The paper "Mapping LLM Security Landscapes: A Comprehensive Stakeholder Risk Assessment Proposal" by Rahul Pankajakshan, Sumitra Biswal, Yuvaraj Govindarajulu, and Gilad Gressel addresses the growing concerns surrounding the security of Large Language Models (LLMs). Despite the transformative capabilities of LLMs in various sectors, they also pose significant risks and vulnerabilities. The authors propose a risk assessment process using the OWASP risk rating methodology, which is commonly used for traditional IT systems. This process involves scenario analysis, dependency mapping, and impact analysis to identify and prioritize risks. The study identifies ten critical vulnerabilities specific to LLMs, including prompt injection, insecure output handling, training data poisoning, and model theft. The proposed threat matrix provides a comprehensive evaluation of these risks, enabling stakeholders to make informed decisions for effective mitigation strategies. The paper also includes a use case analysis of a university virtual assistant to demonstrate the practical application of the risk assessment process. The authors conclude that their approach can serve as a foundation for more robust risk assessment practices in the rapidly evolving field of LLM applications, emphasizing the need for continuous refinement and real-world validation.The paper "Mapping LLM Security Landscapes: A Comprehensive Stakeholder Risk Assessment Proposal" by Rahul Pankajakshan, Sumitra Biswal, Yuvaraj Govindarajulu, and Gilad Gressel addresses the growing concerns surrounding the security of Large Language Models (LLMs). Despite the transformative capabilities of LLMs in various sectors, they also pose significant risks and vulnerabilities. The authors propose a risk assessment process using the OWASP risk rating methodology, which is commonly used for traditional IT systems. This process involves scenario analysis, dependency mapping, and impact analysis to identify and prioritize risks. The study identifies ten critical vulnerabilities specific to LLMs, including prompt injection, insecure output handling, training data poisoning, and model theft. The proposed threat matrix provides a comprehensive evaluation of these risks, enabling stakeholders to make informed decisions for effective mitigation strategies. The paper also includes a use case analysis of a university virtual assistant to demonstrate the practical application of the risk assessment process. The authors conclude that their approach can serve as a foundation for more robust risk assessment practices in the rapidly evolving field of LLM applications, emphasizing the need for continuous refinement and real-world validation.