Nuno Sabino (Carnegie Mellon University, Instituto Superior Técnico, Universidade de Lisboa, and Instituto de Telecomunicações), Darion Cassel (Carnegie Mellon University), Rui Abreu (Universidade do Porto, INESC-ID), Pedro Adão (Instituto Superior Técnico, Universidade de Lisboa, and Instituto de Telecomunicações), Lujo Bauer (Carnegie Mellon University), Limin Jia (Carnegie Mellon University)

DOM-based cross-site scripting (DOM-XSS) is a prevalent form of web vulnerability. Prior work on automated detection and confirmation of such vulnerabilities at scale has several limitations. First, prior work does not interact with the page and thus misses vulnerabilities in event handlers whose execution depends on user actions. Second, prior work does not find URL components, such as GET parameters and fragment values that, when instantiated with specific keys/values, execute more code paths. To address this, we introduce SWIPE, a DOM-XSS analysis infrastructure that uses fuzzing to generate user interactions to trigger event handlers and leverages dynamic symbolic execution (DSE) to automatically synthesize URL parameters and fragments. We run SWIPE on 44,480 URLs found in pages from the Tranco top 30,000 popular domains. Compared to prior work, SWIPE’s fuzzer finds 15% more vulnerabilities. Additionally, we find that a lack of parameters and fragments in URLs significantly hinders DOM-XSS detection, and show that SWIPE’s DSE engine can synthesize previously unseen URL parameters and fragments that trigger 20 new vulnerabilities.

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LLMBisect: Breaking Barriers in Bug Bisection with A Comparative...

Zheng Zhang (University of California, Riverside), Haonan Li (University of California, Riverside), Xingyu Li (University of California, Riverside), Hang Zhang (Indiana University Bloomington), Zhiyun Qian (University of California, Riverside)

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MEVisor: High-Throughput MEV Discovery in DEXs with GPU Parallelism

Weimin CHEN (The Hong Kong Polytechnic University (PolyU)), Xiapu Luo (The Hong Kong Polytechnic University)

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Building Next-Generation Datasets for Provenance-Based Intrusion Detection

Qizhi Cai (Zhejiang University), Lingzhi Wang (Northwestern University), Yao Zhu (Zhejiang University), Zhipeng Chen (Zhejiang University), Xiangmin Shen (Hofstra University), Zhenyuan Li (Zhejiang University)

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