arXiv AI July 22, 2026 By Tanveer Ahmed, Seyedali Pourmoafil

Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT

arXiv:2607. 18429v1 Announce Type: cross Abstract: Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 3

A Robust and Explainable Transformer-Based Framework for Phishing Email Detection

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.

By Sajad U P

llms rag safety

arXiv AI

Jun 16

Phishing Email Detection Using Large Language Models

arXiv:2512. 10104v2 Announce Type: cross Abstract: Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion.

By Najmul Hasan, Prashanth BusiReddyGari, Haitao Zhao, Yihao Ren, Jinsheng Xu, Shaohu Zhang

llms safety

arXiv Machine Learning

Jun 24

A Hybrid, Multi-Layered Pipeline for Phishing and Threat Classification: Independently Validated URL and NLP Engines with a Calibrated Multi-Channel Fusion Stage

arXiv:2606. 21690v2 Announce Type: replace-cross Abstract: Phishing is a multi-modal threat.

By Saifelden M. Ismail, Aser O. Ibrahim, Omar A. Mahmoud

nlp multimodal benchmarks

arXiv AI

Sep 10

PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariants

arXiv:2507.15393v2 Announce Type: replace-cross Abstract: Phishing email is a critical step in the cybercrime kill chain due to the high reachability of victims' email accounts and the low cost of la...

By Ruofan Liu, Yun Lin, Yuxin Wang, Xiwen Teoh, Zhenkai Liang, Gongshen Liu, Haojin Zhu, Jin Song Dong

llms rag benchmarks

arXiv Machine Learning

Jun 11

Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems

arXiv:2606. 11471v1 Announce Type: cross Abstract: The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels.

By Warren Fernando, Nikos Komninos

arXiv Computation and Language

6d ago

Prompt Injection Detection for Email Agents Through Attack Chain Modeling

The paper introduces a prompt‑injection detection framework for email assistants that models attacks as a chain of stages. It combines a text detector, stage‑specific verifiers, rule‑based risk signals, user intent consistency checks, and a logistic decision policy. Experiments on five benchmarks show the framework outperforms pretrained detectors, achieving a mean F1 of 0.406 versus 0.216, and demonstrate that training on benign emails resembling attacks reduces false alarms.

By Ahmad Hashmi, Dhyey Patel, Yunting Yin

llms agents nlp benchmarks

One email a morning, machine-written

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Phishing Email Detection Using Large Language Models

A Hybrid, Multi-Layered Pipeline for Phishing and Threat Classification: Independently Validated URL and NLP Engines with a Calibrated Multi-Channel Fusion Stage

PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariants

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Prompt Injection Detection for Email Agents Through Attack Chain Modeling