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FV8: A Forced Execution JavaScript Engine for Detecting Evasive Techniques

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arxiv 2405.13175 v1 pith:EPUPOVO3 submitted 2024-05-21 cs.CR

classification cs.CR
keywords codemalicioustooldynamicextensionsjavascripttechniquesevasion
verification ladder T0 review T1 audit T2 compute T3 formal
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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 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing JavaScript Malware Detection through Weighted Behavioral DFAs

    cs.CR 2025-05 reject novelty 4.0 of 10

    A weighted behavior DFA built from 10 malicious JavaScript sequences produces match percentages that label traces as benign, partially malicious, or malicious, but no accuracy metrics validate those labels.

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