The emergence of large language models has revolutionized natural language processing by enabling the generation of highly human-like text and expanding a wide range of language-related functionalities. Models such as OpenAI’s GPT-4, Google’s Gemini, Meta’s LLaMA, and Microsoft’s Bing AI are becoming increasingly integral across various fields, including healthcare, education, customer support, and software development. But these advancements also raise security issues that must be overcome to enable their responsible deployment. This paper compares most popular large language models, by comparing their use case stability, training data, and their parameter count. It further evaluates these models against six key security benchmarks: data privacy, data poisoning, hallucination, user authentication, prompt injection, and malicious content generation. By identifying critical gaps in these areas this paper outlines future research directions that will minimize the risks associated with the use of LLMs and will improve their security. The ultimate goal is to encourage the responsible use of these technologies so they provide maximum benefits to the society while reducing risks.