# Chatbots in a Botnet World

arXiv: https://arxiv.org/abs/2212.11126 · HTML: https://mlsanity.com/paper/2212.11126/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 18 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [The Radicalization Risks of GPT-3 and Advanced Neural Language Models](https://mlsanity.com/paper/2009.06807/) (2020)
- [The Lovelace 2.0 Test of Artificial Creativity and Intelligence](https://mlsanity.com/paper/1410.6142/) (2014)
- [Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach](https://mlsanity.com/paper/2112.01724/) (2021)

## Built on by (most cited first, 8 of 8)

- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era](https://mlsanity.com/paper/2304.06488/) (2023)
- [How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study](https://mlsanity.com/paper/2302.10916/) (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)
- [Numeracy from Literacy: Data Science as an Emergent Skill from Large Language Models](https://mlsanity.com/paper/2301.13382/) (2023)
- [Grading Conversational Responses Of Chatbots](https://mlsanity.com/paper/2303.12038/) (2023)

## Abstract (arXiv)

Question-and-answer formats provide a novel experimental platform for investigating cybersecurity questions. Unlike previous chatbots, the latest ChatGPT model from OpenAI supports an advanced understanding of complex coding questions. The research demonstrates thirteen coding tasks that generally qualify as stages in the MITRE ATT&CK framework, ranging from credential access to defense evasion. With varying success, the experimental prompts generate examples of keyloggers, logic bombs, obfuscated worms, and payment-fulfilled ransomware. The empirical results illustrate cases that support the broad gain of functionality, including self-replication and self-modification, evasion, and strategic understanding of complex cybersecurity goals. One surprising feature of ChatGPT as a language-only model centers on its ability to spawn coding approaches that yield images that obfuscate or embed executable programming steps or links.