Introduction to LLM Vulnerabilities

Pragmatic AI Labs via edX

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Overview

As large language models (LLMs) revolutionize the AI landscape, it is crucial to understand and address the unique security challenges they present. This comprehensive course is designed to equip you with the knowledge and skills needed to identify, mitigate, and prevent vulnerabilities in your LLM applications. Through a series of in-depth lessons, you will: Explore common security threats, such as model theft, prompt injection, and sensitive information disclosure Learn techniques to prevent attackers from exploiting vulnerabilities and compromising your AI...

Syllabus

Identifying LLM security vulnerabilities and attack vectors
Mitigating model replication and shadowing attacksRecognizing insecure output handling and prompt injection
Preventing model theft and excessive agency issues
Implementing strategies for secure plugin design
Redacting sensitive information using APIs and regex
Monitoring and updating dependencies for security
Analyzing generative AI application types and architectures
Understanding multi-model applications and specialized models
Comparing API-based, embedded, and multi-model applications