As Large Language Models (LLMs) have become ubiquitous in many facets of society and our everyday lives, they may become tools to enable malicious activity and contribute to societal bias. From biased and toxic responses to outputs that violate societal norms and laws, the vast knowledge an LLM synthesises for us can be poisoned in many ways. As such, ensuring LLMs do not produce biased, toxic or harmful responses is essential, and one way of achieving this is through the red-teaming of LLMs.