  The advent of Large Language Models (LLMs) has garnered significant
popularity and wielded immense power across various domains within Natural
Language Processing (NLP). While their capabilities are undeniably impressive,
it is crucial to identify and scrutinize their vulnerabilities especially when
those vulnerabilities can have costly consequences. One such LLM, trained to
provide a concise summarization from medical documents could unequivocally leak
personal patient data when prompted surreptitiously. This is just one of many
unfortunate examples that have been unveiled and further research is necessary
to comprehend the underlying reasons behind such vulnerabilities. In this
study, we delve into multiple sections of vulnerabilities which are
model-based, training-time, inference-time vulnerabilities, and discuss
mitigation strategies including "Model Editing" which aims at modifying LLMs
behavior, and "Chroma Teaming" which incorporates synergy of multiple teaming
strategies to enhance LLMs' resilience. This paper will synthesize the findings
from each vulnerability section and propose new directions of research and
development. By understanding the focal points of current vulnerabilities, we
can better anticipate and mitigate future risks, paving the road for more
robust and secure LLMs.
