Spam and Phishing Detection

Latest papers

• A Computer Vision Approach to Visual Fraud Detection in Phishing Websites Using YOLOv8

  Basil Sajid Shaikh, Hajar Homayouni · 29 September 2026

  Phishing remains one of the most common vectors for financial and identity fraud, and most detection systems still rely on inspecting a page's URL, HTML markup, or domain registration history. These signals are easy for an attacker to rotate or obfuscate, and they say very little about what actually…

• Business Compromise Detection with Agentic AI and LLM-driven Knowledge Discovery

  Diego Palma, Kyu Bin Kim, Zhen Han, Allbright Dsouza, Zhiyuan Liu · 29 September 2026

  Detecting compromised business ad accounts is a challenge in digital advertising, as attackers exploit hijacked accounts to launch fraudulent campaigns. Large Language Model (LLM) agents show promise for integrity enforcement, but hallucinated mistakes on hard cases create business friction. In a st…

• Prompt Injection Detection for Email Agents Through Attack Chain Modeling

  Ahmad Hashmi, Dhyey Patel, Yunting Yin · 28 September 2026

  Large language model email assistants are particularly vulnerable to indirect prompt injection because untrusted email content can be retrieved into the model context and influence subsequent tool use. Existing prompt injection detectors mainly formulate this problem as binary malicious text classif…

• A Corpus of Real Scam- and Spam-Call Conversations from an Active Voice-Agent Honeypot

  Ethan Traister, Dennis Tsang Ng, Siyu Zhang, Huaiyu Guo, Tommy Duong, Tyler Wu, Yuchen Zhou, Xingyu Shen, Jiaqi Wu, Simiao Ren · 25 September 2026

  Real conversations between fraudsters and their targets are among the most informative artifacts for studying telephone scams, yet also the scarcest: passive honeypots overwhelmingly capture automated messages and hang-ups, large-scale studies characterize call metadata rather than dialogue, and man…

• SR-Fraud: An Outcome-Supervised Reflective LLM Agent Framework for Non-Stationary Payment Fraud Detection

  Xuwei Tan, Yao Ma, Xueru Zhang · 24 September 2026

  Real-time payment fraud detection is a non-stationary streaming prediction problem: adversaries adapt before supervised labels mature, and localized burst attacks can cause losses before retraining. Production systems typically rely on tabular classifiers and rules, which can struggle to capture the…

• CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era

  Yipeng Qian, Pengjie Zhao, Chaoxi Niu · 22 September 2026

  Social bot detection is essential for protecting online platforms from misinformation amplification, coordinated manipulation, and distorted public discourse. However, large language models have made social bots much harder to detect from text alone because semantic camouflage is now cheap, fluent, …

• Open-Jev Judgments on CallScreenBench: Calibrated One-Pass Scam Screening with a Small Language Model

  Simiao Ren, Kidus Zewde, Xingyu Shen, Yuchen Zhou, Dennis Ng, Ankit Raj, Tommy Duong, Yuxin Zhang, Neo Tiangratanakul · 22 September 2026

  Screening a phone call for fraud needs a trustworthy probability after every caller turn, in milliseconds. Jev-style typed decisions promise exactly that: declared options go in, one calibrated probability per option comes out of a single forward pass, with no generated text. We test an open impleme…

• AURA: Adaptive Uncertainty-Routed Analysis for Email Threat Detection

  Omran Berjawi, Walid fahs, Rida Khatoun · 18 September 2026

  Email spam and phishing attacks remain a critical security threat. Adversaries increasingly exploit large language models to craft contextually convincing malicious messages, and existing spam detection systems often struggle to keep pace. Generalization across diverse and evolving attack scenarios …

• When Does the Public Become Suspicious of Bots? Demand-Side Evidence from Botometer Query Logs

  Tu\u{g}rulcan Elmas · 18 September 2026

  We study private bot-checking behavior from the demand side: when people suspect an account is automated, whom they suspect, and what follows. Using Botometer's server-side query logs, the most widely used bot-detection service, we treat each query as a behavioural trace of suspicion. We analyze ove…

• RiskChainBench: A Benchmark for Obfuscated Platform Message Restoration and Evidence-Grounded Web Investigation

  ZhuoXin Liu, Zhiming Ma, Ying Zhang, Mengzheng Yang, Yifan Wang, Zhengqi Huang, Yanhan Zhou, Zekun Lin, Jun Zhang, Shun Zhang, Yue Chen, Qiao Zhao, Peng Chen · 16 September 2026

  Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users through disguised links to services associated with pornography, fraud, gambling, or illicit transactions. Existing benchmarks evaluate obfuscated text …

• CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification

  Mainak Sen, Kumar Sankar Ray, Amlan Chakrabarti · 10 September 2026

  In phishing detection, machine learning classifiers act as a first line of defense, but the false positives they produce are triaged by human analysts. The excessive false alarms cause alert fatigue that erodes human oversight. We propose CoGReV, a hybrid framework that augments standard machine lea…

• Multilingual Models for Check-Worthy Social Media Posts Detection

  Sebastian Kula · 7 September 2026

  This work presents an extensive study of transformer-based NLP models application for detection of social media posts that contain verifiable factual claims and harmful claims. The study covers various activities, including dataset collection, dataset pre-processing, architecture selection, setup of…

• ReCAST: Restoration-aware Cascaded Stage-wise Training for Obfuscated SMS Risk Classification

  Jieyun Huang, Yi Shen, Kaikai Zhao, Jiangze Yan, Wenjing Zhang, Ping Chen, Ning Wang, Zhaoxiang Liu, Kai Wang, Shiguo Lian · 7 September 2026

  Fraudulent messages sent via Short Message Service (SMS) are increasingly obfuscated to evade cost-conscious classifiers in production systems. In Chinese SMS, attackers can exploit a wide range of carefully crafted obfuscation strategies to hide risk-bearing phrases while preserving human readabili…

• Mind the Gap: Robustness Risks in PII Detection Systems

  Adeel Zafar, Slawomir Nowaczyk · 4 September 2026

  Personally Identifiable Information (PII) detection is a foundational component of data protection infrastructure where missed entities constitute direct privacy and security risks. Although modern PII systems report strong performance on standard benchmarks, we show that these evaluations mask subs…

• Incremental Risk Assessment of Progressive Elder Financial Scams via Instruction-Tuned Small Language Models

  Parviz Ghafariasl, Weimin Fu, Xiaolong Guo, Shing I. Chang · 2 September 2026

  Financial scams targeting older adults increasingly occur through text and voice channels such as email, SMS, and phone calls, unfolding over multiple conversational turns that begin with impersonation or casual contact, escalate through trust building and urgency, and culminate in requests for sens…

• Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate

  Ethan Traister, Ankit Raj, Jiaqi Gan, Xingyu Shen, Tyler Wu, Yuchen Zhou, Tommy Duong, Kidus Zewde, Siying Chen, Simiao Ren · 26 August 2026

  Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and spam calls -- 913 hours of audio and 330,956 transcribed turns from 5,780 distinct numbers -- collected over 54 days by an AI voice-agent honeypot tha…

• FraudBench: Stress-Testing Policy-Grounded Banking Agents Against Adaptive Fraud

  Dheeraj Mohandas Pai, Lu Xian · 20 August 2026

  Conversational agents now act for end users through tools while holding access to customer databases and internal policy documents that a caller can reach through dialogue alone. Banking is the clearest case: the same agent that answers a question can also change contact details, reset a PIN, or mov…

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

  Unai Agirre, Imanol Jerico, Felipe Castaño, Andrea Venturi, Francesco Zola · 18 August 2026

  Phishing remains a persistent and evolving cybersecurity threat, with attack volumes reaching record levels. This growth is driven by the industrialization of phishing through widely available phishing kits and reusable templates, which enable cybercriminals to rapidly generate and deploy large numb…

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

  Jerson Francia, Derek Hansen, Benjamin Schooley, Matthew Taylor, Shydra Valynn Murray, Rebekah Cornelius, Greg Snow · 18 August 2026

  Personalized phishing is difficult to defend against because messages can be tailored to a target's work, interests, and social context. Large language models may make such tailoring faster and easier, but it remains unclear whether messages produced from simple prompts are more convincing than thos…

• What Does It Take to Detect an AI Agent? Minimal Feature Sets for Behavioral Detection under Browser Automation

  Vishisht Choudhary, Lukas Schmidt, Anne Zo\"e Kenntner, Feras Skhab, Michel Osswald, Jens Ernstberger · 30 July 2026

  Bot detectors deployed at scale treat traffic as binary: human or bot. This assumption breaks when AI agents browse the web through browser automation, a traffic class that is neither and that binary classifiers structurally cannot represent. We present a three-class detection framework distinguishi…

• Traceable LLM Reasoning for Fake-Order Fraud Detection

  Siqi You, Bingsong Xu, Zhixian Zheng, Xinjian Peng, Yang Xie, Ying Wang, Jiarong Xu · 28 July 2026

  Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features, produce black-box decisions, and provide limited interpretability. To address these limitations, we propose DeepScrub, …

• Mapping the Reddit Bot Ecosystem: Taxonomy and Evolution

  Qiusi Sun, Thomas Gaskin, Branko Blagojevic, Milena Tsvetkova · 28 July 2026

  Automated agents increasingly participate in online communities, yet their population structure and roles remain poorly understood. Using a dataset of 3,389 identified bots and their full activity histories, we construct a taxonomy of bot "species" on the news aggregation and social media platform R…

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

  Tanveer Ahmed, Seyedali Pourmoafil · 22 July 2026

  Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them. Most reported detection accuracies, however, are measured on clean, in-distribution test data rather than on emails deliberately altered to evade detection. This …

• LLM Unlearning for Cyber Defense: A Survey on Methods, Challenges, and Emerging Threats

  Ruppikha Sree Shankar, Abhishek Bhardwaj, Arnav Doshi, Anusri Nagarajan, Troy Paulus Asia, Saptarshi Sengupta · 21 July 2026

  LLMs are increasingly deployed in security-critical systems across healthcare, finance, education, and decision support, yet their inability to forget creates serious cybersecurity, privacy, and safety risks. Sensitive personal information, copyrighted material, hazardous domain knowledge, and memor…

• An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory

  Ahmed Omar Salim Adnan, Yogananda Manjunath, Shivanjali Khare · 14 July 2026

  Following the rapid progress of generative Artificial Intelligence, there is a growing threat posed by conversational scams. These scams often span over multiple weeks or months, gradually build trust and request for money or sensitive information. Existing scam-detection systems mainly focus on iso…