arXiv AI

A Tree-Structured Approach for Phishing Template and Attacker Attribution Analysis

arXiv:2608. 16158v1 Announce Type: cross Abstract: Phishing remains a persistent and evolving cybersecurity threat, with attack volumes reaching record levels.

arXiv Machine Learning

Jun 2

A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features

arXiv:2606. 00889v1 Announce Type: cross Abstract: Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information.

By Uche Unoke Emmanuel, Gideon Francis Oghie

efficiency

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

Jul 22

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.

By Tanveer Ahmed, Seyedali Pourmoafil

llms fine-tuning safety

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 AI

Jul 17

Pretraining Data Can Be Poisoned through Computational Propaganda

arXiv:2607. 15267v1 Announce Type: new Abstract: Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate.

By Victoria Graf, Hannaneh Hajishirzi, Noah A. Smith, David Kohlbrenner, Kyle Lo

llms safety

arXiv AI

Aug 7

IMMENSE: Inductive Multi-perspective User Classification in Social Networks

arXiv:2608. 05259v1 Announce Type: cross Abstract: Online social networks increasingly expose people to users who propagate discriminatory, hateful, and violent content.

By Francesco Benedetti, Antonio Pellicani, Gianvito Pio, Michelangelo Ceci

arXiv AI

Jul 7

Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities

arXiv:2607. 03887v1 Announce Type: cross Abstract: The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats.

By Utkarsh Tiwari, Spoorthi M, Anirudh S, Nidhin Prabhakar T. V

llms benchmarks

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

1d ago

Assessing AI-Generated vs. Human-Authored Spear Phishing SMS Attacks: An Empirical Study

arXiv:2406. 13049v3 Announce Type: replace-cross Abstract: Personalized phishing is difficult to defend against because messages can be tailored to a target's work, interests, and social context.

By Jerson Francia, Derek Hansen, Benjamin Schooley, Matthew Taylor, Shydra Valynn Murray, Rebekah Cornelius, Greg Snow

llms

arXiv AI

Jun 16

MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks

arXiv:2602. 09222v2 Announce Type: replace-cross Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf.

By Georgios Syros, Evan Rose, Brian Grinstead, Christoph Kerschbaumer, William Robertson, Cristina Nita-Rotaru, Alina Oprea

llms agents safety

arXiv AI

Jun 12

Reconstructing Template-Memorized Images from Natural Prompts

arXiv:2507. 07947v4 Announce Type: replace-cross Abstract: Recent advances in generative models, such as diffusion models, have raised concerns related to privacy, copyright infringement, and data stewardship.

By Sol Yarkoni, Mahmood Sharif, Roi Livni

diffusion safety

Related stories