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About this paper
Khalifa Afane, Wenqi Wei, Ying Mao, Junaid Farooq, Juntao Chen
Researchers from Fordham University and the University of Michigan-Dearborn analyzed how Large Language Models can create sophisticated phishing emails with higher evasion rates while also exploring their potential for strengthening detection systems. The study found that LLM-rephrased emails achieved a 16.4% evasion rate, but training detection models on LLM-augmented datasets significantly improved their ability to detect these advanced threats.
Key insights
- LLM-rephrased phishing emails significantly narrow the decision boundary between legitimate and malicious content, making them harder for detection systems to classify.
- Current traditional, machine learning, and even LLM-based phishing detection systems experience performance degradation when faced with LLM-enhanced phishing emails.
- Training machine learning models on LLM-augmented datasets, containing diverse variations of phishing emails, substantially improves their ability to detect sophisticated LLM-rephrased attacks.
Summary by alphaXiv.
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