Abstract
In the emerging field of Meta Computing, where data collection and integration are essential components, the threat of adversary hidden link attacks poses a significant challenge to web crawlers. In this paper, we investigate the influence of these attacks on data collection by web crawlers, which famously elude conventional detection techniques using large language models (LLMs). Empirically, we find some vulnerabilities in the current crawler mechanisms and large language model detection, especially in code inspection, and propose enhancements that will help mitigate these weaknesses. Our assessment of real-world web pages reveals the prevalence and impact of adversary hidden link attacks, emphasizing the necessity for robust countermeasures. Furthermore, we introduce a mitigation framework that integrates element visual inspection techniques. Our evaluation demonstrates the framework's efficacy in detecting and addressing these advanced cyber threats within the evolving landscape of Meta Computing.
Recommended Citation
J. Xiong et al., "Assessing The Effectiveness Of Crawlers And Large Language Models In Detecting Adversarial Hidden Link Threats In Meta Computing," High Confidence Computing, vol. 5, no. 3, article no. 100292, Elsevier, Sep 2025.
The definitive version is available at https://doi.org/10.1016/j.hcc.2024.100292
Department(s)
Computer Science
Publication Status
Open Access
Keywords and Phrases
Adversary hidden link; Content deception detection; Data integration; Large language model; Meta computing; Web crawling
International Standard Serial Number (ISSN)
2667-2952
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Elsevier, All rights reserved.
Publication Date
01 Sep 2025
