{"id":20044,"date":"2023-09-07T17:49:23","date_gmt":"2023-09-07T12:19:23","guid":{"rendered":"https:\/\/www.cigniti.com\/blog\/?p=20044"},"modified":"2023-12-19T18:37:23","modified_gmt":"2023-12-19T13:07:23","slug":"securing-future-risks-large-language-models-llms","status":"publish","type":"post","link":"https:\/\/www.cigniti.com\/blog\/securing-future-risks-large-language-models-llms\/","title":{"rendered":"LLM Security: Navigating Risks for Resilient Digital Futures"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text css=””]Large language models (LLMs) have recently garnered immense popularity and global attention due to their versatile applications across various industries. The advent of ChatGPT in late 2022, particularly resonating with Gen Z, exemplifies their impressive capabilities.<\/p>\n

Nowadays, the cumbersome process of navigating automated phone menus (pressing 1 or 2) for customer support is becoming less desirable, with chatbots like Siri and Alexa offering a more user-friendly alternative.<\/p>\n

However, like any burgeoning technology, concerns about its security<\/a> implications inevitably arise. This blog provides a comprehensive overview of LLMs (Language Models) and sheds light on the security concerns associated with their utilization.<\/p>\n

This blog discusses the high-level LLM security considerations when implementing LLM models in organizations. We promise to delve deeper into this topic in our upcoming posts.<\/p>\n

Understanding LLMs: Functionality and Security Implications<\/h2>\n

Large Language Models (LLMs), or Language Models, are advanced computer programs in natural language processing that utilize artificial neural networks to generate text imitating human language. These algorithms are trained on extensive text-based data, enabling them to analyze contextual word relationships and create a probability model.<\/p>\n

Given the surrounding context, this empowers the model to predict word likelihoods, a process triggered by a prompt, such as asking a question. Although data becomes static after training, it can be refined through fine-tuning. LLMs display remarkable proficiency in generating a diverse array of compelling content across numerous human and computer languages. However, they do exhibit certain significant flaws:<\/p>\n