# Detecting Scams Using Large Language Models

arXiv: https://arxiv.org/abs/2402.03147 · HTML: https://mlsanity.com/paper/2402.03147/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2024.

## Built on by (most cited first, 10 of 10)

- [TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection](https://mlsanity.com/paper/2503.24115/) (2025)
- [Advanced Real-Time Fraud Detection Using RAG-Based LLMs](https://mlsanity.com/paper/2501.15290/) (2025)
- [Enhancing Trust and Safety in Digital Payments: An LLM-Powered Approach](https://mlsanity.com/paper/2410.19845/) (2024)
- [Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance](https://mlsanity.com/paper/2412.00621/) (2024)
- [RITFIS: Robust input testing framework for LLMs-based intelligent software](https://mlsanity.com/paper/2402.13518/) (2024)
- [SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection](https://mlsanity.com/paper/2406.06663/) (2024)
- [Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection](https://mlsanity.com/paper/2506.21443/) (2025)
- [Application of AI-based Models for Online Fraud Detection and Analysis](https://mlsanity.com/paper/2409.19022/) (2024)
- [Scam Shield: Multi-Model Voting and Fine-Tuned LLMs Against Adversarial Attacks](https://mlsanity.com/paper/2511.01746/) (2025)
- [BEACON: A Unified Behavioral-Tactical Framework for Explainable Cybercrime Analysis with Large Language Models](https://mlsanity.com/paper/2512.06555/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have gained prominence in various applications, including security. This paper explores the utility of LLMs in scam detection, a critical aspect of cybersecurity. Unlike traditional applications, we propose a novel use case for LLMs to identify scams, such as phishing, advance fee fraud, and romance scams. We present notable security applications of LLMs and discuss the unique challenges posed by scams. Specifically, we outline the key steps involved in building an effective scam detector using LLMs, emphasizing data collection, preprocessing, model selection, training, and integration into target systems. Additionally, we conduct a preliminary evaluation using GPT-3.5 and GPT-4 on a duplicated email, highlighting their proficiency in identifying common signs of phishing or scam emails. The results demonstrate the models' effectiveness in recognizing suspicious elements, but we emphasize the need for a comprehensive assessment across various language tasks. The paper concludes by underlining the importance of ongoing refinement and collaboration with cybersecurity experts to adapt to evolving threats.