# Exploring the Dark Side of AI: Advanced Phishing Attack Design and Deployment Using ChatGPT

arXiv: https://arxiv.org/abs/2309.10463 · HTML: https://mlsanity.com/paper/2309.10463/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech](https://mlsanity.com/paper/2302.07736/) (2023)
- [Generating Phishing Attacks using ChatGPT](https://mlsanity.com/paper/2305.05133/) (2023)

## Built on by (most cited first, 9 of 9)

- [AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks](https://mlsanity.com/paper/2403.01038/) (2024)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)
- [AbuseGPT: Abuse of Generative AI ChatBots to Create Smishing Campaigns](https://mlsanity.com/paper/2402.09728/) (2024)
- [Review of Generative AI Methods in Cybersecurity](https://mlsanity.com/paper/2403.08701/) (2024)
- [Killer Apps: Low-Speed, Large-Scale AI Weapons](https://mlsanity.com/paper/2402.01663/) (2024)
- [Jailbreaking Generative AI: Empowering Novices to Conduct Phishing Attacks](https://mlsanity.com/paper/2503.01395/) (2025)
- [Jailbreaking Generative AI: Multivector Phishing Threats and Transformer based Defenses](https://mlsanity.com/paper/2507.12185/) (2025)
- [Can LLM Agents Simulate Dynamic Networks? A Case Study on Email Networks with Phishing Synthesis](https://mlsanity.com/paper/2605.12507/) (2026)

## Abstract (arXiv)

This paper explores the possibility of using ChatGPT to develop advanced phishing attacks and automate their large-scale deployment. We make ChatGPT generate the following parts of a phishing attack: i) cloning a targeted website, ii) integrating code for stealing credentials, iii) obfuscating code, iv) automating website deployment on a hosting provider, v) registering a phishing domain name, and vi) integrating the website with a reverse proxy. The initial assessment of the automatically generated phishing kits highlights their rapid generation and deployment process as well as the close resemblance of the resulting pages to the target website. More broadly, we demonstrate that recent advances in AI underscore the potential risks of its misuse in phishing attacks, which can lead to their increased prevalence and severity. This highlights the necessity for enhanced countermeasures within AI systems.