# Decoding the Threat Landscape : ChatGPT, FraudGPT, and WormGPT in Social Engineering Attacks

arXiv: https://arxiv.org/abs/2310.05595 · HTML: https://mlsanity.com/paper/2310.05595/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2023.

## Built on by (most cited first, 23 of 23)

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- [JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models](https://mlsanity.com/paper/2406.09321/) (2024)
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- [Mapping the Ethics of Generative AI: A Comprehensive Scoping Review](https://mlsanity.com/paper/2402.08323/) (2024)
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- [Review of Generative AI Methods in Cybersecurity](https://mlsanity.com/paper/2403.08701/) (2024)
- [Do LLMs Consider Security? An Empirical Study on Responses to Programming Questions](https://mlsanity.com/paper/2502.14202/) (2025)
- [RedTeamLLM: an Agentic AI framework for offensive security](https://mlsanity.com/paper/2505.06913/) (2025)
- [Automated Privacy Information Annotation in Large Language Model Interactions](https://mlsanity.com/paper/2505.20910/) (2025)
- [Survey on AI Ethics: A Socio-technical Perspective](https://mlsanity.com/paper/2311.17228/) (2023)
- [Position: It's Time to Act on the Risk of Efficient Personalized Text Generation](https://mlsanity.com/paper/2502.06560/) (2025)
- [UNSEEN: A Cross-Stack LLM Unlearning Defense against AR-LLM Social Engineering Attacks](https://mlsanity.com/paper/2604.23141/) (2026)
- [PhySE: A Psychological Framework for Real-Time AR-LLM Social Engineering Attacks](https://mlsanity.com/paper/2604.23148/) (2026)
- [Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data](https://mlsanity.com/paper/2505.09974/) (2025)
- [PhishKey: A Novel Centroid-Based Approach for Enhanced Phishing Detection Using Adaptive HTML Component Extraction](https://mlsanity.com/paper/2506.21106/) (2025)

## Abstract (arXiv)

In the ever-evolving realm of cybersecurity, the rise of generative AI models like ChatGPT, FraudGPT, and WormGPT has introduced both innovative solutions and unprecedented challenges. This research delves into the multifaceted applications of generative AI in social engineering attacks, offering insights into the evolving threat landscape using the blog mining technique. Generative AI models have revolutionized the field of cyberattacks, empowering malicious actors to craft convincing and personalized phishing lures, manipulate public opinion through deepfakes, and exploit human cognitive biases. These models, ChatGPT, FraudGPT, and WormGPT, have augmented existing threats and ushered in new dimensions of risk. From phishing campaigns that mimic trusted organizations to deepfake technology impersonating authoritative figures, we explore how generative AI amplifies the arsenal of cybercriminals. Furthermore, we shed light on the vulnerabilities that AI-driven social engineering exploits, including psychological manipulation, targeted phishing, and the crisis of authenticity. To counter these threats, we outline a range of strategies, including traditional security measures, AI-powered security solutions, and collaborative approaches in cybersecurity. We emphasize the importance of staying vigilant, fostering awareness, and strengthening regulations in the battle against AI-enhanced social engineering attacks. In an environment characterized by the rapid evolution of AI models and a lack of training data, defending against generative AI threats requires constant adaptation and the collective efforts of individuals, organizations, and governments. This research seeks to provide a comprehensive understanding of the dynamic interplay between generative AI and social engineering attacks, equipping stakeholders with the knowledge to navigate this intricate cybersecurity landscape.