# "It Warned Me Just at the Right Moment": Exploring LLM-based Real-time Detection of Phone Scams

arXiv: https://arxiv.org/abs/2502.03964 · HTML: https://mlsanity.com/paper/2502.03964/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2025.

## Built on by (most cited first, 12 of 12)

- [TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection](https://mlsanity.com/paper/2503.24115/) (2025)
- [Open-Jev Judgments on CallScreenBench: Calibrated One-Pass Scam Screening with a Small Language Model](https://mlsanity.com/paper/2609.23959/) (2026)
- [ScamAgents: How AI Agents Can Simulate Human-Level Scam Calls](https://mlsanity.com/paper/2508.06457/) (2025)
- [AI-in-the-Loop: Privacy Preserving Real-Time Scam Detection and Conversational Scambaiting by Leveraging LLMs and Federated Learning](https://mlsanity.com/paper/2509.05362/) (2025)
- [Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate](https://mlsanity.com/paper/2608.24127/) (2026)
- [Vishing-Tactics-Bench: Forecasting Exploitation Trajectories in Voice Phishing Calls](https://mlsanity.com/paper/2609.07151/) (2026)
- [One Size Fits All? A Modular Adaptive Sanitization Kit (MASK) for Customizable Privacy-Preserving Phone Scam Detection](https://mlsanity.com/paper/2510.18493/) (2025)
- [SCRIPTMIND: Crime Script Inference and Cognitive Evaluation for LLM-based Social Engineering Scam Detection System](https://mlsanity.com/paper/2601.13581/) (2026)
- [ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage](https://mlsanity.com/paper/2605.16363/) (2026)
- [Poster: Exploring the Limits of Audio-Based Detection of Turkish Phone Call Scams](https://mlsanity.com/paper/2606.24523/) (2026)
- [Semi-Automated Detection of Gaps in LLM Security Knowledge](https://mlsanity.com/paper/2607.18496/) (2026)
- [Before the Warning Comes Too Late: Incremental Phone-Scam Detection from Speech](https://mlsanity.com/paper/2609.20223/) (2026)

## Abstract (arXiv)

Despite living in the era of the internet, phone-based scams remain one of the most prevalent forms of scams. These scams aim to exploit victims for financial gain, causing both monetary losses and psychological distress. While governments, industries, and academia have actively introduced various countermeasures, scammers also continue to evolve their tactics, making phone scams a persistent threat. To combat these increasingly sophisticated scams, detection technologies must also advance. In this work, we propose a framework for modeling scam calls and introduce an LLM-based real-time detection approach, which assesses fraudulent intent in conversations, further providing immediate warnings to users to mitigate harm. Through experiments, we evaluate the method's performance and analyze key factors influencing its effectiveness. This analysis enables us to refine the method to improve precision while exploring the trade-off between recall and timeliness, paving the way for future directions in this critical area of research.