AI Red Teaming: How to Protect Generative AI Systems 

What Is Red Teaming in AI?

AI red teaming involves simulating attacks on real-world applications that incorporate AI features. Technologies such as chatbots, agents, copilots, and AI-powered workflows create new attack surfaces within traditional systems.

The main goal of AI red teaming is to identify and exploit security vulnerabilities that emerge from the integration of AI technologies. It does not involve replacing human red teamers with AI. Additionally, it does not evaluate whether an AI-enabled application follows ethical guidelines or if its outputs appear safe.

For CISOs and SecOps leaders, AI red teaming provides valuable insights into the potential risks posed by Generative AI (GenAI) systems and outlines the necessary changes to mitigate thempotential risks posed by Generative AI (GenAI) systems and outlines necessary changes to mitigate those risks effectively.

Why AI Systems Fail Differently Than Traditional Applications

Traditional application security assumes deterministic execution paths, whereas GenAI systems operate probabilistically and are context-sensitive. Relying solely on source code review seldom predicts their behavior under adversarial input. This results in failure modes that manifest only during adversarial or unexpected usage.

Attack surface in prompts, tools, and retrieval data

AI systems introduce new attack surfaces in prompts, tools, and data. Traditional application security (AppSec) rarely tests these layers directly.

• Prompts act as instructions that can be manipulated to override intent or trigger unsafe behavior. They become control inputs that can change system behavior, especially when system prompts and tool routing are involved
• Tools and plugins, such as application programming interfaces, databases, or ticketing systems, extend model actions into real systems (with whatever permissions the integration allows).
• Data sources, including retrieval augmented generation (RAG) knowledge bases, can expose sensitive content if access controls or filtering fail. RAG pipelines can also pull live data into context windows at inference time, injecting external content directly into the model’s decision-making process. 

Why Do You Need AI Red Teaming?

AI red teaming is especially vital as AI systems become more integrated into high-stakes environments, such as financial systems, healthcare, autonomous vehicles, and critical infrastructure. Additionally, GenAI features are changing the way we think about security in applications. 

When tools like chatbots, copilots, or agents can access internal information, call APIs, or perform tasks in other systems, attackers no longer need to exploit software vulnerabilities. They can simply trick the system into making mistakes by providing misleading inputs, accessing data, or using a legitimate-looking tool response.

According to the latest High-Tech Crime Trends Report, Group-IB experts observed a breach chain in which a single OAuth credential tied to a chatbot integration enabled unauthorized access across multiple connected environments, affecting over 700 organizations.

Organizations use AI red teaming to ensure that security measures hold up against such threats. This approach tests how well the entire GenAI setup can resist manipulation to prevent serious issues such as the exposure of sensitive information and u  unauthorized actions. 

Common failures after launch

High-severity failures usually appear in production environments, where real documents, integrations, and user behaviors intersect. Security teams often find issues only after the system is already connected to sensitive data.

Common points of failure include:

• Indirect prompt injection: Instructions hidden in documents or web pages can unintentionally steer responses or actions.
• Agent tool escalation: Ambiguous tool descriptions may lead agents to misinterpret their scopes, resulting in unauthorized actions or broader data access.
• Unintended data exposure: Misconfigured permissions or poor session handling can expose sensitive information, like personal data or internal files.

Safe outputs do not equal secure systems

A “safe” output does not ensure system security, as content safety evaluation only addresses one aspect of risk, leaving the entire pipeline unexamined.

A system might pass all content policy checks but can still be vulnerable to exploitation. This can result in sensitive documents being exposed, unauthorized API calls being made, personally identifiable information (PII) leaking from storage, or the system being manipulated to bypass your controls. 

Good security is about managing how the system behaves, not just about moderating the output it generates. AI red teaming is designed to thoroughly evaluate and validate a system’s behavior from end to end.

Common Ways GenAI Systems Get Exploited

The most common exploitation techniques targeting GenAI systems are prompt injection, data leakage, tool abuse, RAG retrieval manipulation, and supply chain compromise. 

The following categories are active attack patterns observed in production deployments. They also represent the baseline coverage for most enterprise AI red teaming engagements. 

The dual role of AI as both a defender and an adversary poses a critical security risk. For a deeper look at how attackers are already weaponizing AI, see Group-IB’s Top 5 AI Security Risks to Watch in 2026.

How Does AI Red Teaming Work?

AI red teaming simulates adversarial use of a GenAI-enabled application to uncover abuse paths, going beyond standard testing methods. It tests the full workflow that surrounds the model and validates whether it can be manipulated to leak sensitive data, circumvent business processes, or execute unauthorized actions with tools.

Organizations get the best results by treating AI red teaming as a consistent cycle. Red teamers scope the workflow, test realistic abuse paths, fix what matters, then retest. The process focuses on the highest-risk surfaces in GenAI systems, including prompts, retrieval (RAG), and tool access.

Here’s a practical step-by-step guide that security teams can implement to enhance their red team efforts:

Define the scope and success criteria

Start by identifying the specific use cases you’ll be examining (like chatbots, copilots, or agents) and clarify what the system can access (data sources and tools). It’s important to define what a confirmed issue looks like. 

When thinking about success, focus on concrete outcomes, such as preventing unauthorized data exposure, avoiding cross-user data leaks, stopping unsafe tool actions, or ensuring that required approvals can’t be bypassed.

Map the workflow and trust boundaries

Before you dive into writing tests, take the time to document the key system elements that introduce risk. This doesn’t have to be overly complex, but it should include:

• Entry points: User input, uploaded files, retrieved content, and tool outputs.
• Privileged instructions: System prompts and routing logic.
• Sensitive actions: API calls, record updates, messages sent, and code execution.

This step is what makes later testing realistic and prevents generic prompts from being misrepresented as adequate coverage.

Create high-impact scenarios and a reusable test set

Now, take your documentation and translate it into practical scenarios that reflect your environment. Store these scenarios in a version-controlled set that you can run again after making changes. 

Focus on high-impact scenarios that could cause the most damage, such as prompt injections, misuse of retrieval functions, tool abuse, and cross-user access failures.

Execute tests and capture evidence

Run your scenarios through multi-turn conversations, playing around with different tool selections and retrieval options. Make sure to collect all evidence needed for your engineering team to replicate and solve any issues, such as inputs, outputs, retrieved snippets, tool calls, and any downstream effects

Mitigate risks, report, and retest

Address any confirmed vulnerabilities and retest the workflow to ensure the attack vector has been closed. When reporting, ensure that every finding is presented clearly, enabling engineering teams to reproduce and resolve the issue efficiently. Include evidence of the attack path, its impact on the workflow, and guidance for remediation.

Tip: For every confirmed finding, include verifiable data, such as inputs, outputs, retrieval results, and tool calls. Finally, run the same scenario again, along with a few variations to ensure that the abuse path is closed and will not reemerge after future updates.

When To Run AI Red Teaming

AI red teaming should run before launch, after meaningful changes, and continuously. It delivers the most value when integrated into the lifecycle of a GenAI application rather than treated as a one-time test.

Most teams run AI red teaming at three key stages: 

• Before initial deployment to establish a security baseline and identify risky abuse paths before users interact with the system.
• After any significant updates to prompts, models, tools, or data pipeline.
• On an ongoing basis, by converting confirmed abuse cases into regression tests or as new adversarial techniques emerge.

In the 2026 High-Tech Crime Trends report, Group-IB experts note that several AI-driven malspam tools were active in 2025. Attackers can quickly adapt and expand their campaigns, which is why AI red teaming should be repeated after significant changes.

How AI Red Teaming Complements AppSec Testing and Traditional Red Teaming

Many security programs already use AppSec testing to find bugs and misconfigurations, along with traditional red teaming to evaluate their detection and response capabilities.

AI red teaming adds the missing layer to GenAI deployments by testing what happens when an attacker interacts with the assistant itself, especially when it can retrieve internal content and actfor GenAI deployments by testing what happens when an attacker interacts with the assistant itself, especially when it can retrieve internal content and take actions through tools. The main risk is often workflow manipulation rather than a code defect.

Here’s how organizations can use AI red teaming alongside traditional AppSec and red teams:

• AppSec testing ensures that applications and their integrations are secure, focusing on areas such as authentication, API security, and configuration issues that could potentially expose the model gateway, retrieval services, or tool endpoints.
• Traditional red teaming simulates how an attacker might realistically access the GenAI feature and assesses whether monitoring and response mechanisms are effective once access is achieved.
• AI red teaming assumesoperates under the assumption that the attacker is already inside the conversation. This approach tests whether prompts, RAG, and tool permissions can be exploited to expose data, bypass workflows, or perform unsafe actions.

AI Red Teaming Tools 

The tools available for AI red teaming are still in development, and there isn’t a single standard platform that everyone uses. AI red teaming is currently more of a capability than a defined product category. For now, most real-world engagements rely heavily on the manual testing skills of experienced red teamers.

In practice, security teams blend their adversarial knowledge with a few key tools. These tools help create attack prompts, automate checks, and enable testing across thousands of interactions.

We’re starting to see several categories of tools emerge in this area.

Adversarial prompt testing tools 

These tools generate large volumes of adversarial prompts designed to cause unsafe or unintended model behavior. They help red teams test for common attack patterns such as prompt injection, data leakage, and instruction override.

Examples include open-source frameworks such as PyRIT and Garak, which allow testers to run structured attack scenarios and evaluate how models respond.

Automated safety and evaluation frameworks

Safety evaluation tools assess a model’s output against safety policies and behavioral standards. Security teams often integrate these checks into CI/CD pipelines to automatically test updates to prompts, models, or data before deployment. 

These tools help to catch regressions early when previously fixed vulnerabilities reappear after system updates. Examples include DeepEval and OpenAI Evals, which are commonly used to evaluate LLM outputs across safety, hallucination, and accuracy benchmarks. 

Explainable AI (XAI) libraries

Explainable AI (XAI) is a method for understandingused to understand why an AI model produces a specific outcome. During red team exercises, XAI can support root-cause analysis by identifying which inputs, training signals, or contextual data are influencing a model’s response.

Commonly used libraries include:

• SHAP (SHapley Additive exPlanations), feature contributions to model predictions using Shapley values.
• LIME (Local Interpretable Model-agnostic Explanations), a technique implemented through libraries that approximate how input features influence a model’s output locally.
• Captum, an interpretability library designed for deep learning models built with PyTorch.

Real-World Examples of AI Red Teaming 

Several high-profile exercises have demonstrated how structured adversarial testing can uncover risks before large-scale deployment.

DEFCON Generative AI Red Teaming Challenge

In 2023, Humane Intelligence hosted one of the largest Generative AI Public red teaming events for closed-source API models at DEFCON 2023. Thousands of participants tested LLMs from companies such as OpenAI, Google, Microsoft, and Anthropic.

Participants generated large datasets of adversarial prompts, revealing and revealing weaknesses in model guardrails that developers later used to improve safety mechanisms. The initiative demonstrates how adversarial testing can inform the deployment of safer AI.

OpenAI External Red Teaming for GPT-4

Before releasing GPT-4, OpenAI conducted external red teaming with independent security researchers and domain experts. Red teamers attempted to exploit the model across a range of scenarios, including generating misinformation, assisting with cybercrime techniques, or producing harmful content. The findings were used to refine safety systems and guardrails before the model’s public launch.

Challenges of Red Teaming Generative AI 

Generative models behave differently from traditional software and introduce new complexities for red teamers. Here are the main challenges that may impact enterprise AI red teaming efforts:

Non-Deterministic Model Behavior

GenAI systems don’t always give the same output for the same input. Even slight changes in prompts, context, or the data retrieved can result in different responses. This characteristic makes vulnerabilities harder to reproduce and security testing more difficult to standardize. 

Complex Architectures

AI systems rarely consist of a model alone. Enterprise setups usually combine components such as prompts, orchestration logic, retrieval pipelines, external APIs, and automated tools. 

As a result, security testing needs to assess the entire workflow around the model. Most vulnerabilities actually arise from integrations, data flows, or tool permissions rather than from the model itself.

During enterprise AI security assessments, Group-IB red teamers frequently test for prompt injection, retrieval manipulation, and tool abuse across GenAI workflows. These exercises simulate how attackers could manipulate prompts, retrieve sensitive documents through RAG pipelines, or initiate unintended actions through connected APIs.