Building trust through AI red teaming: Red Hat's approach to testing model safety

In the last few years, large language models (LLMs) have moved from research labs to production systems powering critical business functions. This rapid adoption poses a fundamental challenge for enterprises: How do you deploy AI with confidence when models can behave unpredictably under adversarial conditions? The question keeping IT leaders awake isn't if their AI will fail—it's when, and what will the consequences be?

As we've already discovered, traditional software testing approaches fall short when applied to AI. Models don't just have bugs that can be discovered and quickly patched, they may have much more complex vulnerabilities that might be exploited through carefully crafted prompts. These can be used to generate harmful, biased, or inappropriate content that can damage reputation, violate regulations, and erode user trust. Without systematic red teaming, organizations are deploying blind, hoping their models won't break in the field.

At Red Hat, our AI safety strategy is built on a fundamental principle that security and safety capabilities cannot be bolted on after deployment, they must be integrated throughout the AI lifecycle, from data generation to continuous monitoring in production. In this post, we'll share how Red Hat AI delivers a comprehensive safety stack that makes adversarial testing for LLMs accessible, scalable, and continuous.

What is red teaming?

Red teaming is a structured, adversarial security exercise where you deliberately try to break or exploit a system—an application, an organization, or even an AI model—in order to uncover weaknesses before real attackers do.

One of the biggest gaps in enterprise AI adoption is the lack of systematic red teaming capabilities. Most organizations either skip adversarial testing entirely or rely on ad-hoc manual efforts that don't scale with the pace of AI development; this means that models can reach production without comprehensive safety validation. Red Hat AI helps address this gap with an integrated safety stack built on open source innovation and enterprise-grade reliability. Our approach brings together multiple components that work better together:

• SDG Hub serves as the foundation for scalable adversarial data generation. This modular synthetic data generation toolkit automates the creation of red teaming datasets across multiple harm categories, enabling systematic testing rather than hoping you've covered all the edge cases. Find an example workflow.
• Building on our acquisition of Chatterbox Labs, Red Hat has developed a custom harness using the open source Garak framework, a technology preview (TP) feature as part of Red Hat AI 3.4. This harness employs increasingly complex methods to systematically attempt to jailbreak target models, probing vulnerabilities with sophisticated adversarial testing techniques.
• NeMo Guardrails, generally available (GA) and integrated into Red Hat OpenShift AI, provides intelligent runtime protection that intercepts and neutralizes harmful outputs before they reach users.
• The entire workflow can be triggered using AI Pipelines from eval hub—Red Hat's open source control plane for LLM evaluations with multiple backends—with a single API call on OpenShift AI. This enables continuous monitoring, helping protect models as they evolve.

Figure 1: User experience of red teaming workflow for testing models on Openshift AI using sdg hub, NVIDIA Garak, eval hub, and NVIDIA Nemo Guardrails

While the underlying architecture includes multiple specialized components, the user experience is as straightforward as triggering a job as illustrated in Figure 1. Teams can initiate comprehensive red teaming without needing to manually coordinate data generation, attack execution, and guardrail evaluation.

The entire process runs automatically once triggered, from generating adversarial test cases, to applying sophisticated jailbreak attempts, to evaluating protection effectiveness. This simplicity makes enterprise-scale AI safety testing accessible to teams without requiring deep security expertise in each component.

For many enterprises, trust and safety are the primary blockers to AI adoption, not just performance or cost. Red Hat's integrated safety stack helps organizations test their AI deployments against adversarial scenarios before deployment, protect them with runtime guardrails at the inference boundary, and observe them through continuous safety metrics tracking throughout the model lifecycle.

Final thoughts

Unprotected AI carries significant risks—reputational damage, regulatory exposure, and remediation costs that far exceed prevention—which can cause organizations to slow or halt deployments and cede competitive advantage. Red Hat's integrated safety stack helps address this by making enterprise-scale AI safety accessible through open source innovation.

Building on collaboration with NVIDIA (NeMo Guardrails and Garak) and the broader community, red teaming capabilities that once required specialized security expertise are now available through automated workflows as "AI safety as code." This continuous safety approach integrates adversarial testing with runtime protection throughout the model lifecycle, making it systematic,  scalable, and accessible from day one.