Prompted Contextual Vectors for Spear-Phishing Detection

Daniel Nahmias; Gal Engelberg; Dan Klein; Asaf Shabtai

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arXiv · 2402.08309

Prompted Contextual Vectors for Spear-Phishing Detection

Daniel Nahmias·Gal Engelberg·Dan Klein·Asaf Shabtai

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Abstract

Spear-phishing attacks present a significant security challenge, with large language models (LLMs) escalating the threat by generating convincing emails and facilitating target reconnaissance. To address this, we propose a detection approach based on a novel document vectorization method that utilizes an ensemble of LLMs to create representation vectors. By prompting LLMs to reason and respond to human-crafted questions, we quantify the presence of common persuasion principles in the email's content, producing prompted contextual document vectors for a downstream supervised machine learning model. We evaluate our method using a unique dataset generated by a proprietary system that automates target reconnaissance and spear-phishing email creation. Our method achieves a 91\% F1 score in identifying LLM-generated spear-phishing emails, with the training set comprising only traditional phishing and benign emails. Key contributions include a novel document vectorization method utilizing LLM reasoning, a publicly available dataset of high-quality spear-phishing emails, and the demonstrated effectiveness of our method in detecting such emails. This methodology can be utilized for various document classification tasks, particularly in adversarial problem domains.

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BibTeX RIS

Daniel Nahmias, Gal Engelberg, Dan Klein, Asaf Shabtai. 2024-12-24. Prompted Contextual Vectors for Spear-Phishing Detection. https://arxiv.org/abs/2402.08309

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