Recently, many domestic financial companies have been building GenAI (Generative AI, LLM) systems. However, discussions on LLM security vulnerabilities have been limited to the risk of personal information leakage, while various information security considerations defined in the OWASP Top 10 for LLM Applications have not been taken into account. 1. Vulnerability Test This vulnerability maps
Overview of RAG RAG (Retrieval-Augmented Generation) has become an essential component, alongside PEFT, in the development of GenAI (Large Language Model) systems. It is a technology designed to overcome the inherent limitations of standard LLM architectures. RAG enhances a model’s ability to provide accurate and contextually relevant answers by retrieving and referencing external documents during
OWASP Top 10 for LLM Application LLM01. Prompt InjectionMalicious users may manipulate the LLM (GenAI) to redefine system prompts or induce unintended actions through external inputs, leading to data leakage or social engineering attacks. LLM02. Insecure Output HandlingIf the outputs generated by the LLM (GenAI) are not properly validated, vulnerabilities such as XSS, CSRF, or
Specific inquiry details. I work in the information security team at my company and am in charge of security reviews.Our company is planning to build a generative AI (GenAI) system.Since building a GenAI system differs from building a conventional information system, I’m wondering how to approach it.Also, please tell me what risks generative AI poses.
Specific inquiry details. I was introduced to AutoML, which is said to make it easy to build machine learning models even without deep expert knowledge of machine learning, as long as you have a strong understanding of the business domain.Among the AutoML tools, I chose PyCaret because it is considered stable and integrates well with