Scamming the Scammers: Using ChatGPT to Reply Mails for Wasting Time and Resources
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Abstract (arXiv)
The use of Artificial Intelligence (AI) to support cybersecurity operations is now a consolidated practice, e.g., to detect malicious code or configure traffic filtering policies. The recent surge of AI, generative techniques and frameworks with efficient natural language processing capabilities dramatically magnifies the number of possible applications aimed at increasing the security of the Internet. Specifically, the ability of ChatGPT to produce textual contents while mimicking realistic human interactions can be used to mitigate the plague of emails containing scams. Therefore, this paper investigates the use of AI to engage scammers in automatized and pointless communications, with the goal of wasting both their time and resources. Preliminary results showcase that ChatGPT is able to decoy scammers, thus confirming that AI is an effective tool to counteract threats delivered via mail. In addition, we highlight the multitude of implications and open research questions to be addressed in the perspective of the ubiquitous adoption of AI.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 9 of its 33 references that are in the map. First posted on arXiv 10 Feb 2023.
Builds on
9 of 33 references.
References · 9
- 2022ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reportscited by 100
- 2022ChatGPT: The End of Online Exam Integrity?cited by 88
- 2023ChatGPT is not all you need. A State of the Art Review of large Generative AI modelscited by 85
- 2022Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2cited by 55
- 2022Targeted Phishing Campaigns using Large Scale Language Modelscited by 22
- 2020Data Minimization for GDPR Compliance in Machine Learning Modelscited by 21
- 2023Toward General Design Principles for Generative AI Applicationscited by 20
- 2021A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutionscited by 9
- 2023Chatbots in a Honeypot Worldcited by 9
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Citing papers per year: 2023: 3; 2024: 3; 2025: 2.
8 papers.
- 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 134
- 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validationcited by 91
- 2024Large Language Models for Cyber Security: A Systematic Literature Reviewcited by 65
- 2024Large Language Models in Cybersecurity: State-of-the-Artcited by 34
- 2023On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writingcited by 26
- 2025Generative AI and Empirical Software Engineering: A Paradigm Shiftcited by 9
- 2024Application of AI-based Models for Online Fraud Detection and Analysiscited by 1
- 2025Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniquescited by 0
- 2025Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques18 Jul 2025
- 2025Generative AI and Empirical Software Engineering: A Paradigm Shift12 Feb 2025
- 2024Application of AI-based Models for Online Fraud Detection and Analysis25 Sep 2024
- 2024Large Language Models for Cyber Security: A Systematic Literature Review8 May 2024
- 2024Large Language Models in Cybersecurity: State-of-the-Art30 Jan 2024
- 2023On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writing7 Jun 2023
- 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey30 May 2023
- 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation19 May 2023