Course Outline
Securing Generative AI: Introduction
3m
Learning objectives
1m 18s
1.1 Understanding the Significance of LLMs in the AI Landscape
7m 6s
1.2 Exploring the Resources for this Course - GitHub Repositories and Others
2m 54s
1.3 Introducing Retrieval Augmented Generation (RAG)
12m 25s
1.4 Understanding the OWASP Top-10 Risks for LLMs
5m 46s
1.5 Exploring the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Framework
5m 39s
Learning objectives
1m 2s
2.1 Defining Prompt Injection Attacks
11m 42s
2.2 Exploring Real-life Prompt Injection Attacks
3m 57s
2.3 Using ChatML for OpenAI API Calls to Indicate to the LLM the Source of Prompt Input
10m 5s
2.4 Enforcing Privilege Control on LLM Access to Backend Systems
6m 10s
2.5 Best Practices Around API Tokens for Plugins, Data Access, and Function-level Permissions
3m 2s
2.6 Understanding Insecure Output Handling Attacks
3m 22s
2.7 Using the OWASP ASVS to Protect Against Insecure Output Handling
4m 43s
Learning objectives
47s
3.1 Understanding Training Data Poisoning Attacks
4m 27s
3.2 Exploring Model Denial of Service Attacks
3m 12s
3.3 Understanding the Risks of the AI and ML Supply Chain
8m 34s
3.4 Best Practices when Using Open-Source Models from Hugging Face and Other Sources
12m 46s
3.5 Securing Amazon BedRock, SageMaker, Microsoft Azure AI Services, and Other Environments
16m 5s
Learning objectives
1m 4s
4.1 Understanding Sensitive Information Disclosure
2m 53s
4.2 Exploiting Insecure Plugin Design
3m 12s
4.3 Avoiding Excessive Agency
3m 46s
Learning objectives
47s
5.1 Understanding Overreliance
5m 17s
5.2 Exploring Model Theft Attacks
4m 57s
5.3 Understanding Red Teaming of AI Models
13m 34s
Learning objectives
1m 6s
6.1 Understanding the RAG, LangChain, Llama Index, and AI Orchestration
17m 25s
6.2 Securing Embedding Models
9m 39s
6.3 Securing Vector Databases
12m 1s
6.4 Monitoring and Incident Response
7m 50s