Large Language Models as a Cyber Threat: Towards Countering LLM-based Spam Attacks

The rapid advancement and accessibility of large language models (LLMs) have significantly enhanced their utility across various disciplines. However, this pervasive presence of LLMs raises substantial security and trust concerns, particularly at the user-level, within the context of cybersecurity. This paper investigates the potential misuse of LLMs in crafting spam emails that are convincingly legitimate, thus bypassing traditional spam filters. We already conducted an experiment utilizing ChatGPT 3.5 Turbo to alter spam emails, resulting in a 70% success rate of these emails being misclassified as legitimate. We present a pipeline demonstrating the ease with which LLMs can be exploited to undermine email security, highlighting the urgency for improved defensive mechanisms. To counteract these threats, we propose a novel methodology designed to enhance the robustness of spam filters against such sophisticated attacks. This methodology comprises three phases: assessing the vulnerability of current systems to LLM-modified spam, detailed examination of the changes imposed by LLMs, and applying insights gained to fortify existing security infrastructures.