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.
arXiv:2607. 11707v1 Announce Type: cross Abstract: Following the rapid progress of generative Artificial Intelligence, there is a growing threat posed by conversational scams.
arXiv:2607. 20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.
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...
The paper presents a cumulative turn‑based risk assessment framework for detecting financial scams targeting older adults, which aggregates conversational turns and updates risk estimates at each step. A multi‑turn dialogue dataset covering investment, charity, and tech support scams is created, with annotations for risk level, score, rationale, and safety recommendation at every cumulative stage. Four small language models (Phi‑4, LLaMA‑3.2, DeepSeek‑R1, Qwen3) are fine‑tuned; Phi‑4 and LLaMA‑3.2 outperform others in turn‑aware risk estimation, demonstrating that compact models can effectively support incremental scam detection in resource‑constrained, privacy‑aware deployments.
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.
arXiv:2606. 12073v1 Announce Type: cross Abstract: Generative AI has made fluent prose cheap to produce, breaking the old promise to readers that good writing meant real thinking.
arXiv:2606. 18062v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used to fulfill users' information needs; users ask LLMs about the weather, pose educational questions, and consult them for legal assistance.
arXiv:2606. 05256v1 Announce Type: new Abstract: This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView.
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.
arXiv:2512. 10104v2 Announce Type: cross Abstract: Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion.
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 trans...
arXiv:2608.21841v1 Announce Type: new Abstract: Conversational AI increasingly shapes consequential decisions, yet users have limited support for recognizing and resisting manipulation. We present AI...