# Targeted Phishing Campaigns using Large Scale Language Models

arXiv: https://arxiv.org/abs/2301.00665 · HTML: https://mlsanity.com/paper/2301.00665/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 30 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation](https://mlsanity.com/paper/1906.03497/) (2019)
- [Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers](https://mlsanity.com/paper/2203.07983/) (2022)
- [Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements](https://mlsanity.com/paper/2001.00623/) (2020)
- [Automated Crowdturfing Attacks and Defenses in Online Review Systems](https://mlsanity.com/paper/1708.08151/) (2017)
- [Automated email Generation for Targeted Attacks using Natural Language](https://mlsanity.com/paper/1908.06893/) (2019)
- [Neural Text Generation: A Practical Guide](https://mlsanity.com/paper/1711.09534/) (2017)

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

- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)
- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [Provably Robust Multi-bit Watermarking for AI-generated Text](https://mlsanity.com/paper/2401.16820/) (2024)
- [Beyond the Safeguards: Exploring the Security Risks of ChatGPT](https://mlsanity.com/paper/2305.08005/) (2023)
- [BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B](https://mlsanity.com/paper/2311.00117/) (2023)
- [Large Language Models in Cybersecurity: State-of-the-Art](https://mlsanity.com/paper/2402.00891/) (2024)
- [KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection](https://mlsanity.com/paper/2403.02253/) (2024)
- [Generating Phishing Attacks using ChatGPT](https://mlsanity.com/paper/2305.05133/) (2023)
- [From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude](https://mlsanity.com/paper/2310.19181/) (2023)
- [A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models](https://mlsanity.com/paper/2311.11415/) (2023)
- [Chatbots in a Honeypot World](https://mlsanity.com/paper/2301.03771/) (2023)
- [Cyber Sentinel: Exploring Conversational Agents in Streamlining Security Tasks with GPT-4](https://mlsanity.com/paper/2309.16422/) (2023)
- [Scamming the Scammers: Using ChatGPT to Reply Mails for Wasting Time and Resources](https://mlsanity.com/paper/2303.13521/) (2023)
- [Chatbots to ChatGPT in a Cybersecurity Space: Evolution, Vulnerabilities, Attacks, Challenges, and Future Recommendations](https://mlsanity.com/paper/2306.09255/) (2023)
- [Algorithmic Ghost in the Research Shell: Large Language Models and Academic Knowledge Creation in Management Research](https://mlsanity.com/paper/2303.07304/) (2023)
- [An Empirical Study of AI-based Smart Contract Creation](https://mlsanity.com/paper/2308.02955/) (2023)
- [Is Generative AI the Next Tactical Cyber Weapon For Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks](https://mlsanity.com/paper/2408.12806/) (2024)
- [dgMARK: Decoding-Guided Watermarking for Diffusion Language Models](https://mlsanity.com/paper/2601.22985/) (2026)
- [Embracing the Generative AI Revolution: Advancing Tertiary Education in Cybersecurity with GPT](https://mlsanity.com/paper/2403.11402/) (2024)
- [Towards Neural Network based Cognitive Models of Dynamic Decision-Making by Humans](https://mlsanity.com/paper/2407.17622/) (2024)
- [Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets](https://mlsanity.com/paper/2409.04286/) (2024)
- [CTIGuardian: A Few-Shot Framework for Mitigating Privacy Leakage in Fine-Tuned LLMs](https://mlsanity.com/paper/2512.12914/) (2025)

## Abstract (arXiv)

In this research, we aim to explore the potential of natural language models (NLMs) such as GPT-3 and GPT-2 to generate effective phishing emails. Phishing emails are fraudulent messages that aim to trick individuals into revealing sensitive information or taking actions that benefit the attackers. We propose a framework for evaluating the performance of NLMs in generating these types of emails based on various criteria, including the quality of the generated text, the ability to bypass spam filters, and the success rate of tricking individuals. Our evaluations show that NLMs are capable of generating phishing emails that are difficult to detect and that have a high success rate in tricking individuals, but their effectiveness varies based on the specific NLM and training data used. Our research indicates that NLMs could have a significant impact on the prevalence of phishing attacks and emphasizes the need for further study on the ethical and security implications of using NLMs for malicious purposes.