Introduction to LLM vulnerabilities [Video]

Introduction to LLM vulnerabilities
This introductory course on vulnerabilities for Large Language Models (LLMs) and language models in general. It provides a deep dive into the practical applications of large language models (LLMs) using Azure's AI services.
Upon completion, learners will be able to:
Explain the concept of model replication or model shadowing as a potential attack vector in large language models, and describe methods to mitigate it through techniques like rate limiting and buffering.
Analyze the potential benefits and limitations of using pre-trained LLMs
Develop strategies for mitigating risks and ethical considerations when deploying LLM-powered applications.
Describe the high-level process of creating a large language model, including data collection, cleaning, and training.
Explain the role of security in large language models and recognize potential security vulnerabilities and attack vectors.
Identify insecure plugin designs in large language model software development kits (SDKs) that could lead to remote execution and implement strategies to secure plugins.
You will learn how to secure your large language model (LLM) applications by addressing potential vulnerabilities. You will explore strategies to mitigate risks from insecure plugin design, including proper input validation and sanitization. Additionally, you will discover techniques to protect against sensitive information disclosure, such as using a redaction service to remove personally identifiable data from prompts and model responses. Finally, you will learn how to actively monitor your application dependencies for security updates and vulnerabilities, ensuring your system remains secure over time.