Prompted Contextual Vectors for Spear-Phishing Detection
<|reference_start|>Prompted Contextual Vectors for Spear-Phishing Detection: 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 an innovative 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.<|reference_end|>
@article{nahmias2024prompted,
    title={Prompted Contextual Vectors for Spear-Phishing Detection},
    author={Daniel Nahmias, Gal Engelberg, Dan Klein, Asaf Shabtai},
    journal={arXiv preprint arXiv:2402.08309},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08309},
    primaryClass={cs.LG cs.CL cs.CR}
}