This program is tentative and subject to change.
The Language Server Protocol (LSP) has revolutionized the integration of code intelligence in modern software development. There are approximately 300 LSP server implementations for various languages and 50 editors offering LSP integration. However, the reliability of LSP servers is a growing concern, as crashes can disable all code intelligence features and significantly impact productivity, while vulnerabilities can put developers at risk even when editing untrusted source code. Despite the widespread adoption of LSP, no existing techniques specifically target LSP server testing. To bridge this gap, we present LSPFuzz, a grey-box hybrid fuzzer for systematic LSP server testing. Our key insight is that effective LSP server testing requires holistic mutation of source code and editor operations, as bugs often manifest from their combinations. To satisfy the sophisticated constraints of LSP and effectively explore the input space, we employ a two-stage mutation pipeline: syntax-aware mutations to source code, followed by context-aware dispatching of editor operations. We evaluated LSPFuzz on four widely used LSP servers. LSPFuzz demonstrated superior performance compared to baseline fuzzers, and uncovered previously unknown bugs in real-world LSP servers. Of the 51 bugs we reported, 42 have been confirmed, 26 have been fixed by developers, and two have been assigned CVE numbers. Our work advances the quality assurance of LSP servers, providing both a practical tool and foundational insights for future research in this domain.
This program is tentative and subject to change.
Wed 19 NovDisplayed time zone: Seoul change
14:00 - 15:30 | |||
14:00 10mTalk | Terminator: enabling efficient fuzzing of closed-source GUI programs by automatic coverage-guided termination Research Papers | ||
14:10 10mTalk | Function Clustering-Based Fuzzing Termination: Toward Smarter Early Stopping Research Papers ding liang University of Science and Technology of China, Wenzhang Yang Institute of AI for industries, Yinxing Xue Institute of AI for Industries, Chinese Academy of Sciences | ||
14:20 10mTalk | Risk Estimation in Differential Fuzzing via Extreme Value Theory Research Papers Rafael Baez University of Texas at El Paso, Alejandro Olivas University of Texas at El Paso, Nathan K Diamond University of Texas at El Paso, Marcelo F. Frias Dept. of Software Engineering Instituto Tecnológico de Buenos Aires, Yannic Noller Ruhr University Bochum, Saeid Tizpaz-Niari University of Illinois Chicago | ||
14:30 10mTalk | Advanced White-Box Heuristics for Search-Based Fuzzing of REST APIs Journal-First Track Andrea Arcuri Kristiania University College and Oslo Metropolitan University, Man Zhang Beihang University, China, Juan Pablo Galeotti University of Buenos Aires | ||
14:40 10mTalk | BCFuzz: Bytecode-Driven Fuzzing for JavaScript Engines Research Papers Jiming Wang SKLP, Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Chenggang Wu Institute of Computing Technology at Chinese Academy of Sciences; University of Chinese Academy of Sciences; Zhongguancun Laboratory, Jikai Ren SKLP, Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Yuhao Hu SKLP, Institute of Computing Technology, CAS & University of Chinese Academy of Sciences, Yan Kang Institute of Computing Technology at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Xiaojie Wei SKLP, Institute of Computing Technology, CAS, Yuanming Lai Institute of Computing Technology at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Mengyao Xie SKLP, Institute of Computing Technology, CAS, Zhe Wang Institute of Computing Technology at Chinese Academy of Sciences; Zhongguancun Laboratory | ||
14:50 10mTalk | LSPFuzz: Hunting Bugs in Language Servers Research Papers Hengcheng Zhu The Hong Kong University of Science and Technology, Songqiang Chen The Hong Kong University of Science and Technology, Valerio Terragni University of Auckland, Lili Wei McGill University, Yepang Liu Southern University of Science and Technology, Jiarong Wu , Shing-Chi Cheung Hong Kong University of Science and Technology Pre-print | ||
15:00 10mTalk | TEPHRA: Principled Discovery of Fuzzer Limitations Research Papers Vasil Sarafov μCSRL, CODE Research Institute, University of the Bundeswehr Munich, David Markvica μCSRL, CODE Research Institute, University of the Bundeswehr Munich, Stefan Brunthaler μCSRL, CODE Research Institute, University of the Bundeswehr Munich | ||
15:10 10mTalk | Learning-Guided Fuzzing for Testing Stateful SDN Controllers Journal-First Track Raphaël Ollando University of Luxembourg, Seung Yeob Shin University of Luxembourg, Lionel Briand University of Ottawa, Canada; Lero centre, University of Limerick, Ireland | ||
15:20 10mTalk | Learning from the Past: Real-World Exploit Migration for Smart Contract PoC Generation Research Papers Kairan Sun Nanyang Technological University, Zhengzi Xu Imperial Global Singapore, Kaixuan Li Nanyang Technological University, Lyuye Zhang Nanyang Technological University, Yebo Feng Nanyang Technological University, Daoyuan Wu Lingnan University, Yang Liu Nanyang Technological University | ||