arXiv:2512.10104v2 Announce Type: cross Abstract: Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion. As systems increasingly deploy Large Language Models (LLMs) applications, these systems face evolving phishing email threats that exploit their fundamental architectures. Current LLMs require substantial hardening before deployment in email security systems, particularly against coordinated multi-vector attacks that exploit architectural vulnerabilities. This paper proposes LLMPEA, an LLM-based framework to detect phishing email attacks across mul
The increasing deployment of Large Language Models (LLMs) in critical applications, including email security, creates new vulnerabilities that cyber attackers are actively exploiting.
As LLMs become ubiquitous, securing them against sophisticated phishing attacks is paramount to preventing widespread cyber intrusion and maintaining trust in AI systems.
Traditional phishing detection methods are becoming less effective against LLM-crafted attacks, necessitating new AI-based defense mechanisms like LLMPEA to secure digital communications.
- · Cybersecurity firms developing AI-native defenses
- · Organizations investing in advanced email security
- · Academic researchers in AI security
- · Organizations with inadequate LLM security protocols
- · Users susceptible to sophisticated phishing
- · Legacy cybersecurity solution providers
Increased investment and research in securing AI-powered systems against adversarial attacks will follow.
The development of a new arms race between AI-driven attack vectors and AI-driven defense mechanisms will accelerate.
This could lead to regulatory pressures for mandatory AI security audits and certifications for deployed LLMs.
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Read at arXiv cs.AI