Owasp Genai Red Teaming Complete Guide

Red Teaming RAG, APIs, and Multimodal Architectures

What you'll learn

Understand the full GenAI threat landscape across security, safety, and trust domains

Differentiate traditional red teaming from generative AI-specific red teaming approaches

Apply OWASP, NIST, and MITRE frameworks for AI threat modeling and risk categorization

Identify and exploit key GenAI attack surfaces (LLMs, agents, RAG pipelines, APIs)

Craft prompt injection, jailbreaks, and adversarial multi-turn exploits

Evaluate model responses for hallucinations, bias, toxicity, and alignment bypasses

Test implementation-level controls including content filters, RBAC, and vector store poisoning

Analyze runtime and agentic risks such as decision hijacking and over-reliance

Use tools like PyRIT and PromptBench to simulate real-world adversarial scenarios

Track and report red team metrics, scenario brittleness, and mitigation effectiveness

Design a cross-functional GenAI red team with defined roles, RACI matrices, and governance

Customize red teaming strategies for regional laws, cultural sensitivities, and industry sectors

Create and execute red team playbooks for scalable, automated evaluation pipelines

Close the loop: document, remediate, and communicate risks to stakeholders

Requirements

Some exposure to OWASP or NIST frameworks

Description

This comprehensive course on OWASP GenAI Red Teaming Complete Guide equips learners with practical and strategic expertise to test and secure generative AI systems. The curriculum begins with foundational concepts, introducing learners to the generative AI ecosystem, large language models (LLMs), and the importance of red teaming to uncover security, safety, and trust failures. It contrasts GenAI red teaming with traditional methods, highlighting how risks evolve across model architectures, human interfaces, and real-world deployments. Through in-depth risk taxonomy, students explore OWASP and NIST risk categories, STRIDE modeling, MITRE ATLAS tactics, and socio-technical frameworks like the RAG Triad. Key attack surfaces across LLMs, agents, and multi-modal inputs are mapped to emerging threat vectors. The course then presents a structured red teaming blueprint—guiding learners through scoping engagements, evaluation lifecycles, and defining metrics for success and brittleness. Advanced modules dive into prompt injection, jailbreaks, adversarial prompt design, multi-turn exploits, and bias evaluation techniques. Students also assess model vulnerabilities such as hallucinations, cultural insensitivity, and alignment bypasses. Implementation-level risks are analyzed through tests on content filters, prompt firewalls, RAG vector manipulation, and access control abuse. System-level modules examine sandbox escapes, API attacks, logging gaps, and supply chain integrity. Learners are also introduced to runtime and agentic risks like overtrust, social engineering, multi-agent manipulation, and traceability breakdowns. Practical tooling sessions feature hands-on red teaming with PyRIT, PromptBench, automation workflows, and playbook design. Finally, the course addresses operational maturity—showing how to build cross-functional red teams, align roles with RACI matrices, and apply red teaming within regulatory and cultural boundaries. With case-driven instruction and security-by-design thinking, this course prepares learners to operationalize GenAI red teaming at both the technical and governance levels.

Overview

Section 1: Foundations of GenAI Red Teaming

Lecture 1 Introduction to GenAI and LLM Ecosystems

Lecture 2 What is GenAI Red Teaming and Why It Matters

Lecture 3 Key Risks in Generative AI Systems

Lecture 4 Differences Between Traditional and GenAI Red Teaming

Section 2: Risk Taxonomy and Threat Modeling

Lecture 5 OWASP & NIST Risk Categories (Security, Safety, Trust)

Lecture 6 Threat Modeling for AI Systems (STRIDE, MITRE ATLAS, NIST AI RMF)

Lecture 7 Attack Surfaces: LLMs, Agents, Multi-modal Inputs

Lecture 8 Risk Mapping with RAG Triad and Socio-technical Layers

Section 3: The GenAI Red Teaming Process

Lecture 9 Lifecycle and Blueprint Overview

Lecture 10 Scoping the Engagement (Use Cases, Regulatory Priorities)

Lecture 11 Four-Phase Evaluation Model (Model, Implementation, System, Runtime)

Lecture 12 Red Teaming Metrics, Reporting, and Risk Dispositioning

Section 4: Adversarial Techniques and Prompt Attacks

Lecture 13 Prompt Injection and Jailbreak Techniques

Lecture 14 Adversarial Prompt Engineering & Dataset Design

Lecture 15 Multi-Turn Attacks and CoT Reasoning Chains

Lecture 16 Evaluation Criteria for Prompt Success and Brittleness

Section 5: Model Evaluation and Exploitation

Lecture 17 Testing for Hallucination, Bias, Toxicity

Lecture 18 Data Poisoning, Model Extraction, Alignment Bypass

Lecture 19 Socio-Technical Harm & Cultural Sensitivity Testing

Lecture 20 Factuality, Grounding, and Response Coherence Tests

Section 6: Implementation and Guardrail Bypass

Lecture 21 Testing Content Filters and Prompt Firewalls

Lecture 22 RAG Security and Vector Store Manipulation

Lecture 23 Role-based Access Control (RBAC), Token Abuse

Lecture 24 Testing System Prompts, Caching, and Instruction Retention

Section 7: System and Supply Chain Testing

Lecture 25 Code Generation Exploits and Sandbox Escape

Lecture 26 API Injection, Template Attacks, Dependency Risks

Lecture 27 Monitoring Evasion and Logging Weaknesses

Lecture 28 Testing for System-wide Data Integrity and Downtime

Section 8: Runtime Evaluation and Agentic AI Risks

Lecture 29 Human-AI Trust Manipulation and Over-reliance

Lecture 30 Social Engineering via Generative Output

Lecture 31 Multi-Agent Attack Chains and Decision Hijacking

Lecture 32 Chain-of-Custody and Traceability Failures

Section 9: Tools, Automation and Playbooks

Lecture 33 Open-Source Tools for Model Testing (e.g., PyRIT, PromptBench)

Lecture 34 Automation of Adversarial Scenarios and Static Datasets

Lecture 35 Logging, Monitoring, and Alerting Integrations

Lecture 36 Sample Red Team Playbooks and Walkthroughs

Section 10: Organizational Maturity and Governance

Lecture 37 Building a Cross-Functional Red Team

Lecture 38 Roles, Responsibilities, and RACI Matrix for AI Security

Lecture 39 Regional and Domain-Specific Red Teaming Considerations

Lecture 40 Designing and Running Your GenAI Red Team Program

AI Security Engineers looking to build red teaming capabilities for LLM systems,Cybersecurity Analysts and SOC teams responsible for detecting GenAI misuse,Red Team Professionals seeking to expand into AI-specific adversarial simulation,Risk, Compliance, and Governance Leads aiming to align GenAI systems with NIST, OWASP, or EU AI Act standards,Product Owners and Engineering Managers deploying GenAI copilots or RAG-based assistants,AI Researchers and Data Scientists focused on model safety, bias mitigation, and interpretability,Ethics, Policy, and Trust & Safety teams developing responsible AI frameworks and testing protocols,Advanced learners and cybersecurity students wanting hands-on exposure to adversarial GenAI evaluation,Organizations adopting LLMs in regulated domains such as finance, healthcare, legal, and government