AI Red Teaming: 8-Step Process and Key Techniques

What Is AI Red Teaming? 

AI red teaming is a structured, adversarial testing process designed to uncover vulnerabilities in AI systems. By simulating real-world threats and attacks, red teams identify flaws, security risks, and unexpected behaviors before malicious actors do, making it a critical practice for robust AI security and compliance.

Unlike traditional security testing, which focuses on infrastructure like firewalls and servers, AI red teaming focuses on the unpredictable, non-deterministic behaviors of the AI model itself. It tests how the system reacts when manipulated or exposed to adversarial inputs.

The main focus areas include:

• Jailbreaking: Finding ways to bypass AI safety guardrails to elicit prohibited or harmful outputs.
• Prompt injection: Manipulating system inputs to force the AI to execute unintended commands or ignore its rules.
• Data exfiltration: Tests whether attackers can extract sensitive data, system prompts, or proprietary information from AI systems.
• Model evasion: Evaluates whether malicious inputs can bypass AI detection or safety mechanisms.
• Training data extraction: Corrupting training data or finding ways to steal proprietary models and extract sensitive user information.
• Hallucination testing: Assesses whether the model generates false, misleading, or unsupported information.
• Toxicity, bias, and harmful output testing: Identifies offensive, discriminatory, or unsafe responses across different scenarios.
• Tool abuse and unauthorized action testing: Verifies that AI agents cannot misuse connected tools, APIs, or permissions.
• Model supply chain and dependency testing: Evaluates risks introduced by third-party models, datasets, libraries, and plugins.

This is part of a series of article about AI cyber security

Why AI Red Teaming Is Important

AI Systems Create New Attack Surfaces

AI systems introduce attack surfaces that did not exist in traditional software architectures. Unlike static code, AI models learn from data and adapt to new information, which means their behavior can be less predictable and more susceptible to manipulation. Attackers can exploit these surfaces by crafting adversarial examples, targeting data pipelines, or abusing model APIs in ways that bypass conventional security controls.

These attack surfaces often span multiple layers of the tech stack, including the model, data ingestion, orchestration components, and integrated tools or plugins. This complexity increases the likelihood of hidden vulnerabilities. By probing these layers through red teaming, organizations can uncover weaknesses that might otherwise go undetected and reduce the risk of real-world misuse.

Related content: Explore techniques and examples in our guide to AI-driven cyber security.

LLMs Can Be Manipulated Through Natural Language

Large language models (LLMs) are vulnerable to manipulation through natural language prompts. Attackers can craft inputs, often called prompt injections, that trick the model into revealing restricted information, generating harmful content, or performing unintended actions. Because LLMs interpret and respond to human language, it is difficult to anticipate every form of abuse.

Even well-designed guardrails can be bypassed with creative or obfuscated prompts. This creates a persistent challenge: defenders must test for new attack patterns while attackers develop new techniques. Red teaming simulates these adversarial interactions and evaluates the effectiveness of safeguards under realistic conditions.

AI Applications May Expose Sensitive Data

AI-powered applications often handle sensitive or proprietary data, increasing the risk of disclosure. If data is not sanitized or access controls are weak, attackers may extract private information by interacting with the AI model. This risk is heightened in generative AI systems, which may output training data or user inputs as part of their responses.

Red teaming uncovers these exposures by simulating data exfiltration attempts and testing the boundaries of the model’s recall capabilities. Through targeted queries and adversarial scenarios, teams can identify where sensitive data leaks occur and recommend stronger privacy controls.

Agentic AI Expands Risk Through Tool Use and Autonomy

Agentic AI systems that autonomously use tools, access APIs, or interact with external resources expand the impact of an attack. These agents can be manipulated to perform unauthorized actions, such as sending emails, making transactions, or altering data. Increased autonomy and integration with other systems mean that a successful attack can have broad consequences.

Red teaming for agentic AI simulates scenarios where agents are tricked into abusing privileges or making unsafe decisions. By probing weaknesses in tool usage, permission management, and oversight mechanisms, organizations can identify risks and implement guardrails that restrict agent behavior to intended operations.

The AI Red Teaming Process 

1. Define Scope and Objectives

The first step is to define the scope and objectives of the engagement. This includes determining which AI systems, models, or applications will be tested, what types of attacks will be simulated, and what the goals of the assessment are. Scope definition should consider: 

• Business priorities
• Compliance requirements
• Potential impact to users or stakeholders

Clear objectives ensure the effort is focused and measurable. Objectives might include identifying data leakage vectors, testing guardrail resilience, or evaluating responses to adversarial prompts. A defined scope helps allocate resources efficiently and avoid unnecessary risks during testing.

2. Map the AI System Architecture

Mapping the architecture clarifies the attack surface and dependencies of the AI system. This involves documenting: 

• Data flows
• Model interactions
• API integrations
• External tools or plugins

A detailed architectural map highlights where vulnerabilities may exist within AI components and their connections. Architecture mapping also clarifies internal and external interfaces, which supports realistic attack simulation. Visualizing how data and commands move through the environment helps red teams target critical and exposed elements.

3. Identify Threat Models and Abuse Cases

Threat modeling analyzes how adversaries might attack the system, their goals, and their capabilities. Threat actors may include: 

• Insiders
• External hackers
• Automated bots exploiting AI weaknesses

Red teams also define abuse cases, specific scenarios where the AI system could be misused or produce harmful outcomes. These might include prompt injection, data exfiltration, or model evasion. Prioritizing relevant abuse cases ensures testing focuses on the most impactful risks.

4. Design Adversarial Test Scenarios

With threat models defined, teams design adversarial scenarios that mimic realistic attack techniques. These target AI inputs, outputs, and integrations. Test cases may include:

• Malicious prompts
• Manipulated data flows
• Unauthorized tool use through agentic interfaces

Scenario design should cover known attack patterns and emerging threats relevant to the system’s context. Thoughtful scenario development increases the likelihood of uncovering meaningful vulnerabilities.

5. Execute Manual and Automated Attacks

Execution includes manual and automated attacks. Manual testing allows red teamers to adapt based on system responses and uncover nuanced vulnerabilities. Automated tools test large volumes of prompts or API requests efficiently. Combining both approaches improves coverage, including elements such as:

• Prompt injection attempts
• Jailbreak and guardrail bypass testing
• Data leakage and extraction attempts
• API and tool misuse testing
• Model evasion and output manipulation

Testing should be monitored to avoid unintended production impacts, and findings should be documented in real time.

6. Document Findings and Business Impact

After testing, teams document vulnerabilities, misbehaviors, and abuse vectors. Documentation should describe each finding, how it was exploited, and its technical root cause. Reports should also assess business impact, including data loss, regulatory exposure, reputational harm, or operational disruption. Prioritizing findings by impact helps stakeholders address critical risks first. 

Documentation should include the following elements to improve reproducibility: 

• Screenshots
• Logs 
• Payload samples

7. Remediate Vulnerabilities and Strengthen Guardrails

Remediation involves fixing vulnerabilities and improving AI guardrails. Actions may include updating:

• Training data
• Refining prompt filters
• Enhancing access controls
• Modifying integrations

Improvements should address both specific weaknesses and related attack vectors. Collaboration between development, security, and data science teams supports effective remediation.

8. Retest Continuously After Model or App Changes

AI systems are dynamic; models, prompts, and logic change frequently. Continuous retesting ensures updates do not reintroduce vulnerabilities. After significant changes, red teams should validate fixes and test for new weaknesses. Retesting should include:

• Guardrail regression testing
• Prompt and policy update validation
• Integration and plugin retesting
• Model drift and behavior checks
• Previously fixed issue verification

Automated regression testing helps maintain security over time, while periodic manual testing uncovers subtle issues.

Common AI Red Teaming Techniques 

Jailbreaking

Jailbreaking attempts to bypass safety policies through crafted prompts. Techniques include role-playing, hypothetical scenarios, multi-step conversations, or encoded instructions that persuade the model to ignore constraints. Red teams test resistance across languages, conversation lengths, and formats to identify weaknesses.

Prompt Injection

Prompt injection crafts inputs that manipulate model behavior and bypass controls. Attackers use phrased prompts or embedded instructions to reveal confidential information, perform unauthorized actions, or generate harmful content. Red teams probe models with varied inputs to evade filters and subvert safety mechanisms. Defenses require ongoing prompt engineering, input validation, and testing against new techniques.

Data Exfiltration Attempts

Data exfiltration testing evaluates whether AI systems can be manipulated into revealing sensitive information, including user data, API keys, system prompts, or retrieved knowledge base content. Red teams simulate extraction attempts and verify that access controls and output filtering function correctly.

Model Evasion

Model evasion modifies inputs so they appear benign while achieving malicious goals. Examples include altering text or files to bypass spam filters or malware detectors. Red teams test adversarial inputs, edge cases, and obfuscated content to evaluate how well models detect malicious intent despite attempts to disguise it. This helps improve model robustness against evolving attack techniques.

Training Data Extraction

Training data extraction tests whether models reproduce memorized training data. Red teams use targeted prompts to detect memorized content and evaluate privacy risks. They also assess whether attackers can reconstruct sensitive information through repeated queries or prompt variations, helping organizations validate privacy safeguards and reduce the risk of unintended data disclosure.

Hallucination and Misinformation Testing

Hallucination testing measures how often AI generates incorrect or fabricated information while presenting it as factual. Red teams challenge models with ambiguous prompts and verify that uncertainty is expressed when appropriate. They also evaluate whether the model cites reliable information, refuses unsupported claims, or clearly communicates confidence and limitations in its responses.

Toxicity, Bias, and Harmful Output Testing

This testing evaluates whether AI produces offensive or discriminatory content. Red teams test across demographics, languages, and sensitive topics to identify inconsistent behavior. The goal is to uncover harmful outputs, biased decision-making, or unequal treatment of different user groups so guardrails and model behavior can be improved.

Tool Abuse and Unauthorized Action Testing

AI systems that call APIs or execute code introduce new risks. Red teams test whether permissions and approvals can be bypassed through prompt manipulation. They also verify that tool access follows least-privilege principles and that sensitive actions require appropriate authorization before execution.

Model Supply Chain and Dependency Testing

AI systems rely on external models, libraries, datasets, and APIs. Red teams review dependency origins, update processes, and risks such as poisoned models or compromised packages. They also assess the integrity of the AI supply chain to identify third-party components that could introduce vulnerabilities or malicious behavior.

AI Red Teaming Frameworks and Standards 

OWASP GenAI Red Teaming Guide

The OWASP GenAI Red Teaming Guide provides a framework for planning, executing, and reporting adversarial testing against generative AI systems. It targets security teams, AI/ML engineers, red teamers, risk managers, and business leaders evaluating LLMs, copilots, chatbots, and agentic workflows. OWASP focuses on real-world behavior rather than traditional software vulnerabilities. It helps test prompt injection, jailbreaks, data leakage, unsafe outputs, tool misuse, bias, and user trust failures.

NIST AI Risk Management Framework

The NIST AI Risk Management Framework (AI RMF) is a voluntary framework for managing risks across the AI lifecycle. It organizes risk management around four functions: Govern, Map, Measure, and Manage. Red teaming supports the Measure and Manage functions by generating evidence of system behavior under adversarial conditions and identifying risks requiring remediation.

MITRE ATLAS

MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base of adversary tactics, techniques, and mitigations for AI-enabled systems. Red teams use ATLAS for threat modeling and test design, mapping AI-specific attack techniques and categorizing findings consistently.

AI Red Teaming Best Practices 

Organizations can improve their AI red teaming approach by implementing the following best practices.

1. Test the Full Application, Not Just the Model

AI security depends on the complete application, including prompts, retrieval pipelines, APIs, authentication, business logic, tools, and user interfaces. Vulnerabilities often arise from interactions between components. Red teaming should test workflows end to end, including role-based access and cross-user boundaries.

Key actions:

• Test end-to-end AI workflows.
• Assess APIs, retrieval, and tool integrations.
• Verify authentication and authorization controls.

2. Validate Guardrails Under Realistic Attack Conditions

Guardrails should be tested with realistic, multi-turn, and obfuscated attack scenarios. Red teams should vary prompt formats and embed attacks in retrieved content or files. Validation should measure both prevention and detection capabilities.

Key actions:

• Test multi-turn and obfuscated attacks.
• Validate prompt injection and jailbreak defenses.
• Measure prevention and detection effectiveness.

3. Prioritize Findings by Business Impact

Findings should be ranked by data exposure, financial loss, regulatory obligations, and customer trust impact. Severity ratings and exploitation scenarios clarify remediation priorities. Organizations should also consider the likelihood of exploitation and the business criticality of affected AI applications so remediation efforts focus on the risks with the greatest operational impact.

Key actions:

• Rank findings by business risk.
• Include exploitability in prioritization.
• Focus remediation on high-impact issues.

4. Retest After Model, Prompt, or Data Changes

Organizations should perform regression testing after significant updates, including prompt changes, model retraining, new integrations, or guardrail modifications. Continuous validation helps ensure security improvements remain effective and that new features do not introduce previously unseen vulnerabilities.

Key actions:

• Run regression tests after updates.
• Validate fixes and guardrail changes.
• Retest new integrations and workflows.

5. Integrate AI Red Teaming Into Security Operations

AI red teaming should feed into vulnerability management, incident response, threat intelligence, and governance workflows. Clear ownership and escalation paths support ongoing security. Integrating findings into existing security operations also helps organizations prioritize remediation, improve detection rules, and continuously strengthen AI security as threats evolve.

Key actions:

• Integrate findings into security workflows.
• Track remediation through ticketing systems.
• Share results with security and AI teams.

Related content: Learn how to operationalize detection in our guide to the AI-powered SOC.

How Exabeam Helps Secure AI Systems and Agents

Exabeam New-Scale Fusion is a modular, cloud-native security operations platform that combines log management, SIEM, behavioral analytics, dynamic risk scoring, automation, and AI-driven assistance in a single experience. It applies behavioral analytics not only to human activity but also to machine activity, including AI agents and other non-human identities, helping security teams detect and respond to the AI-specific threats that red teaming is designed to surface, both before and after models reach production.

Key capabilities of Exabeam New-Scale Fusion:

• Agent behavior analytics: Extends behavioral monitoring to AI agents and other non-human identities, baselining their activity to expose anomalies that signal misuse or compromise.
• AI and agent security use cases: The Exabeam Nova Advisor Agent maps coverage to MITRE ATT&CK^® and the OWASP Agentic Top 10, with benchmarks, assessments, and guidance to strengthen security posture.
• AI-driven investigation: Exabeam Nova agents automate routine and strategic tasks, analyze detections, simplify triage, and create case summaries to accelerate threat response.
• Unified detection and response: Brings together SIEM detections, behavioral analytics, dynamic risk scoring, and AI-driven investigation to reduce investigation time and focus analysts on the highest-risk threats.
• Open, integrated platform: Integrates with existing SIEMs, data sources, and security tools, adding behavioral analytics and improved workflows without restructuring the environment.
• LLM gateway and application log correlation: Ingests and normalizes telemetry from AI safety firewalls, API managers, and custom LLM applications. Correlating these logs with identity data allows security teams to link prompt injections or jailbreak attempts to specific user accounts and trace any subsequent lateral movement.
• Adversarial prompt and query monitoring: Detects rapid, repetitive query patterns and structured inputs characteristic of automated AI red teaming tools. By monitoring user query activity, the platform flags anomalous natural language interactions and repeated safety policy violations.
• Behavioral RAG data exfiltration alerts: Baselines the typical volume and classification of data retrieved by AI applications via Retrieval-Augmented Generation (RAG). The platform triggers high-risk alerts when a user query forces an agent to pull abnormal data volumes or access restricted document repositories, sensitive knowledge repositories.

Ready to monitor, investigate, and respond to threats against your AI systems and agents? Explore the Exabeam New-Scale Fusion Security Operations Platform.