AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification

The rapid proliferation of digitalization and the growing reliance on internet-based technologies by individuals and organizations have led to a significant escalation in the frequency and sophistication of cyberattacks. As attackers continuously refine their methods to evade conventional defense mechanisms, antivirus solutions, despite their widespread utilization as primary security tools, face increasing challenges in addressing these evolving threats. This study introduces AV-Teller, a novel framework designed for analyzing antivirus behavior through interactions with web browsers. AV-Teller reveals weaknesses in antivirus detection mechanisms by highlighting ways in which web browser interactions may inadvertently expose critical aspects of antivirus operations. The framework provides key insights into the vulnerabilities inherent to these detection processes and their implications for the interplay between antivirus systems and modern web technologies. To assess the efficacy of the AV-Teller in detecting antivirus via web browsers, the framework evaluates three detection scenarios: Document Object Model (DOM) Monitoring-Based Detection, Signature-Based Detection, and Phishing Page-Based Detection. The results revealed performance inconsistencies: 16 products (57%) failed to respond to any tested scenarios, exhibiting deficiencies in threat mitigation capabilities. Of the 12 products (43%) that successfully handled three scenarios, 9 (75%) inadvertently disclosed identifiable antivirus metadata during assessments, thereby enabling attackers to pinpoint specific antivirus solutions and exploit their vulnerabilities. These findings highlight critical gaps in the interaction between antivirus systems and web technologies, exposing systemic flaws in existing security mechanisms. The inadvertent exposure of sensitive antivirus data underscores the necessity for robust data handling protocols, necessitating collaboration between antivirus developers and web technology stakeholders to design secure frameworks. By exposing these risks, the AV-Teller framework elucidates the limitations of current defenses and establishes a foundation for the enhancement of antivirus technologies to address emerging cyber threats effectively.

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Publication Details

Journal
Applied Sciences
Published
2025-05-02
DOI
https://doi.org/10.3390/app15095059
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification

Hyun‐Jun Jang, Ki-Woong Park, Mohsen Ali Alawami
Applied Sciences
Advanced Malware Detection Techniques
article

AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification

Hyun‐Jun Jang, Ki-Woong Park, Mohsen Ali Alawami
article en

Abstract

The rapid proliferation of digitalization and the growing reliance on internet-based technologies by individuals and organizations have led to a significant escalation in the frequency and sophistication of cyberattacks. As attackers continuously refine their methods to evade conventional defense mechanisms, antivirus solutions, despite their widespread utilization as primary security tools, face increasing challenges in addressing these evolving threats. This study introduces AV-Teller, a novel framework designed for analyzing antivirus behavior through interactions with web browsers. AV-Teller reveals weaknesses in antivirus detection mechanisms by highlighting ways in which web browser interactions may inadvertently expose critical aspects of antivirus operations. The framework provides key insights into the vulnerabilities inherent to these detection processes and their implications for the interplay between antivirus systems and modern web technologies. To assess the efficacy of the AV-Teller in detecting antivirus via web browsers, the framework evaluates three detection scenarios: Document Object Model (DOM) Monitoring-Based Detection, Signature-Based Detection, and Phishing Page-Based Detection. The results revealed performance inconsistencies: 16 products (57%) failed to respond to any tested scenarios, exhibiting deficiencies in threat mitigation capabilities. Of the 12 products (43%) that successfully handled three scenarios, 9 (75%) inadvertently disclosed identifiable antivirus metadata during assessments, thereby enabling attackers to pinpoint specific antivirus solutions and exploit their vulnerabilities. These findings highlight critical gaps in the interaction between antivirus systems and web technologies, exposing systemic flaws in existing security mechanisms. The inadvertent exposure of sensitive antivirus data underscores the necessity for robust data handling protocols, necessitating collaboration between antivirus developers and web technology stakeholders to design secure frameworks. By exposing these risks, the AV-Teller framework elucidates the limitations of current defenses and establishes a foundation for the enhancement of antivirus technologies to address emerging cyber threats effectively.

Applied SciencesVol. 15(9)
Sejong University (KR), Hankuk University of Foreign Studies (KR)
National Research Foundation, National Research Foundation of Korea, Institute for Information and Communications Technology Promotion
Openalex Percentile: Top 90%
Advanced Malware Detection Techniques
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