# From Text to MITRE Techniques: Exploring the Malicious Use of Large Language Models for Generating Cyber Attack Payloads

arXiv: https://arxiv.org/abs/2305.15336 · HTML: https://mlsanity.com/paper/2305.15336/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2023.

## Built on by (most cited first, 30 of 32)

- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://mlsanity.com/paper/2308.05374/) (2023)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies](https://mlsanity.com/paper/2407.19354/) (2024)
- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems](https://mlsanity.com/paper/2401.05778/) (2024)
- [Design Principles for Generative AI Applications](https://mlsanity.com/paper/2401.14484/) (2024)
- [Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices](https://mlsanity.com/paper/2403.12503/) (2024)
- [Large Language Models in Cybersecurity: State-of-the-Art](https://mlsanity.com/paper/2402.00891/) (2024)
- [Cyber-Zero: Training Cybersecurity Agents without Runtime](https://mlsanity.com/paper/2508.00910/) (2025)
- [D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security](https://mlsanity.com/paper/2502.10931/) (2025)
- [RatGPT: Turning online LLMs into Proxies for Malware Attacks](https://mlsanity.com/paper/2308.09183/) (2023)
- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)
- [Training Language Model Agents to Find Vulnerabilities with CTF-Dojo](https://mlsanity.com/paper/2508.18370/) (2025)
- [A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models](https://mlsanity.com/paper/2311.11415/) (2023)
- [U-shaped and Inverted-U Scaling behind Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2410.01692/) (2024)
- [Review of Generative AI Methods in Cybersecurity](https://mlsanity.com/paper/2403.08701/) (2024)
- [Discovering Forbidden Topics in Language Models](https://mlsanity.com/paper/2505.17441/) (2025)
- [Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM](https://mlsanity.com/paper/2508.05775/) (2025)
- [Warfare:Breaking the Watermark Protection of AI-Generated Content](https://mlsanity.com/paper/2310.07726/) (2023)
- [A Survey of Large Language Models in Cybersecurity](https://mlsanity.com/paper/2402.16968/) (2024)
- [ChatGPT Code Detection: Techniques for Uncovering the Source of Code](https://mlsanity.com/paper/2405.15512/) (2024)
- [CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research](https://mlsanity.com/paper/2411.01176/) (2024)
- [CyberLLMInstruct: A Pseudo-malicious Dataset Revealing Safety-performance Trade-offs in Cyber Security LLM Fine-tuning](https://mlsanity.com/paper/2503.09334/) (2025)
- [Living Off the LLM: How LLMs Will Change Adversary Tactics](https://mlsanity.com/paper/2510.11398/) (2025)
- [AI In Cybersecurity Education -- Scalable Agentic CTF Design Principles and Educational Outcomes](https://mlsanity.com/paper/2603.21551/) (2026)
- [Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data](https://mlsanity.com/paper/2505.09974/) (2025)

## Abstract (arXiv)

This research article critically examines the potential risks and implications arising from the malicious utilization of large language models(LLM), focusing specifically on ChatGPT and Google's Bard. Although these large language models have numerous beneficial applications, the misuse of this technology by cybercriminals for creating offensive payloads and tools is a significant concern. In this study, we systematically generated implementable code for the top-10 MITRE Techniques prevalent in 2022, utilizing ChatGPT, and conduct a comparative analysis of its performance with Google's Bard. Our experimentation reveals that ChatGPT has the potential to enable attackers to accelerate the operation of more targeted and sophisticated attacks. Additionally, the technology provides amateur attackers with more capabilities to perform a wide range of attacks and empowers script kiddies to develop customized tools that contribute to the acceleration of cybercrime. Furthermore, LLMs significantly benefits malware authors, particularly ransomware gangs, in generating sophisticated variants of wiper and ransomware attacks with ease. On a positive note, our study also highlights how offensive security researchers and pentesters can make use of LLMs to simulate realistic attack scenarios, identify potential vulnerabilities, and better protect organizations. Overall, we conclude by emphasizing the need for increased vigilance in mitigating the risks associated with LLMs. This includes implementing robust security measures, increasing awareness and education around the potential risks of this technology, and collaborating with security experts to stay ahead of emerging threats.