  Large Language Models (LLMs) have become a cornerstone in the field of
Natural Language Processing (NLP), offering transformative capabilities in
understanding and generating human-like text. However, with their rising
prominence, the security and vulnerability aspects of these models have
garnered significant attention. This paper presents a comprehensive survey of
the various forms of attacks targeting LLMs, discussing the nature and
mechanisms of these attacks, their potential impacts, and current defense
strategies. We delve into topics such as adversarial attacks that aim to
manipulate model outputs, data poisoning that affects model training, and
privacy concerns related to training data exploitation. The paper also explores
the effectiveness of different attack methodologies, the resilience of LLMs
against these attacks, and the implications for model integrity and user trust.
By examining the latest research, we provide insights into the current
landscape of LLM vulnerabilities and defense mechanisms. Our objective is to
offer a nuanced understanding of LLM attacks, foster awareness within the AI
community, and inspire robust solutions to mitigate these risks in future
developments.
