The WASM Cloak: Evaluating Browser Fingerprinting Defenses Under WebAssembly based Obfuscation
- URL: http://arxiv.org/abs/2508.21219v1
- Date: Thu, 28 Aug 2025 21:21:51 GMT
- Title: The WASM Cloak: Evaluating Browser Fingerprinting Defenses Under WebAssembly based Obfuscation
- Abstract summary: Browser fingerprinting defenses have historically focused on detecting JavaScript(JS)-based tracking techniques.<n>WebAssembly (WASM) introduces a potential blind spot, as adversaries can convert JS to WASM's low-level binary format to obfuscate malicious logic.<n>This paper presents the first systematic evaluation of how such WASM-based obfuscation impacts the robustness of modern fingerprinting defenses.
- Score: 3.3544069030990538
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Browser fingerprinting defenses have historically focused on detecting JavaScript(JS)-based tracking techniques. However, the widespread adoption of WebAssembly (WASM) introduces a potential blind spot, as adversaries can convert JS to WASM's low-level binary format to obfuscate malicious logic. This paper presents the first systematic evaluation of how such WASM-based obfuscation impacts the robustness of modern fingerprinting defenses. We develop an automated pipeline that translates real-world JS fingerprinting scripts into functional WASM-obfuscated variants and test them against two classes of defenses: state-of-the-art detectors in research literature and commercial, in-browser tools. Our findings reveal a notable divergence: detectors proposed in the research literature that rely on feature-based analysis of source code show moderate vulnerability, stemming from outdated datasets or a lack of WASM compatibility. In contrast, defenses such as browser extensions and native browser features remained completely effective, as their API-level interception is agnostic to the script's underlying implementation. These results highlight a gap between academic and practical defense strategies and offer insights into strengthening detection approaches against WASM-based obfuscation, while also revealing opportunities for more evasive techniques in future attacks.
Related papers
- Breaking and Defending LLM-Powered Social Media Bot Detection Systems [0.0]
Social media bots pose a persistent threat, enabling misinformation, opinion manipulation, and the erosion of trust in online platforms.<n>To combat this, machine learning systems have been developed to detect and limit bot activity, but attackers continuously adapt through techniques such as adversarial learning and behavior imitation.<n>Recent advances in large language models (LLMs) have significantly improved bot detection by enabling deeper semantic and contextual analysis of accounts and their content.<n>We propose a robust multi-LLM defense architecture designed to preserve detection reliability under adaptive adversarial conditions.
arXiv Detail & Related papers (2026-08-16T18:56:01Z) - Broken Gates: Re-evaluating Web Bot Defenses in the Age of LLM Agents [0.10467550794914121]
We evaluate the resilience of challenge-based defenses and non-interactive trust-based defenses against two attacker classes: commercial Captcha-solving services and LLM-based browser agents.<n>Our results show that challenge-based defenses are broadly ineffective against commercial solvers, which achieve near-perfect bypass at negligible cost.<n>Non-interactive defenses such as reCaptcha v3 exhibit stronger resistance, but our analysis reveals that this resilience does not reflect a fundamental security property.
arXiv Detail & Related papers (2026-07-21T03:02:57Z) - WebAgentGuard: A Reasoning-Driven Guard Model for Detecting Prompt Injection Attacks in Web Agents [117.65855863464863]
Web agents are highly vulnerable to prompt injection attacks.<n>Existing defenses, including system prompt defenses and direct fine-tuning of agents, have shown limited effectiveness.<n>We introduce WebAgentGuard, a reasoning-driven, multimodal guard model for prompt injection detection.
arXiv Detail & Related papers (2026-04-14T04:50:35Z) - Obfuscating Code Vulnerabilities against Static Analysis in JavaScript Code [3.7140746756072045]
This paper empirically quantifying the impact of JavaScript obfuscation on state-of-practice Static Application Security Testing.<n>We analyze 16 vulnerable-by-design Node.js web applications from the directory and 260 in-the-wild JavaScript/Node.js projects from GitHub.<n>Our results show that current JavaScript SAST is fundamentally not robust against commonplace obfuscations and that "clean" reports on obfuscated code may offer only a false sense of security.
arXiv Detail & Related papers (2026-04-01T16:52:37Z) - BrowseSafe: Understanding and Preventing Prompt Injection Within AI Browser Agents [8.923854146974783]
We examine the landscape of prompt injection attacks and synthesize a benchmark of attacks embedded in realistic HTML payloads.<n>Our benchmark goes beyond prior work by emphasizing injections that can influence real-world actions rather than mere text outputs.<n>We propose a multi-layered defense strategy comprising both architectural and model-based defenses.
arXiv Detail & Related papers (2025-11-25T18:28:35Z) - BrowserArena: Evaluating LLM Agents on Real-World Web Navigation Tasks [51.803138848305814]
We introduce BrowserArena, a live open-web agent evaluation platform that collects user-submitted tasks.<n>We identify three consistent failure modes: captcha resolution, pop-up banner removal, and direct navigation to URLs.<n>Our findings surface both the diversity and brittleness of current web agents.
arXiv Detail & Related papers (2025-10-02T15:22:21Z) - Manipulating LLM Web Agents with Indirect Prompt Injection Attack via HTML Accessibility Tree [8.511846002129522]
We show that adversaries can embed universal adversarial triggers in webpage HTML to hijack agent behavior.<n>Our system demonstrates high success rates across real websites in both targeted and general attacks.
arXiv Detail & Related papers (2025-07-20T03:10:13Z) - VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents [74.6761188527948]
Computer-Use Agents (CUAs) with full system access pose significant security and privacy risks.<n>We investigate Visual Prompt Injection (VPI) attacks, where malicious instructions are visually embedded within rendered user interfaces.<n>Our empirical study shows that current CUAs and BUAs can be deceived at rates of up to 51% and 100%, respectively, on certain platforms.
arXiv Detail & Related papers (2025-06-03T05:21:50Z) - The Hidden Dangers of Browsing AI Agents [0.0]
This paper presents a comprehensive security evaluation of such agents, focusing on systemic vulnerabilities across multiple architectural layers.<n>Our work outlines the first end-to-end threat model for browsing agents and provides actionable guidance for securing their deployment in real-world environments.
arXiv Detail & Related papers (2025-05-19T13:10:29Z) - Defending against Indirect Prompt Injection by Instruction Detection [109.30156975159561]
InstructDetector is a novel detection-based approach that leverages the behavioral states of LLMs to identify potential IPI attacks.<n>InstructDetector achieves a detection accuracy of 99.60% in the in-domain setting and 96.90% in the out-of-domain setting, and reduces the attack success rate to just 0.03% on the BIPIA benchmark.
arXiv Detail & Related papers (2025-05-08T13:04:45Z) - Enhancing Webshell Detection With Deep Learning-Powered Methods [0.6390468088226495]
Webshell attacks are becoming more common, requiring robust detection mechanisms to protect web applications.<n>The dissertation proposes ASAF, an advanced DL-Powered Source-Code Scanning Framework that uses signature-based methods and deep learning algorithms to detect known and unknown webshells.<n>Second, the dissertation introduces a deep neural network that detects webshells using real-time HTTP traffic analysis of web applications.
arXiv Detail & Related papers (2024-12-07T04:26:36Z) - SoK: Analysis techniques for WebAssembly [0.0]
WebAssembly is a low-level bytecode language that allows languages like C, C++, and Rust to be executed in the browser at near-native performance.
Vulnerabilities in memory-unsafe languages, like C and C++, can translate into vulnerabilities in WebAssembly binaries.
WebAssembly has been used for malicious purposes like cryptojacking.
arXiv Detail & Related papers (2024-01-11T14:28:13Z) - BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive
Learning [85.2564206440109]
This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses.
We introduce the emphtoolns attack, which is resistant to backdoor detection and model fine-tuning defenses.
arXiv Detail & Related papers (2023-11-20T02:21:49Z) - Static Semantics Reconstruction for Enhancing JavaScript-WebAssembly Multilingual Malware Detection [51.15122099046214]
WebAssembly allows attackers to hide the malicious functionalities of JavaScript malware in cross-language interoperations.
The detection of JavaScript-WebAssembly multilingual malware (JWMM) is challenging due to the complex interoperations and semantic diversity between JavaScript and WebAssembly.
We present JWBinder, the first technique aimed at enhancing the static detection of JWMM.
arXiv Detail & Related papers (2023-10-26T10:59:45Z) - Open-set Adversarial Defense [93.25058425356694]
We show that open-set recognition systems are vulnerable to adversarial attacks.
Motivated by this observation, we emphasize the need of an Open-Set Adrial Defense (OSAD) mechanism.
This paper proposes an Open-Set Defense Network (OSDN) as a solution to the OSAD problem.
arXiv Detail & Related papers (2020-09-02T04:35:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.