and CSS. Thus, unlike memory corruption bugs, the incorrect rendering output does not raise the violation or crash. In practice, render-update bug detection relies on the time-prohibitive manual analysis of domain experts to determine the bug.
This paper proposes Metamong, an automated framework to detect render-update bugs without false positive issues via differential fuzz testing. Metamong features two key components: (i) page mutator, and (ii) render-update oracle. The page mutator generates render-update operations, which change the content of the web page, to trigger a render-update bug. The render-update oracle exploits an HTML standard rule, so-called yielding, to produce the correct rendering result of a given web page. Combining these components, Metamong creates two HTML files where each constructs the same web page, but only one of them induces the render-update. It then uses differential testing to compare their rendering outputs to determine a bug. We implemented a prototype of Metamong, which performs differential fuzz testing on popular browsers, Chrome and Firefox. By far, Metamong identified 19 new render-update bugs, 17 in Chrome and two in Firefox. All of those have been confirmed by each browser vendor and five are already fixed, demonstrating the practical effectiveness of Metamong in identifying render-update bugs.
• Software and its engineering → Software testing and debugging.
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引用它的顶会 Paper3
- Tacoma: Enhanced Browser Fuzzing with Fine-Grained Semantic AlignmentJiashui Wang, Peng Qian, Xilin Huang, Xinlei Ying 等ISSTA 2024 · 被引用 3 次
- Janus: Detecting Rendering Bugs in Web Browsers via Visual Delta ConsistencyChijin Zhou, Quan Zhang, Bingzhou Qian, Yu JiangICSE 2025 · 被引用 2 次
- BUIzz: Finding Policy Enforcement Bugs via Interaction Simulation on the Browser User InterfaceMingi Jung, Donggyu Kim, Mijung Kim, Seongil WiUSENIX Security 2026
它引用的顶会 Paper18
- Angora: Efficient Fuzzing by Principled SearchPeng Chen, Hao ChenS&P 2018 · 被引用 616 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
- Razzer: Finding Kernel Race Bugs through FuzzingDae R. Jeong, Kyungtae Kim, Basavesh Shivakumar, Byoungyoung Lee 等S&P 2019 · 被引用 202 次
- NEZHA: Efficient Domain-Independent Differential TestingTheofilos Petsios, Adrian Tang, Salvatore J. Stolfo, Angelos D. Keromytis 等S&P 2017 · 被引用 132 次
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