Unknown Vulnerability Mining for Power Monitoring Systems Aided by Large Language Modeling

An exciting new direction for improving operational efficiency and decision-making is the use of large language models (LLMs) to contemporary power systems. Nevertheless, there may be unanticipated security risks associated with this move. Using LLMs to power networks may pose certain risks, which this paper examines. It stresses the need of doing research and developing remedies immediately. It is a challenging but vital job to secure large language models in a power monitoring context. Through the implementation of thorough security measures, the promotion of a security-conscious culture, and the continuous monitoring of new threats while technologies, we may maximize the benefits of LLMs while minimizing their hazards. It is our duty as information security experts to pioneer this new field and make sure that our security protocols adapt to the increasing sophistication of our AI systems. Security flaws in LLM that allow rapid injection attacks are among the most critical ones. These types of attacks take advantage of LLMs' fundamental features by deliberately feeding them data that will cause them to operate in an unexpected way or leak private information. Industries that deal with sensitive data are especially worried about the consequences of these vulnerabilities. Creating a comprehensive strategy for LLM security is essential for reducing these threats. The first step is to establish reliable procedures for training and selecting models. Many large language models (LLMs) have seen extensive usage with the introduction of commercially accessible systems like ChatGPT. This has piqued interest in semantic search, which is able to do searches that consider the meaning of words. Here, we built an LLM model using TinyLlama Chat 1.1. The LLM takes the processed packet data and the extracted context as input and outputs a user-friendly summary of the packet file. Through the use of machine learning models, the program provides a concise, well-organized, and straightforward overview of the network's operations.