Securing Generative AI
ISBN: 0135401801 | .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 3h 31m | 845 MB
Instructor: Omar Santos
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Introduction
Securing Generative AI: Introduction
Lesson 1: Introduction to AI Threats and LLM Security
Learning objectives
1.1 Understanding the Significance of LLMs in the AI Landscape
1.2 Exploring the Resources for this Course - GitHub Repositories and Others
1.3 Introducing Retrieval Augmented Generation (RAG)
1.4 Understanding the OWASP Top-10 Risks for LLMs
1.5 Exploring the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Framework
Lesson 2: Understanding Prompt Injection Insecure Output Handling
Learning objectives
2.1 Defining Prompt Injection Attacks
2.2 Exploring Real-life Prompt Injection Attacks
2.3 Using ChatML for OpenAI API Calls to Indicate to the LLM the Source of Prompt Input
2.4 Enforcing Privilege Control on LLM Access to Backend Systems
2.5 Best Practices Around API Tokens for Plugins, Data Access, and Function-level Permissions
2.6 Understanding Insecure Output Handling Attacks
2.7 Using the OWASP ASVS to Protect Against Insecure Output Handling
Lesson 3: Training Data Poisoning, Model Denial of Service Supply Chain Vulnerabilities
Learning objectives
3.1 Understanding Training Data Poisoning Attacks
3.2 Exploring Model Denial of Service Attacks
3.3 Understanding the Risks of the AI and ML Supply Chain
3.4 Best Practices when Using Open-Source Models from Hugging Face and Other Sources
3.5 Securing Amazon BedRock, SageMaker, Microsoft Azure AI Services, and Other Environments
Lesson 4: Sensitive Information Disclosure, Insecure Plugin Design, and Excessive Agency
Learning objectives
4.1 Understanding Sensitive Information Disclosure
4.2 Exploiting Insecure Plugin Design
4.3 Avoiding Excessive Agency
Lesson 5: Overreliance, Model Theft, and Red Teaming AI Models
Learning objectives
5.1 Understanding Overreliance
5.2 Exploring Model Theft Attacks
5.3 Understanding Red Teaming of AI Models
Lesson 6: Protecting Retrieval Augmented Generation (RAG) Implementations
Learning objectives
6.1 Understanding the RAG, LangChain, Llama Index, and AI Orchestration
6.2 Securing Embedding Models
6.3 Securing Vector Databases
6.4 Monitoring and Incident Response
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