The paper introduces a dataset of 10,015 real scam and spam phone calls collected over 53 days using an active voice‑agent honeypot. Each call is recorded, transcribed, and automatically labeled, yielding 328,869 turn‑level transcripts and 895 hours of audio from 5,665 distinct numbers. The corpus distinguishes between predatory‑but‑legal lead generation and outright scams, with labels validated by human review and technical checks on realism.
By Ethan Traister, Dennis Tsang Ng, Siyu Zhang, Huaiyu Guo, Tommy Duong, Tyler Wu, Yuchen Zhou, Xingyu Shen, Jiaqi Wu, Simiao Ren
ScamWorld is an interactive environment that recreates reported scam websites across various scenarios, fraud mechanisms, and languages, allowing researchers to observe fraud indicators before a web agent’s final action. The platform enables controlled, repeatable evaluation of web agents, which are increasingly used to browse, shop, and manage accounts on the open web. A study of six leading LLM-powered agents revealed deception rates between 11% and 35%, with agents more likely to proceed with payments than to provide credentials or wallet information, and some agents continuing after recognizing suspicious conditions.
By Yutao Wu, Xiao Liu, Ming Wen, Hanxun Huang, Jun Wang, Xingjun Ma
ReDence is an open‑source hybrid framework that combines a multimodal agentic Investigator model with a specialized Decider model to detect modern social engineering scams. It automates investigative planning, evidence interpretation, and decision‑making across multiple scam categories, achieving 96% accuracy and operating at least four times faster than comparable agentic models. The system outputs structured, evidence‑grounded reports that enable analysts to audit decisions and support mitigation efforts.
By Jaishnoor Kaur, Josef Hor\'a\v{c}ek, Ian Waskom, Sayak Saha Roy
arXiv:2607. 11707v1 Announce Type: cross Abstract: Following the rapid progress of generative Artificial Intelligence, there is a growing threat posed by conversational scams.
By Ahmed Omar Salim Adnan, Yogananda Manjunath, Shivanjali Khare
The article surveys fake review detection research, focusing on how pre‑trained language models (PLMs) and large language models (LLMs) influence both the generation of deceptive reviews and their detection. It reviews 211 studies from 2018 to early 2026, categorizing methods by evidence source—such as review text, sentiment, rating behavior, temporal metadata, user‑product graphs, multimodal content, external knowledge, and LLM‑generated signals—and by fusion level. The survey traces the evolution from traditional machine learning to PLM‑based and LLM‑based approaches, evaluates performance on Amazon, Yelp, and OpSpam benchmarks, and highlights open challenges including adversarial generation, cross‑domain transfer, uncertainty‑aware fusion, robustness to missing sources, interpretability, and trustworthy evaluation of AI‑generated deceptive content.
By Fanji Yang (Guizhou University of Finance and Economics), Huiyao Chen (Harbin Institute of Technology), Xi Yu (Guizhou University of Finance and Economics), Meishan Zhang (Harbin Institute of Technology), Xiaohong Xiao (Guizhou University of Commerce), Mingsen Deng (Guizhou University of Finance and Economics)
arXiv:2602. 05056v2 Announce Type: replace-cross Abstract: Online scams increasingly leverage fluent and context-aware social engineering strategies, creating growing demand for AI systems that explain why a message may be risky.
By Heajun An, Connor Ng, Sandesh Sharma Dulal, Junghwan Kim, Jin-Hee Cho
arXiv:2501. 14728v2 Announce Type: replace-cross Abstract: While generative artificial intelligence (GenAI) models have achieved significant success, their misuse for generating deceptive content raises growing concerns about online information security.
By Zehong Yan, Peng Qi, Wynne Hsu, Mong Li Lee
arXiv:2610.07939v1 Announce Type: new
Abstract: Due to rapid advances in Generative AI, commercial video generation tools can be used to produce fabricated surveillance footage that can fool both hum...
By Baptiste Chopin, Thomas Swearingen, Arun Ross, Antitza Dantcheva, Christian Rathgeb
VisualTrust introduces a counterfactual benchmark to evaluate how multimodal large language models handle on‑screen claims that may conflict with visual evidence. The benchmark contains 5,063 cases across 14 claim‑fact classes, each featuring a question paired with three video versions: no claim, accurate claim, and misleading claim. Results show a drop in accuracy from 90.1% to 51.9% when a misleading claim is present, and a new training‑free agent, DEAR, improves accuracy under misleading claims to 81.8–85.2% while reducing trap‑answer selection.
By Huachao Zhu, Junjielong Xu, Sicheng Li, Liang Yang, Mingtao Chen, Zhichao Hu, Li Jiang, Pinjia He
The study analyzes 10,211 real scam and spam calls collected by an AI voice‑agent honeypot, revealing that scammers operate on a templated, office‑hour schedule and use disposable numbers to recycle scripts. Callers predominantly seek identity anchors such as home addresses and dates of birth, and the amount of conversation increases with the target’s age, though the requested information remains unchanged. Early detection is feasible, with escalation predictability reaching 0.87 ROC‑AUC by the eighth line using simple bag‑of‑words models.
By Ethan Traister, Ankit Raj, Jiaqi Gan, Xingyu Shen, Tyler Wu, Yuchen Zhou, Tommy Duong, Kidus Zewde, Siying Chen, Simiao Ren
arXiv:2410. 01574v4 Announce Type: replace-cross Abstract: The rapid advancement of Generative Artificial Intelligence (GenAI) capabilities is accompanied by a concerning rise in its misuse.
By Sina Mavali, Jonas Ricker, David Pape, Asja Fischer, Lea Sch\"onherr
The paper presents a signal-theoretic machine learning framework to detect deceptive online job ads that facilitate forced labour. Using 464 verified cases from nine countries and 21 industries, the authors build multimodal models that combine computer vision, natural language processing, and semantic embeddings, achieving ROC‑AUC scores between 0.87 and 0.97. SHAP analysis identifies text quality, risk language, and visual features as key discriminators, and the authors deliver a proof‑of‑concept decision support system that outputs interpretable risk scores for practitioners.
By Sajid Siraj, Mahnaz Hosseinzadeh, Amin Vafadarnikjoo, Shuyang Li