This paper proposes a comprehensive risk assessment framework for Large Language Models (LLMs) based on the OWASP Risk Rating Methodology, designed to help stakeholders evaluate and mitigate risks associated with LLM deployment. The framework involves scenario analysis, dependency mapping, and impact analysis to estimate the likelihood of cyberattacks and assess their potential impact. The process is applied to three key stakeholder groups: developers fine-tuning LLMs, application developers using third-party LLM APIs, and end users. The resulting threat matrix provides a holistic view of LLM-related risks, enabling stakeholders to make informed decisions for effective mitigation strategies. LLMs introduce a range of security risks, including prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft. These risks are mapped against the three stakeholder groups, with specific mitigation strategies proposed for each. The paper also presents a hypothetical use case of a university virtual assistant, demonstrating the application of the risk assessment process. The analysis reveals that prompt injection poses a high risk, while training data poisoning has a medium risk. The threat matrix is presented as a reference tool for risk assessment, enabling stakeholders to prioritize mitigation efforts based on the calculated risk ratings. The study highlights the importance of a systematic approach to risk assessment in LLM systems, emphasizing the need for continuous refinement of the threat matrix to address emerging risks and evolving attack strategies. The proposed framework serves as a valuable tool for security practitioners, developers, and organizations to enhance the security and reliability of LLM-based systems. The paper also discusses related work in the field of LLM security, emphasizing the need for further research and development in this area. Overall, the study contributes to the growing body of knowledge on LLM security, providing a practical framework for risk assessment and mitigation in the rapidly evolving landscape of large language models.This paper proposes a comprehensive risk assessment framework for Large Language Models (LLMs) based on the OWASP Risk Rating Methodology, designed to help stakeholders evaluate and mitigate risks associated with LLM deployment. The framework involves scenario analysis, dependency mapping, and impact analysis to estimate the likelihood of cyberattacks and assess their potential impact. The process is applied to three key stakeholder groups: developers fine-tuning LLMs, application developers using third-party LLM APIs, and end users. The resulting threat matrix provides a holistic view of LLM-related risks, enabling stakeholders to make informed decisions for effective mitigation strategies. LLMs introduce a range of security risks, including prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft. These risks are mapped against the three stakeholder groups, with specific mitigation strategies proposed for each. The paper also presents a hypothetical use case of a university virtual assistant, demonstrating the application of the risk assessment process. The analysis reveals that prompt injection poses a high risk, while training data poisoning has a medium risk. The threat matrix is presented as a reference tool for risk assessment, enabling stakeholders to prioritize mitigation efforts based on the calculated risk ratings. The study highlights the importance of a systematic approach to risk assessment in LLM systems, emphasizing the need for continuous refinement of the threat matrix to address emerging risks and evolving attack strategies. The proposed framework serves as a valuable tool for security practitioners, developers, and organizations to enhance the security and reliability of LLM-based systems. The paper also discusses related work in the field of LLM security, emphasizing the need for further research and development in this area. Overall, the study contributes to the growing body of knowledge on LLM security, providing a practical framework for risk assessment and mitigation in the rapidly evolving landscape of large language models.