OWASP GenAI Red Teaming Complete Guide

OWASP GenAI Red Teaming Complete Guide

OWASP GenAI Red Teaming Complete Guide is a practical course designed for cybersecurity professionals, AI security engineers, penetration testers, red teamers, application security professionals, AI developers, security architects, DevSecOps professionals, technology-risk teams and professionals responsible for testing and securing Generative AI, LLM and Agentic AI systems.

The course explores Generative AI Red Teaming, AI Red Teaming, LLM Security, GenAI Security, OWASP GenAI Security, NIST AI Risk, MITRE ATLAS, threat modeling, STRIDE, Prompt Injection, Jailbreaks, Adversarial Prompt Engineering, RAG Security, Vector Store Security, Model Testing, Data Poisoning, Model Extraction, Hallucinations, Bias, Toxicity, Prompt Firewalls, Guardrail Bypass, Sandbox Escape, API Injection, Agentic AI Security, Multi-Agent Attacks, AI Supply Chain Security and AI Security Governance.

Rather than treating Generative AI security as only a model-safety problem, this course examines the complete attack surface across models, prompts, RAG pipelines, vector stores, system prompts, access controls, APIs, generated code, sandboxes, dependencies, logging, runtime behavior, AI agents and organizational processes.

Learners explore how to design and execute structured GenAI red-team engagements, identify attack surfaces, model threats, develop adversarial scenarios, evaluate model behavior, test guardrails, examine RAG systems, investigate supply-chain weaknesses and translate technical findings into measurable security risks.

The course also examines the human and organizational dimensions of AI security, including Human-AI trust manipulation, over-reliance, social engineering through generated output, chain-of-custody weaknesses, traceability failures, cross-functional red-team structures, roles and responsibilities, and domain-specific red-team planning.

A practical operating model is:

AI System → Threat Model → Attack Surface → Adversarial Test → Evidence → Risk Assessment → Mitigation → Retest

The objective is not simply to collect jailbreak prompts.

It is to develop a repeatable methodology for performing authorized, controlled and evidence-based security evaluation of Generative AI systems.

Generative AI & LLM Security Foundations

Generative AI systems may include:

• Large Language Models

• Multimodal models

• Retrieval-Augmented Generation systems

• AI agents

• Generative applications

• Enterprise AI assistants

These systems introduce security risks beyond traditional application vulnerabilities.

A useful security model is:

Model + Data + Prompt + Application + Tools + Users + Infrastructure

A weakness in any of these areas may affect the security of the complete AI system.

What Is GenAI Red Teaming?

Generative AI red teaming is the structured process of testing AI systems from an adversarial perspective.

The goal is to identify weaknesses before they become real incidents.

A useful workflow is:

Scope → Threat Model → Test Cases → Adversarial Evaluation → Evidence → Risk → Mitigation → Retest

GenAI red teaming may evaluate:

• Security

• Safety

• Reliability

• Abuse resistance

• Data protection

• System integrity

• Human interaction

The emphasis is on controlled security testing within authorized environments.

Traditional Security Testing vs GenAI Red Teaming

Traditional application security often examines:

• Authentication

• Authorization

• Input validation

• APIs

• Infrastructure

• Software vulnerabilities

Generative AI systems introduce additional questions:

Can the model be manipulated?

Can the prompt hierarchy be bypassed?

Can retrieval content influence system behavior?

Can the model expose sensitive information?

Can an AI agent misuse tools?

Can generated output manipulate a human user?

GenAI red teaming therefore expands traditional security testing rather than replacing it.

OWASP GenAI Security

OWASP provides useful frameworks and taxonomies for analyzing Generative AI risks.

An OWASP-oriented approach helps security teams identify weaknesses across:

• LLM behavior

• Prompt handling

• Data

• Retrieval systems

• Applications

• Agents

• External integrations

A useful principle is:

AI Security Begins with Understanding the Complete System, Not Only the Model.

NIST AI Risk Categories

NIST-oriented AI risk thinking can help organizations place technical AI vulnerabilities within a broader risk-management context.

A strong assessment considers:

Threat → Vulnerability → Impact → Risk → Control → Monitoring

This helps translate technical red-team findings into information useful for:

• Security teams

• Engineering

• Risk management

• Governance

• Leadership

Threat Modeling for AI Systems

Threat modeling helps teams understand how an AI system could be attacked before adversarial testing begins.

A practical workflow is:

Architecture → Assets → Trust Boundaries → Attack Surface → Threats → Controls

The course explores threat-modeling approaches including STRIDE and AI-focused threat perspectives.

Threat modeling prevents red teaming from becoming random prompt experimentation.

GenAI Attack Surfaces

Modern AI systems can contain multiple attack surfaces:

• LLMs

• Prompts

• System instructions

• RAG pipelines

• Vector stores

• APIs

• AI agents

• Multimodal inputs

• Generated code

• External tools

• Users

• Infrastructure

A red team should understand how these components interact.

A weakness may originate in one component and create impact elsewhere.

RAG Triad & AI Risk Mapping

RAG-based systems introduce security concerns around:

• Retrieval

• Knowledge sources

• Generated responses

Security testing should examine whether:

• Retrieved content is trustworthy

• Unauthorized information can be accessed

• Malicious documents can influence responses

• The generated answer remains grounded

A useful model is:

Query → Retrieval → Context → Generation → Validation

The GenAI Red Teaming Lifecycle

A professional GenAI red-team engagement should begin with scope.

Important questions include:

What system is being tested?

What behaviors are in scope?

Which data is allowed?

Which integrations can be tested?

What regulatory or organizational constraints apply?

What constitutes a successful finding?

Clear scope helps keep testing safe, repeatable and useful.

GenAI Red Team Blueprint

A structured blueprint can include:

Scope → Architecture Review → Threat Modeling → Test Design → Execution → Evidence → Severity → Mitigation → Retest

This creates consistency across red-team exercises.

It also makes findings easier to reproduce.

Red Team Metrics & Reporting

A red-team engagement should produce more than a list of successful prompts.

Useful outputs may include:

• Finding

• Attack scenario

• Affected component

• Evidence

• Potential impact

• Reproducibility

• Severity

• Recommended mitigation

• Retest status

A strong finding connects:

Technical Weakness → Realistic Impact → Actionable Control

Prompt Injection

Prompt injection attempts to manipulate the behavior of an AI system through crafted input.

A conceptual attack path is:

Malicious Input → Instruction Conflict → Model Manipulation → Undesired Behavior

Prompt injection becomes especially important when an AI system can access:

• Sensitive data

• Tools

• External systems

• Other agents

Red-team testing should evaluate both obvious and indirect injection paths.

Jailbreak Testing

Jailbreak techniques attempt to bypass intended model restrictions.

Testing may examine:

• Instruction conflicts

• Context manipulation

• Role manipulation

• Multi-turn strategies

• Encoded or transformed requests

The objective of authorized jailbreak testing is to understand whether system controls withstand adversarial interaction.

Adversarial Prompt Engineering

Red teams use structured adversarial prompts to explore how an AI system behaves under unexpected or malicious input.

Useful testing considers:

Attack Goal → Prompt Strategy → Model Response → Evidence → Control Failure

This turns prompt experimentation into systematic testing.

Multi-Turn Attacks

Some vulnerabilities may not appear in a single request.

Attackers may gradually manipulate an AI system across multiple interactions.

A conceptual pattern is:

Benign Context → Trust Building → Instruction Shift → Adversarial Objective

Red teams should therefore test both single-turn and multi-turn behavior.

Reasoning-Chain Attacks

Complex AI interactions may involve multi-step reasoning.

Security testing should examine whether adversarial input can influence later stages of a reasoning or decision workflow.

This becomes especially important in Agentic AI environments where reasoning may lead directly to actions.

Hallucination Testing

Generative AI may produce information that appears plausible but is unsupported or incorrect.

Red-team evaluations may examine:

• Factual accuracy

• Unsupported claims

• Fabricated information

• Grounding failures

A fundamental security principle is:

Confident AI Output ≠ Verified Information

Bias & Toxicity Testing

GenAI evaluations may examine whether outputs produce inappropriate or harmful patterns across different contexts.

Testing should be:

• Systematic

• Documented

• Reproducible

• Relevant to the intended use case

This allows organizations to understand where additional safeguards may be required.

Data Poisoning

Data poisoning attempts to influence an AI system by manipulating information it learns from or retrieves.

A conceptual attack path is:

Malicious Data → AI Knowledge/Context → Altered Output → Security Impact

Red teams may examine whether AI data pipelines contain sufficient controls to protect integrity.

Model Extraction

Model extraction concerns attempts to reproduce valuable behavior or information from an AI system through repeated interaction.

Potential risks include:

• Intellectual property loss

• Unauthorized replication

• Security-control discovery

Testing helps organizations understand how exposed their AI capabilities may be.

Alignment & Model Behavior Testing

Security evaluation should examine whether a model remains within intended behavioral boundaries when exposed to adversarial input.

The question becomes:

Can an attacker push the system outside its intended operating constraints?

Factuality & Grounding

For systems expected to provide factual information, red teams should evaluate:

• Whether claims are grounded

• Whether retrieved evidence supports answers

• Whether missing information is acknowledged

• Whether conflicting information is handled correctly

Grounding becomes particularly important in RAG applications.

Response Coherence

AI output should also remain logically coherent under adversarial conditions.

Security testing may examine whether manipulated context causes:

• Contradictory outputs

• Broken reasoning

• Inconsistent policy application

• Unreliable responses

Content Filter Testing

AI systems may use content filters to limit unsafe output.

Red teams can evaluate whether:

• Filters trigger consistently

• Alternative phrasing bypasses controls

• Multi-turn interactions weaken enforcement

• System behavior changes across contexts

The objective is to identify where safeguards require strengthening.

Prompt Firewall Testing

Prompt firewalls and similar controls attempt to identify or block malicious instructions.

Testing may examine:

Adversarial Input → Detection → Policy Decision → Model Interaction → Response

A successful security control should reduce malicious influence without unnecessarily breaking legitimate workflows.

RAG Security

Retrieval-Augmented Generation creates additional attack surfaces.

Potential concerns include:

• Malicious documents

• Poisoned knowledge bases

• Unauthorized retrieval

• Cross-user information leakage

• Manipulated vector-store content

A useful security model is:

Data Source → Ingestion → Vector Store → Retrieval → Model → User

Every stage deserves security review.

Vector Store Manipulation

Vector databases can become security-sensitive because AI systems rely on retrieved information.

An attacker who can influence indexed content may influence downstream model behavior.

Red teams should examine:

• Data provenance

• Ingestion controls

• Retrieval boundaries

• Authorization

• Content integrity

Role-Based Access Control — RBAC

AI applications may require different levels of access for:

• Users

• Administrators

• AI agents

• Applications

• Services

RBAC helps ensure that access aligns with legitimate responsibilities.

A useful principle is:

AI Capability Should Not Exceed User Authorization

System Prompt Testing

System prompts may define:

• Agent behavior

• Safety restrictions

• Output rules

• Tool boundaries

• Confidential instructions

Red-team testing should evaluate whether untrusted users can override or expose these instructions.

Caching Security

Caching can introduce unexpected security concerns when responses or context are reused incorrectly.

Testing should examine whether cached information can:

• Cross user boundaries

• Expose sensitive information

• Preserve malicious context

Caching should be considered part of the AI application's security architecture.

Code Generation Exploits

Generative AI systems that create code introduce software-security concerns.

Red-team testing may examine whether generated code contains:

• Unsafe patterns

• Insecure assumptions

• Unexpected functionality

Generated code should be reviewed and tested before execution.

Sandbox Escape

Generated code may be executed inside isolated environments.

A sandbox reduces risk only if isolation is effective.

Red teams may test whether code can escape expected boundaries or interact with unauthorized resources.

A strong model is:

Generated Code → Isolated Execution → Monitoring → Controlled Result

API Injection

AI applications often interact with APIs.

Testing should examine whether malicious input can influence:

• API parameters

• Requests

• Authorization

• Downstream systems

This becomes particularly important for AI agents capable of invoking APIs automatically.

Template Attacks

Templates used to construct prompts or application messages can become another attack surface.

Security teams should evaluate whether untrusted content can alter the intended structure of an AI request.

Dependency & Supply Chain Security

AI applications rely on:

• Libraries

• Models

• APIs

• Plugins

• Frameworks

• External services

A useful security principle is:

AI Application Security Includes Supply Chain Security

A trusted AI model running inside a compromised dependency chain is not a secure system.

Monitoring Evasion

Attackers may attempt to perform malicious activity without triggering security monitoring.

Red-team evaluations should consider whether:

• Important events are logged

• Alerts can be bypassed

• Security telemetry is complete

• Suspicious patterns remain visible

Logging Weaknesses

Logs provide evidence for:

• Investigation

• Incident response

• Detection

• Auditing

Security testing should evaluate whether important AI activity is captured accurately.

System-Wide Data Integrity

AI systems often depend on multiple connected data sources.

Red teams should evaluate whether malicious or incorrect information can propagate across:

Data → Retrieval → Model → Application → Decision

Integrity failures can affect the complete AI workflow.

Runtime Evaluation

Some security problems become visible only when the AI system is operating.

Runtime evaluation may examine:

• Model behavior

• User interaction

• Tool use

• Agent activity

• Unexpected workflows

• Human trust

Continuous evaluation becomes increasingly important as AI systems become more autonomous.

Human-AI Trust Manipulation

A Generative AI system can influence human decision-making through authoritative or persuasive output.

Red teams should examine whether:

• Users may over-trust generated content

• Important uncertainty is hidden

• Generated recommendations create inappropriate confidence

Human behavior is part of the AI security boundary.

AI Over-Reliance

Organizations should avoid treating AI output as automatically correct.

A secure operating model is:

AI Output → Evidence Check → Human Review → Action

The higher the impact of a decision, the stronger the validation should be.

Social Engineering Through Generative AI

Generative AI may create persuasive content at scale.

Red-team evaluations can examine how generated output might contribute to social-engineering risk within an organization's defensive context.

Organizations should combine technical controls with user awareness.

Agentic AI Security

AI agents can move from generating information to taking actions.

Potential capabilities include:

• Planning

• Tool use

• Memory

• Delegation

• Multi-step workflows

This changes the risk model.

Incorrect Output may become Incorrect Action.

Agentic AI red teaming should therefore test both reasoning and execution.

Multi-Agent Attack Chains

Multi-agent systems introduce security challenges involving:

• Agent communication

• Delegation

• Shared context

• Trust relationships

• Consensus

• Tool access

A compromised agent may influence downstream agents.

Red-team testing should examine how attacks propagate through the agent network.

Decision Hijacking

If an attacker can manipulate the information used by an AI agent, the attacker may influence the agent's decisions.

A conceptual chain is:

Malicious Input → Manipulated Context → Agent Decision → Tool Action

This demonstrates why Agentic AI security must extend beyond model output.

Chain-of-Custody & Traceability

Security teams need to understand:

• Where information originated

• How it changed

• Which agent processed it

• Which decision was made

• Which action followed

Traceability becomes particularly important in autonomous workflows.

A useful model is:

Input → Transformation → Decision → Action → Evidence

Open-Source GenAI Red Teaming Tools

Open-source security tools can help automate portions of AI model and application testing.

Tools may support:

• Prompt testing

• Model evaluation

• Adversarial scenario execution

• Result collection

• Regression testing

Automation can improve repeatability.

Human security analysis remains necessary for interpreting impact.

Automating Adversarial Scenarios

Red teams can automate repeatable test cases to evaluate:

• New models

• New prompts

• New guardrails

• Application changes

• Regression

A useful workflow is:

Test Case → Automated Execution → Evidence → Comparison → Alert

Logging, Monitoring & Alerting Integration

Red-team findings become more valuable when they improve defensive monitoring.

Security teams should ask:

Would this attack be detected in production?

This connects:

Red Teaming → Detection Engineering → Monitoring → Response

Red Team Playbooks

Playbooks help make GenAI red teaming repeatable.

A useful playbook structure is:

Objective → Scope → Preconditions → Test Procedure → Expected Control → Evidence → Risk → Mitigation → Retest

This supports consistent evaluation across systems and teams.

Building a Cross-Functional GenAI Red Team

Effective Generative AI red teaming may require collaboration across:

• Cybersecurity

• AI/ML engineering

• Application security

• Data teams

• Risk

• Governance

• Product teams

• Legal or compliance functions

AI security is rarely owned by one discipline alone.

Roles, Responsibilities & RACI

A clear RACI structure can establish who is:

Responsible

Accountable

Consulted

Informed

for important red-team activities.

Clear accountability reduces security gaps.

Regional & Domain-Specific Red Teaming

AI risks may differ according to:

• Industry

• Business process

• Data sensitivity

• Regulation

• Geography

• User population

Red-team programs should therefore reflect the environment where the AI system will actually operate.

Designing a GenAI Red Team Engagement

A strong engagement begins before adversarial testing starts.

A useful planning model is:

Objective → Scope → Architecture → Risk Context → Threat Model → Test Plan → Rules of Engagement

This keeps testing focused and authorized.

Running a GenAI Red Team Engagement

A practical execution model is:

Test → Observe → Capture Evidence → Assess Impact → Report → Mitigate → Retest

The goal is not simply to prove that a model can fail.

The goal is to produce findings that help make the system more secure.

Who Should Take This Course?

This course is suitable for:

• AI Red Teamers

• Cybersecurity Professionals

• AI Security Engineers

• Penetration Testers

• Application Security Professionals

• Security Architects

• AI/ML Engineers

• GenAI Developers

• Agentic AI Developers

• DevSecOps Professionals

• Technology Risk Professionals

• AI Governance Professionals

• Security Researchers

• Professionals responsible for LLM and Generative AI security

Whether you work in GenAI Security, LLM Security, AI Red Teaming, application security, Agentic AI Security, RAG Security, threat modeling or AI governance, this course provides a practical foundation for systematically evaluating modern Generative AI systems.