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This research article critically examines the potential risks andimplications arising from the malicious utilization of large languagemodels(LLM), focusing specifically on ChatGPT and Google's Bard. Although theselarge language models have numerous beneficial applications, the misuse of thistechnology by cybercriminals for creating offensive payloads and tools is asignificant concern. In this study, we systematically generated implementablecode for the top-10 MITRE Techniques prevalent in 2022, utilizing ChatGPT, andconduct a comparative analysis of its performance with Google's Bard. Ourexperimentation reveals that ChatGPT has the potential to enable attackers toaccelerate the operation of more targeted and sophisticated attacks.Additionally, the technology provides amateur attackers with more capabilitiesto perform a wide range of attacks and empowers script kiddies to developcustomized tools that contribute to the acceleration of cybercrime.Furthermore, LLMs significantly benefits malware authors, particularlyransomware gangs, in generating sophisticated variants of wiper and ransomwareattacks with ease. On a positive note, our study also highlights how offensivesecurity researchers and pentesters can make use of LLMs to simulate realisticattack scenarios, identify potential vulnerabilities, and better protectorganizations. Overall, we conclude by emphasizing the need for increasedvigilance in mitigating the risks associated with LLMs. This includesimplementing robust security measures, increasing awareness and educationaround the potential risks of this technology, and collaborating with securityexperts to stay ahead of emerging threats.

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