Xiangyu Guo (University of Toronto), Akshay Kawlay (University of Toronto), Eric Liu (University of Toronto), David Lie (University of Toronto)

As more critical services move onto the web, it has become increasingly important to detect and address vulnerabilities in web applications. These vulnerabilities only occur under specific conditions: when 1) the vulnerable code is executed and 2) the web application is in the required state. If the application is not in the required state, then even if the vulnerable code is executed, the vulnerability may not be triggered. Previous work naively explores the application state by filling every field and triggering every JavaScript event before submitting HTML forms. However, this simplistic approach can fail to satisfy constraints between the web page elements, as well as input format constraints. To address this, we present EvoCrawl, a web crawler that uses evolutionary search to efficiently find different sequences of web interactions. EvoCrawl finds sequences that can successfully submit inputs to web applications and thus explore more code and server-side states than previous approaches. To assess the benefits of EvoCrawl we evaluate it against three state-of-the-art vulnerability scanners on ten web applications. We find that EvoCrawl achieves better code coverage due to its ability to execute code that can only be executed when the application is in a particular state. On average, EvoCrawl achieves a 59% increase in code coverage and successfully submits HTML forms 5x more frequently than the next best tool. By integrating IDOR and XSS vulnerability scanners, we used EvoCrawl to find eight zero-day IDOR and XSS vulnerabilities in WordPress, HotCRP, Kanboard, ImpressCMS, and GitLab.

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BrowserFM: A Feature Model-based Approach to Browser Fingerprint Analysis

Maxime Huyghe (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Clément Quinton (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Walter Rudametkin (Univ. Rennes, Inria, CNRS, UMR 6074 IRISA)

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Throwaway Accounts and Moderation on Reddit

Cheng Guo (Clemson University), Kelly Caine (Clemson University)

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BumbleBee: Secure Two-party Inference Framework for Large Transformers

Wen-jie Lu (Ant Group), Zhicong Huang (Ant Group), Zhen Gu (Alibaba Group), Jingyu Li (Ant Group & Zhejiang University), Jian Liu (Zhejiang University), Cheng Hong (Ant Group), Kui Ren (Zhejiang University), Tao Wei (Ant Group), WenGuang Chen (Ant Group)

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THEMIS: Regulating Textual Inversion for Personalized Concept Censorship

Yutong Wu (Nanyang Technological University), Jie Zhang (Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore), Florian Kerschbaum (University of Waterloo), Tianwei Zhang (Nanyang Technological University)

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