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FV8: A Forced Execution JavaScript Engine for Detecting Evasive Techniques
Nikolaos Pantelaios, Alexandros Kapravelos
Abstract
Evasion techniques allow malicious code to never be observed. This impacts significantly the detection capabilities of tools that rely on either dynamic or static analysis, as they never get to process the malicious code. The dynamic nature of JavaScript, where code is often injected dynamically, makes evasions particularly effective. Yet, we lack tools that can detect evasive techniques in a challenging environment such as JavaScript. In this paper, we present FV8, a modified V8 JavaScript engine designed to identify evasion techniques in JavaScript code. FV8 selectively enforces code execution on APIs that conditionally inject dynamic code, thus enhancing code coverage and consequently improving visibility into malicious code. We integrate our tool in both the Node.js engine and the Chromium browser, compelling code execution in npm packages and Chrome browser extensions. Our tool increases code coverage by 11% compared to default V8 and detects 28 unique evasion categories, including five previously unreported techniques. In data confirmed as malicious from both ecosystems, our tool identifies 1,443 (14.6%) npm packages and 164 (82%) extensions containing at least one type of evasion. In previously unexamined extensions (39,592), our tool discovered 16,471 injected third-party scripts, and a total of 8,732,120 lines of code executed due to our forced execution instrumentation. Furthermore, it tagged a total of 423 extensions as both evasive and malicious and we manually verify 110 extensions (26%) to actually be malicious, impacting two million users. Our tool is open-source and serves both as an in-browser and standalone dynamic analysis tool, capable of detecting evasive code, bypassing obfuscation in certain cases, offering improved access to malicious code, and supporting recursive analysis of dynamic code injections
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Cited by top-tier papers3
- OBsmith: LLM-Powered JavaScript Obfuscator TestingShan Jiang, Chenguang Zhu, Sarfraz KhurshidOOPSLA 2026 · 2 citations
- Same Script, Different Behavior: Characterizing Divergent JavaScript Execution Across Different Device PlatformsAhsan Zafar, Junhua Su, Sohom Datta, Alexandros Kapravelos et al.CCS 2025
- PyFEX: Uncovering Evasive Python-based Threats via Resilient and Exhaustive Path ExplorationMeng Wang, Yue Ma, Majid Garoosi, Wenting Fan et al.CCS 2026
Builds on12
- AdGraph: A Graph-Based Approach to Ad and Tracker BlockingUmar Iqbal, Peter Snyder, Shitong Zhu, Benjamin Livshits et al.S&P 2020 · 112 citations
- HideNoSeek: Camouflaging Malicious JavaScript in Benign ASTsAurore Fass, Michael Backes, Ben StockCCS 2019 · 78 citations
- JSgraph: Enabling Reconstruction of Web Attacks via Efficient Tracking of Live In-Browser JavaScript ExecutionsBo Li, Phani Vadrevu, Kyu Hyung Lee, Roberto PerdisciNDSS 2018 · 61 citations
- EmPoWeb: Empowering Web Applications with Browser ExtensionsDolière Francis SoméS&P 2019 · 60 citations
- Towards Realistic and ReproducibleWeb Crawl MeasurementsJordan Jueckstock, Shaown Sarker, Peter Snyder, Aidan Beggs et al.WWW 2021 · 52 citations
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