LLMSecurityTester: A Tool for Detection of Vulnerabilities in LLM-based Chatbots
Vladimir Lavrentiev, Dmitry Sergeevich Levshun · 2025
The development of generative algorithms in the last few years, including large language models, imposes increasingly high requirements in the field of data security and protection. Vulnerabilities of information systems related to the generation of inappropriate information are becoming an increasingly serious challenge. They can lead to negative consequences, such as misinformation, creation of fake news or disclosure of sensitive data. This paper presents the architecture of a system designed to identify vulnerabilities in large language models and proposes an approach for vulnerability detection based on prompt engineering. The idea of the approach lies in making certain requests to large language models, the execution of which can help to use the algorithm for illegal purposes. The authors provide a detailed description of the system prototype for automating vulnerability detection, named LLMSecurityTester. Additionally, preliminary results of experiments on various models and datasets are presented, demonstrating the applicability of the solution.