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 assess whether an AI-enabled application adheres to ethical guidelines or whether its outputs are 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 them effectively.

Why AI Systems Fail Differently Than Traditional Applications

Unlike traditional applications, which produce consistent outputs for the same inputs, AI systems can produce different results from the same prompt. No fixed code path exists to verify, making it difficult to audit their behavior. Traditional application security relies on this predictability, where testers can map inputs to outputs and ensure the logic remains consistent during reviews. 

Generative AI models evaluate each input based on contextual information and system instructions. This shifts the vulnerability from a specific line of code to the model’s overall behavior. Security teams identify these failures only when they challenge the system with adversarial inputs.

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?

A thorough AI risk assessment is no longer optional as AI systems become more integrated into high-stakes environments, such as financial systems, healthcare, autonomous vehicles, and critical infrastructure. GenAI features are also changing how we think about application security. 

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 instead trick the system by providing misleading inputs, accessing data, or using a legitimate-looking tool response.

According to the 2026 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. No core vulnerability was exploited. The attacker used a trusted integration as designed, which is precisely the kind of abuse path AI red teaming is built to surface before an attacker finds it.

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 connects 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

Safe output does not ensure system security, as content safety evaluation addresses only one aspect of risk and leaves the rest of the pipeline unexamined.

A system might pass all content policy checks but can still be vulnerable to exploitation. This can expose sensitive documents, enable unauthorized API calls, leak personally identifiable information (PII) from storage, or let attackers manipulate the system 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.

AI plays both roles in this equation. It is a defensive tool for security teams and, increasingly, a resource attackers weaponize against the same systems. For a closer look at how AI expands the attack surface, 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 surrounding the model and validates whether it can be manipulated with tools to leak sensitive data, circumvent business processes, or execute unauthorized actions.

Skilled red teamers run these tests in a controlled, production-safe manner, so the assessment surfaces real abuse paths without disrupting live services, corrupting data, or affecting real users.

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 examine (such as chatbots, copilots, or agents) and clarifying what the system can access (data sources and tools). 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 write tests, take 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 so you can rerun them 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. The engagement remains production-safe because testers use controlled, reversible inputs that probe abuse paths without triggering destructive actions on live data.

Collect all evidence your engineering team needs to replicate and resolve any issues, including inputs, outputs, retrieved snippets, tool calls, and any downstream effects.

Mitigate risks, report, and retest

Address confirmed vulnerabilities and retest the workflow to ensure the attack vector is closed. When reporting, present each finding clearly so engineering teams can 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, rerun the same scenario, along with a few variations, to ensure 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, establish a security baseline and identify risky abuse paths before users interact with the system.
• After any significant updates to prompts, models, tools, or the data pipeline.
• Ongoing, 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, so teams should repeat AI red teaming after significant changes.

How AI Red Teaming Complements AppSec Testing and Traditional Red Teaming

AI red teaming complements AppSec testing and traditional red teaming by testing the layer that neither fully covers: what happens when an attacker interacts with the AI assistant itself, especially when it can retrieve internal content and take actions through tools. 

Many security programs already use AppSec testing to find bugs and misconfigurations, along with traditional red teaming to evaluate their detection and response capabilities. Neither is built to test the assistant as an attack surface in its own right, which is where 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, such as Group-IB’s Managed XDR, are effective once access is achieved.
• AI red teaming assumes 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

AI red teaming tools are still in development, and no single standard platform exists. AI red teaming is currently more of a capability than a defined product category. The gap is most visible in agentic AI security, where 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 seeing 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 run these checks in CI/CD pipelines to test updates to prompts, models, or data before deployment, so issues surface in a controlled pre-production environment rather than in front of real users. This keeps evaluation production-safe, since the tests run against staged changes and never expose live traffic to untested behavior.

These frameworks catch regressions early when previously fixed vulnerabilities reappear after system updates. Examples include DeepEval and OpenAI Evals, which security and engineering teams use to evaluate LLM outputs across safety, hallucination, and accuracy benchmarks.

Explainable AI (XAI) libraries

Explainable AI (XAI) is a method for understanding 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 influence 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 is 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.

EchoLeak: Zero-click data exfiltration in Microsoft 365 Copilot

EchoLeak showed how a single crafted email could pull sensitive data from Microsoft 365 Copilot without user interaction. In June 2025, Aim Labs disclosed EchoLeak (CVE-2025-32711), chaining several bypasses, including Copilot’s prompt injection classifier and content security policy, to exfiltrate data from its context. Aim Labs reported the flaw to Microsoft, which patched it server-side and confirmed no customers were affected before disclosure.

Skeleton Key: A jailbreak found through Microsoft’s own AI Red Teaming

Skeleton Key is a jailbreak Microsoft’s own AI red teamers found in 2024 that pushed several production models past their safety guardrails through a multi-turn conversation rather than one crafted prompt. It worked across models from OpenAI, Google, Meta, Anthropic, Mistral, and Cohere. Microsoft disclosed the findings to affected vendors first, then updated Azure AI’s Prompt Shields and added Skeleton Key scenarios to PyRIT.

DEFCON Generative AI Red Teaming Challenge

This challenge showed what public, at-scale adversarial testing can surface that a small internal team might miss. In 2023, Humane Intelligence co-organized one of the largest Generative AI public red-teaming events for closed-source API models, held at DEF CON 2023. Thousands of participants tested LLMs from companies such as OpenAI, Google, NVIDIA, Microsoft, and Anthropic.

Participants generated large datasets of adversarial prompts, 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.

The same DEFCON program ran GRT2 at DEFCON 32 and, most recently, GRT3 at DEFCON 33 (2025), shifting focus from attacking models directly to attacking the evaluation methods vendors use to certify model safety.

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 helped refine safety systems and guardrails before the model’s public launch.

Challenges of AI Red Teaming 

Red teaming generative AI comes with challenges that traditional software testing doesn’t prepare you for, since GenAI models behave differently from deterministic code. 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 retrieved data can produce different responses. This makes vulnerabilities harder to reproduce and security testing harder 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. Agentic AI security sits at the intersection of all of them, since an agent’s failures usually trace back to how these pieces interact rather than to any single component. 

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 via RAG pipelines, or trigger unintended actions via connected APIs.

Evolving attack techniques

The techniques attackers use against GenAI systems are shifting from single-prompt tricks toward attacks that target the tools, memory, and context windows an agent relies on, which is why a fixed test suite goes stale fast.

• MCP tool poisoning: The Model Context Protocol lets AI agents discover and call external tools, and its unsanitized tool-description field is a direct attack surface. A malicious MCP server can hide instructions inside a tool’s description that the agent follows silently, enabling data exfiltration or hijacking of other trusted tools in the same session. CVE-2025-49596 (CVSS 9.4), an authentication flaw in Anthropic’s MCP Inspector, allowed unauthenticated remote code execution. A peer-reviewed study of 1,899 open-source MCP servers found 7.2% carried general vulnerabilities and 5.5% showed MCP-specific tool poisoning. Maps to OWASP’s Agentic Top 10 ASI04 (supply chain) and ASI03 (identity and privilege abuse).
• Memory and context poisoning: Attackers plant instructions or false context early in a session that only activate later, exploiting an agent’s memory across turns rather than evaluating each prompt in isolation. Tracked as ASI06 in OWASP’s Top 10 for Agentic Applications.
• Many-shot jailbreaking: In Anthropic’s many-shot jailbreaking paper, the company disclosed that adding a prompt with hundreds of faux dialogues showing an AI assistant complying with harmful requests overrides the model’s safety training through in-context learning, though it barely works with only a few examples. Effectiveness followed a power-law curve up to the 256-shot maximum Anthropic tested, across Claude 2.0, GPT-3.5, GPT-4, Llama 2 (70B), and Mistral 7B. One prompt-based mitigation cut a specific attack’s success rate from 61% to 2%. 
• Agent-to-agent manipulation: In multi-agent workflows, a compromised agent can pass poisoned instructions to another it coordinates with, letting an attack move laterally without touching a human-facing prompt.

Tip: Effective AI red teaming focuses on high-risk workflows and realistic attack paths, rather than testing every possible prompt variation. Combining structured adversarial testing with real-time threat intelligence can reveal which prompt injection techniques, automation tools, or abuse campaigns are already being used against GenAI systems.

Best Practices for Implementing AI Red Teaming: Step-by-Step 

Implementing AI red teaming step-by-step comes down to four repeatable moves: scope and map the system, build adversarial scenarios, run and analyze the tests, then remediate and retest. The detailed walkthrough above covers each stage in depth. Use this condensed version once your team already understands the process.

Step 1: Define the scope and map the AI attack surface

Start by locking down which use case you’re testing and what it can touch.

• List the chatbots, copilots, or agents in scope, along with the data sources and tools each one can reach.
• Document entry points, privileged instructions, and sensitive actions before writing a single test case.
• Write down what a confirmed issue looks like so findings are judged against a fixed bar, not a moving one.

Step 2: Identify threats and create adversarial test scenarios

Turn your mapped attack surface into a reusable set of high-impact test scenarios.

• Prioritize scenarios that mirror the exploitation categories covered earlier, such as prompt injection, tool abuse, and RAG manipulation.
• Store scenarios in a version-controlled set so you can rerun the same tests after every update.
• Weight scenarios by potential damage, not just by how easy they are to script.

Step 3: Execute tests, analyze findings, and prioritize risks

Run the scenarios in a controlled, production-safe environment and rank results by real-world impact.

• Test across multi-turn conversations and varied tool or retrieval choices, not single-shot prompts.
• Capture full evidence for every finding, including inputs, outputs, tool calls, and retrieved content.
• Rank findings by what an attacker could actually reach, not by how novel the technique looks.

Step 4: Remediate vulnerabilities and retest the AI system

Close the confirmed gaps, then prove they stay closed.

• Fix the highest-impact findings first and hand engineering teams clear reproduction steps.
• Convert every confirmed finding into a regression test so you can rerun it after future changes.
• Retest with variations on the original scenario, not just the exact input that triggered the finding.

OWASP and Other Emerging AI Safety Frameworks

Security teams increasingly lean on four frameworks to structure AI red teaming, as GenAI risk has shifted toward agentic systems. Together they give teams a shared vocabulary for testing, from what a model outputs to what an autonomous agent does with that output.

• OWASP Top 10 for Large Language Model (LLM) Applications ranks the risk categories most likely to compromise LLM-powered applications, including prompt injection, sensitive information disclosure, and excessive agency. The 2026 edition, published in August 2026, moved excessive agency up the rankings to reflect how often agentic tool access, not the model’s raw output, ends up being the actual point of failure.
• OWASP Top 10 for Agentic Applications covers risks unique to autonomous agents that plan, hold memory, call tools, and act with delegated authority, such as agent goal hijacking, tool misuse, and identity and privilege abuse. It extends the LLM Top 10 rather than replacing it, so teams testing agentic AI security typically use both together.
• MITRE ATLAS Threat Matrix documents real-world adversarial techniques targeting machine learning systems. Its coverage has grown quickly alongside agentic AI adoption, now spanning more than 80 techniques across 16 tactics, including new additions for AI agent tool abuse and credential harvesting.
• The NIST AI Risk Management Framework (AI RMF) provides guidance on identifying and managing AI risks throughout the AI system lifecycle. Its companion Generative AI Profile (NIST AI 600-1) adds twelve risk categories specific to generative systems, covering areas like data privacy, confabulation, and information integrity.

These frameworks tell you what to look for. Group-IB’s AI Red Teaming service maps its testing scenarios to them and adds what a framework alone can’t provide: evidence of whether your specific system actually holds up.

Getting Started with AI Red Teaming 

Red teaming for GenAI systems doesn’t have to be resource-intensive. For many organizations, a practical starting point is a focused AI risk assessment to evaluate how their AI applications perform under adversarial conditions. 

What sets Group-IB’s approach apart is combining several disciplines under one roof. Our team brings more than 20 years of experience in incident response, threat hunting, and red teaming, plus hands-on ML engineering and AI threat intelligence. Group-IB’s AI Red Teaming service combines adversarial testing with insights from the Threat Intelligence platform, which monitors cybercriminal activity and emerging attack techniques in real time.

The engagement simulates real-world adversarial behavior to assess how your AI applications perform under pressure, with actionable insights that will strengthen your defenses, including:

• Identifying vulnerabilities in GenAI models, prompts, and integrations.
• Offering practical remediation guidance tailored to your organization’s AI architecture.
• Prioritizing risks based on real attacker techniques and emerging threats. 
• Providing evidence of proactive security testing for internal stakeholders and regulators.

When deploying GenAI at scale, structured AI red teaming lets you safely test new capabilities, strengthen controls, and reduce the risk of data exposure, model misuse, or reputational damage.

Talk to Group-IB experts today for a proof of concept on how AI red teaming can help you deploy programs safely and secure your systems.

Evolving Attack Techniques

Attackers and security researchers are developing new adversarial techniques, including prompt-injection variants, jailbreak methods, and retrieval manipulation. AI red teaming cannot rely solely on static test cases, as these techniques evolve quickly.

Tip: Effective AI red teaming focuses on high-risk workflows and realistic attack paths, rather than testing every possible prompt variation. Combining structured adversarial testing with real-time threat intelligence can reveal which prompt injection techniques, automation tools, or abuse campaigns are already being used against GenAI systems. 

OWASP and Other Emerging AI Safety Frameworks

As organizations adopt generative AI systems, they’re turning to various security frameworks to support their use. The following frameworks assist security teams in organizing AI red teaming exercises and identifying common failure points:

• OWASP Top 10 for Large Language Model Applications, which highlights important risk categories like prompt injection, sensitive information disclosure, and handling insecure output. Many security teams rely on this framework to determine which adversarial testing scenarios to prioritize during their AI red team efforts.
• MITRE ATLAS Threat Matrix, which outlines adversarial techniques targeting machine learning systems, including model manipulation and data poisoning.
• The NIST AI Risk Management Framework (AI RMF) provides guidance on identifying and managing AI risks throughout the lifecycle of AI systems, covering areas such as governance, testing, and monitoring. 

Getting Started with AI Red Teaming 

Red teaming for GenAI systems doesn’t have to be resource-intensive. For many organizations, a practical starting point is to evaluate how their AI applications perform under adversarial conditions. 

What sets Group-IB’s approach apart is the combination of several disciplines under one roof. Our team brings together more than 20 years of experience in incident response, threat hunting, and red teaming with hands-on ML engineering and AI threat intelligence. Group-IB’s AI Red Teaming service combines adversarial testing with insights from the Threat Intelligence platform, which monitors cybercriminal activity and emerging attack techniques in real time.

The engagement simulates real-world adversarial behavior to assess how your AI applications perform under pressure, with actionable insights that will strengthen your defenses, including:

• Identifying vulnerabilities in GenAI models, prompts, and integrations.
• Offering practical remediation guidance tailored to your organization’s AI architecture.
• Prioritizing risks based on real attacker techniques and emerging threats.
• Providing evidence of proactive security testing for internal stakeholders and regulators.

When deploying GenAI at scale, structured AI red teaming allows you to safely test new capabilities, strengthen controls, and reduce the risks of data exposure, model misuse, or reputational damage.

Talk to Group-IB experts today for a proof of concept on how AI red teaming can help you deploy programs safely and secure your systems.