In today's interconnected digital landscape, the
proliferation of artificial intelligence (AI) and machine learning
(ML) models is revolutionizing technology from personalized
recommendations to autonomous vehicles. However, with this
advancement comes an inherent vulnerability: the threat of
exploitation and other malicious intent. Two of the most insidious
forms of exploitation are through the training data and
manipulation of the Large Language Models (LLM), practices known as
training data poisoning and prompt injection, respectively.
LLMs are trained on vast amounts of data and are designed to
generate human-like responses. The data used to train these models
are often sourced from the internet, user-generated content, or
other digital platforms and may contain unintended biases from the
human creator(s), inaccuracies, or even malicious content. This
makes them highly susceptible to malicious interaction via training
data or prompt manipulation, which can
