What Is AI Red Teaming?

AI systems do not behave like traditional software, where a normal application follows defined logic: A user clicks a button, submits a form, or calls an API, and the system responds in a predictable way. AI systems are more dynamic. A small change in a prompt, data source, user role, or retrieved context can change what the system says or does.

That makes standard testing necessary, but not always enough. AI red teaming helps teams test how an AI system behaves under pressure, especially when users try to manipulate instructions, extract sensitive information, or make the system act outside its intended purpose.

AI red teaming is especially important for:

• Generative AI applications that create text, code, summaries, or recommendations
• Large language models (LLMs) that respond to open-ended user input
• AI agents that can take actions through tools, APIs, plugins, or connected systems
• Internal knowledge assistants that retrieve sensitive company information
• Customer-facing chatbots that interact with users at scale

The goal is not to prove an AI system is perfect, but to find where it can fail, understand the impact of those failures, and reduce risk before the system is exposed to real users or attackers.

How AI red teaming works

AI red teaming uses an adversarial mindset, with testers thinking like attackers, curious users, insiders, and people trying to bend the system’s rules. They test not only whether the AI gives the “right” answer, but whether it can be pushed into unsafe, unauthorized, or unreliable behavior.

A typical AI red teaming workflow includes several stages:

Define the system and scope

The team starts by identifying what’s being tested. That might be a standalone model, an AI-powered application, a chatbot, an internal assistant, or an agent connected to other systems. Scope should include:

• The model or models in use
• The application layer around the model
• Data sources the system can access
• Tools, APIs, or plugins the AI can call
• User roles and permissions
• Safety policies or business rules the system must follow

This matters because AI risk often appears at the boundaries. A model may behave safely on its own, but become riskier when it can retrieve confidential data, summarize untrusted web content, or trigger actions in another system.

Build threat scenarios

Next, the team defines realistic ways the system could be misused. These scenarios should reflect how the AI system actually works, not generic attack ideas.

For example, an internal AI assistant might be tested for data leakage across departments. A customer support chatbot might be tested for whether it reveals internal instructions. An AI agent might be tested for whether it can be tricked into calling the wrong API or changing a record without proper approval.

This is where AI red teaming overlaps with threat modeling. Teams identify who might misuse the system, what they might try to access or change, and which controls should stop them.

Test with adversarial inputs

The red team then probes the system with prompts, files, context, user actions, or chained instructions designed to make it fail. These tests may include direct user prompts, indirect instructions hidden in retrieved content, or multi-step interactions that slowly steer the system away from its intended behavior.

Common testing methods will include:

• Asking the AI to ignore or reveal hidden instructions
• Trying to bypass safety rules through roleplay or rewording
• Hiding malicious instructions in documents or web pages
• Requesting sensitive data the user shouldn’t access
• Attempting to make an agent misuse a connected tool

For readers exploring one of the most common risk areas, prompt injection attacks are a useful adjacent concept because they show how malicious instructions can alter AI behavior.

Analyze findings and retest

Strong AI red teaming doesn’t stop when a failure is found. Teams need to document what happened, why it matters, how severe it is, and whether a fix actually works.

Findings should be specific enough for security, engineering, and governance teams to act on. A useful finding explains the prompt or scenario used, the response or action observed, the expected behavior, the potential impact, and the recommended remediation.

Key risks that AI red teaming tests

AI red teaming can test many types of risk, but most findings fall into a few common categories.

• Prompt injection: A user or external source gives the AI instructions that override, redirect, or conflict with the system’s intended rules.
• Jailbreaking: A user finds a way to bypass model safeguards and make the system produce restricted, unsafe, or policy-violating content.
• Data leakage: The system reveals sensitive data, hidden prompts, internal context, credentials, customer information, or information from another user’s session.
• Unsafe outputs: The AI generates harmful, biased, false, misleading, or inappropriate content that could create user, business, or compliance risk.
• Tool misuse: An AI agent uses connected tools in unintended ways, such as retrieving the wrong record, sending unauthorized requests, or making changes without proper checks.
• Model manipulation: Inputs steer the AI toward unreliable behavior, attacker-controlled outputs, or conclusions that appear confident but aren’t supported.

These risks become more important as AI systems gain access to business data and operational tools. A chatbot that only answers general questions has a different risk profile than an AI agent that can query internal systems, summarize sensitive documents, or perform actions on behalf of a user.

This is why AI security posture management (AI-SPM) is closely related. Red teaming can reveal weaknesses in specific systems, while posture management helps teams understand and manage AI risk across a broader environment.

AI red teaming examples and use cases

Customer support chatbot

A red team might test whether a chatbot can be manipulated into revealing internal instructions, escalation rules, customer data, or information outside its approved knowledge base.

The team might also test whether the chatbot gives unsafe advice, invents policies, or handles sensitive requests inconsistently.

AI agent with tool access

An AI agent can create more risk because it may not only respond, but act. If the agent can call APIs, update tickets, retrieve records, or trigger workflows, red teaming should test whether it respects permission boundaries and approval steps.

This is especially relevant as teams explore agentic workflows and standards like Model Context Protocol (MCP), where tool access and context handling become central security questions.

LLM-powered security assistant

A security assistant might summarize alerts, recommend next steps, or explain suspicious behavior. Red teaming can test whether malicious context causes the assistant to suppress important details, misclassify risk, or generate unsafe recommendations.

Findings from this type of testing can inform AI threat detection by showing what suspicious AI interactions look like in practice.

Internal knowledge assistant

An internal assistant may have access to policies, documents, tickets, or team-specific knowledge. AI red teaming can test whether users can extract restricted information through reworded prompts, indirect requests, or attempts to confuse the system about identity and access.

How AI red teaming fits into security operations

AI red teaming complements existing security practices, but doesn’t replace them. Traditional red teaming usually focuses on infrastructure, identity, networks, applications, and human processes, while AI red teaming focuses on how AI systems behave when users, inputs, context, and tools interact in unexpected ways.

It also differs from penetration testing in that a penetration test often looks for exploitable technical vulnerabilities in an application or environment. AI red teaming may include technical testing, but it also examines model behavior, unsafe outputs, prompt handling, policy bypasses, and misuse scenarios.

In a mature security program, AI red teaming can feed several workflows:

• Application security: Fix weaknesses in AI-powered applications before release
• Threat modeling: Update assumptions about how AI systems can be misused
• Detection engineering: Create signals for suspicious prompts, tool calls, or agent behavior
• Incident response: Prepare for AI-related misuse, data exposure, or unsafe actions
• Governance: Document risk decisions, control gaps, and remediation progress

The most effective approach treats AI red teaming as iterative – AI systems change when prompts, models, plugins, tools, data sources, and user workflows change. Red teaming should happen before launch, after major changes, and when new risk patterns emerge.