February 10, 2026
The OWASP GenAI Red Teaming Guide, a widely referenced resource in the field, structures this holistic practice around four interconnected areas. These areas form the foundation of thorough adversarial evaluation, addressing risks that span model behavior, code integration, underlying infrastructure, and live operations.
This area focuses directly on the core generative model itself, probing intrinsic weaknesses independent of surrounding code or deployment.
Red teamers craft adversarial prompts and sequences to expose these issues, measuring refusal effectiveness, output harm severity, and consistency across sampling methods. Evaluation often incorporates automated scorers alongside human judgment for nuanced socio-technical harms.
Thorough model-level testing establishes a baseline understanding of behavioral risks before examining how the model behaves in realistic environments.
Here the focus shifts to how the model integrates into applications, APIs, agents, or workflows. Many severe incidents stem from poor engineering choices rather than model flaws alone.
Testing involves simulating end-to-end attack chains—combining crafted prompts with tool calls, external data retrieval, or multi-step reasoning—to reveal implementation gaps. Emphasis falls on realistic misuse scenarios that mirror production usage patterns.
This layer reveals how seemingly minor oversights can cascade into major breaches or harmful behaviors.
GenAI systems rely on complex backends, making supply-chain and operational infrastructure prime targets.
Red teamers evaluate access controls, dependency scanning, runtime isolation, and supply-chain integrity. Techniques include attempting model inversion, probing for side-channel leaks, and testing container or serverless configurations for escape paths.
Strong infrastructure hardening prevents foundational compromises that could undermine even well-aligned models.
Live deployments introduce dynamic risks absent in offline testing—real user interactions, evolving contexts, and continuous operation.
This phase involves monitoring production-like environments, stress-testing under sustained load, and analyzing logs for anomalous patterns. It often incorporates chaos engineering—intentionally injecting perturbations to observe resilience—and continuous adversarial probing.
Runtime analysis closes the loop, validating that protections endure in the face of real-world pressure and adaptation.
Isolated testing misses critical interactions across layers. A prompt that fails innocently at the model level might succeed catastrophically when combined with tool access and weak output parsing.
A layered strategy uncovers these interdependencies, enabling prioritized mitigations that strengthen the entire stack.
Hybrid execution combines manual adversarial creativity with automated scaling. Diverse teams—including domain experts, ethicists, and global perspectives—catch subtle cultural or contextual failures.
Iterative cycles drive continuous improvement: discover → analyze → mitigate → re-validate. Findings feed into guardrail updates, alignment retraining, infrastructure hardening, and monitoring rules.
A holistic GenAI red teaming approach, structured around model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis, provides the depth needed to confront modern threats. This method moves beyond surface-level checks to deliver verifiable resilience across the full system lifecycle. For a comprehensive overview of The Complete Guide to GenAI Red Teaming, refer to the blog The Complete Guide to GenAI Red Teaming: Securing Generative AI Against Emerging Risks in 2026.
