AI red teaming is a security assessment process where a dedicated group—the red team—simulates adversarial attacks against AI systems, models, policies, and applications. The goal is to identify vulnerabilities, demonstrate the impact of potential attacks, and rigorously test existing defenses.
AI red teaming uses attack simulations to uncover behavioral risks that arise from how AI investments reason, generate content, and interact with users and other systems. This process helps IT and security decisionmakers understand how AI systems can fail—and how those failures could affect confidentiality, integrity, availability, safety, and compliance. For large language models (LLMs) and multimodal AI, AI red teaming involves both testing technical weaknesses as well as logical, ethical, and policy-based failures.
AI red teaming plays a critical role in mitigating adversarial AI and other advanced cyberattacks. It’s essential due to the rapid evolution of threats, the rise of agentic AI, new regulations, and the unpredictability of LLMs and generative AI systems.
Overall, organizations are adopting AI red teaming because of drivers such as:
For CISOs and CIOs, AI red teaming is becoming a foundational control—similar to penetration testing for applications or threat modeling for infrastructure.
AI red teaming typically follows a repeatable lifecycle designed to produce actionable, defensible results.
Compared to traditional machine learning and software security testing, LLM and generative AI red teaming must account for fundamentally new behaviors and challenges.
Here, the key differences include:
As a result, AI red teaming blends elements of application security, social engineering, model evaluation, and governance testing into a single discipline.
AI red teaming uses a range of techniques to probe model behavior under adversarial conditions. Frequently used approaches include jailbreaks, adversarial prompting, automated probing, and safety stress-testing.
A growing ecosystem of open-source tools, commercial platforms, and safety evaluation frameworks supports AI red teaming efforts. These range from lightweight prompt-testing utilities to enterprise platforms that integrate testing into CI/CD and MLOps pipelines.
Open-source and community tools are often used for experimentation and research, while commercial offerings provide automation, reporting, governance alignment, and scalability required by large organizations.
As AI adoption grows, organizations need ways to operationalize red teaming across multiple models, teams, and business units. Enterprise-grade platforms help by:
At scale, AI red teaming becomes a continuous control that supports ongoing model updates, new use cases, and evolving threat landscapes.
Automated adversarial testing uses AI to generate attacks, run simulations, and score model vulnerabilities. Automation enables broader coverage, faster feedback, and consistent evaluation across environments, while human experts focus on high-impact and novel attack paths. For enterprise leaders, automation is key to making AI red teaming repeatable, measurable, and cost-effective.
When deployed as part of an enterprise red teaming program, automation allows organizations to continuously test models as they change—across versions, deployments, and business units—without relying on manual effort alone. This makes it possible to scale red teaming in step with AI adoption and ensure that security and risk controls keep pace with the growth and complexity of enterprise AI systems.
F5 AI Red Team combines three automated testing types—agentic resistance, signature attacks, and operational attacks—for full-spectrum validation. Agentic resistance tests run dynamic, multi-turn campaigns that emulate sophisticated real-world attackers and generate agentic fingerprints for transparent explainability. Signature attacks leverage tens of thousands of up-to-date prompts every month that keep testing aligned to emerging threat techniques, while operational attacks validate resilience under stresses such as crashes, resource exhaustion, or latency. Together, these methods deliver high-confidence vulnerability discovery across models, apps, and integrations.
Using this solution, security teams get prioritized remediation guidance in detailed reports that include successful malicious prompts, model responses, security scores, and severity classifications. Recurring campaign scheduling and CI/CD integration let organizations adopt continuous, automated testing, closing the gap between development and secure production rollouts. These insights also feed F5 AI Guardrails, enabling defenders to translate AI Red Team findings into runtime policies and protections rapidly.
