arXiv:2511.12085v3 Announce Type: replace-cross Abstract: Phishing and related cyber threats are becoming increasingly sophisticated, with email-based phishing remaining the most persistent attack vector. These attacks exploit human vulnerabilities to deliver malware or gain unauthorized access to sensitive information. Transformer-based models enhance phishing detection through robust contextual language understanding; yet they are often regarded as black boxes due to a lack of interpretability. Moreover, recent AI-enabled attacks further undermine model resilience. To address these challenge
The increasing sophistication of cyber threats and AI-enabled attacks necessitates more robust and explainable phishing detection mechanisms, pushing for immediate advancements in security protocols.
Phishing remains a critical vulnerability across all sectors, and advancements in detection, especially with explainable AI, are vital for protecting sensitive information and maintaining operational security against evolving threats.
The explicit focus on explainability in Transformer-based models for phishing detection moves the field beyond 'black-box' solutions, offering greater transparency and potentially more trustworthy security tools.
- · Cybersecurity industry
- · Organizations handling sensitive data
- · AI developers focused on interpretability
- · Cybercriminals
- · Organizations with outdated security infrastructure
- · Legacy signature-based detection systems
Improved detection rates for sophisticated phishing attacks, reducing data breaches and financial losses.
Increased adoption of explainable AI in other cybersecurity domains due to demonstrated success in phishing detection.
A potential arms race where AI-powered defenses force attackers to develop even more advanced AI-driven obfuscation techniques, leading to cyclical innovation.
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