Phishing Detection

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16 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers
Oct 13, 2026Xiang Li, Aichun Huang, Mingming Zhang +1Phishing Detection Language Model Safety Evaluation
Nankai University · Zhongguancun Laboratory
A Computer Vision Approach to Visual Fraud Detection in Phishing Websites Using YOLOv8
Sep 27, 2026Basil Sajid Shaikh, Hajar HomayouniPhishing Detection
Independent Researcher Seattle, WA, USA · Associate Professor, Dept. of Computer Science San Diego State University San Diego, CA
AURA: Adaptive Uncertainty-Routed Analysis for Email Threat Detection
Sep 17, 2026Omran Berjawi, Walid fahs, Rida KhatounDistribution Shift Robustness Phishing Detection
Institut Polytechnique de Paris, Télécom Paris, Palaiseau, France · Islamic University of Lebanon, Faculty of Engineering, Wardanieh, Lebanon
Evidence-Consistent Generative Detection under Scenario-Level Distribution Shift
Aug 21, 2026San Kim, JinYeong BakPhishing Detection OOD Generalization
Sungkyunkwan University Suwon, Republic of Korea
Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT
Jul 20, 2026Tanveer Ahmed, Seyedali PourmoafilAdversarial Robustness Cybersecurity
School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, Herts AL10 9AB, United Kingdom
An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory
Jul 13, 2026Ahmed Omar Salim Adnan, Yogananda Manjunath, Shivanjali KhareAgentic AI Phishing Detection
University of New Haven · M.S. in Data Science · Department of Electrical & Computer Engineering and Computer Science
A Hybrid, Multi-Layered Pipeline for Phishing and Threat Classification: Independently Validated URL and NLP Engines with a Calibrated Multi-Channel Fusion Stage
Jun 19, 2026Saifelden M. Ismail, Aser O. Ibrahim, Omar A. MahmoudPhishing Detection Multimodal Fusion
Department of Communications and Information Engineering Zewail City of Science and Technology, Giza, Egypt
Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems
Jun 9, 2026Warren Fernando, Nikos KomninosConcept Drift Phishing Detection
Department of Computer Science, School of Mathematics, Computer Science and Engineering, City St George’s, University of London, UK
A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features
May 30, 2026Uche Unoke Emmanuel, Gideon Francis OghieMultilayer Perceptrons Phishing Detection
Department of Cyber Security Science, School of Information and Communication Technology, Federal University of Technology, Minna, Nigeria
CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification
Apr 28, 2026Mainak Sen, Kumar Sankar Ray, Amlan ChakrabartiLogical Reasoning Cybersecurity
Techno India University, West Bengal, India · A. K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India · Electronics and Communication Sciences Unit (ECSU), Indian Statistical Institute, Kolkata, India +1
CyberCane: a privacy-bounded neuro-symbolic retrieval framework for phishing detection under distribution shift
Apr 26, 2026Safayat Bin Hakim, Aniqa Afzal, Qi Zhao +3Ontology-Based Reasoning Privacy-Preserving Language Models
α University of Maryland, Baltimore County · β Bowie State University
TraceScope: Interactive URL Triage via Decoupled Checklist Adjudication
Apr 23, 2026Haolin Zhang, William Reber, Yuxuan Zhang +2Digital Forensics Phishing Detection
Texas A&M University
The System Prompt Is the Attack Surface: How LLM Agent Configuration Shapes Security and Creates Exploitable Vulnerabilities
Mar 26, 2026Ron LitvakPhishing Detection LLM Agent Security
Independent Researcher, Columbia University
Robustness, Cost, and Attack-Surface Concentration in Phishing Detection
Mar 19, 2026Julian Allagan, Mohamed Elbakary, Zohreh Safari +4Phishing Detection Adversarial Robustness
Department of Mathematics, Computer Science, and Engineering Technology Elizabeth City State University, Elizabeth City, NC 27909, USA
Phishing the Phishers with SpecularNet: Hierarchical Graph Autoencoding for Reference-Free Web Phishing Detection
Mar 2, 2026Tailai Song, Pedro Casas, Michela MeoPhishing Detection Graph Neural Networks
Politecnico di Torino Turin, Italy · AIT Austrian Institute of Technology Vienna, Austria
X-MAP: eXplainable Misclassification Analysis and Profiling for Spam and Phishing Detection
Feb 17, 2026Qi Zhang, Dian Chen, Lance M. Kaplan +4Phishing Detection Classification
Virginia Tech, USA · U.S. Army DEVCOM Army Research Laboratory, USA · University of Oslo, Norway +2

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Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers

Xiang Li, Aichun Huang, Mingming Zhang, Zheli Liu

Nankai University · Zhongguancun Laboratory

Phishing Detection Language Model Safety Evaluation LLM-as-a-Judge Adversarial Attacks on LLMs

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TypeSafe AI's Jev is a new kind of safety judge. It generates no text and no label: it answers questions the developer defines in advance and returns a probability for each answer. The vendor admits that adversarial input can change these answers but gives no numbers. In this paper, we present the first controlled adversarial evaluation of Jev. We build a test harness that sends every system the same payload under the same policy, and run it on four public-data tasks, from rule-based ones (input moderation, injection detection, routing) to a judgment-based one (phishing detection), with fifty attack variants from thirteen families. The targets are Jev, five GPT and GLM judges, and two local classifiers (Llama Guard 3, Prompt Guard 2). We find that Jev is the most robust system on input moderation: eight forgeries of its structured input bypass none of its decisions, and a sentence asking only why someone might make a harmful request bypasses 9% of its blocks, against 56% for GPT-6 Astra. On phishing detection. However, the same forgeries bypass 19-29% of the emails it flags, and even without attack Jev detects only 62% of phishing emails, against 77-81% for the GLM judges. Jev is also the fastest judge, but it flags the most benign requests (27%). The local classifiers fail differently: a training-example statement makes Llama Guard 3 miss 62% of the phishing it had flagged. These results suggest that Jev resists attacks on tasks with a clear rule to check but is more exposed on tasks that call for an overall judgment.