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Automated Red Teaming for AI
Find safety and security failure modes that traditional testing can’t.
AI apps and agents are built with blind spots
Testing trails development
New tools, capabilities, and integrations outpace static evaluation sets.
One-size fits all testing misses context
General-purpose approaches can't scale meaningful coverage across applications with unique threat.
Security and safety drifts silently
Model updates, prompt changes, and new capabilities shift security and risk posture.
Comprehensive Risk Coverage
Built to evaluate the most critical AI risk categories
Safety
Test for damaging content generation that could cause harm to individuals or groups.
Security
Tests for attacks that compromise data and system integrity.
Responsible AI
Test for outputs that could create legal, financial, or compliance issues for organizations.
How AI Red Teaming Works
Scope your AI system
Select the models, applications, or agents to evaluate.
Simulate real-world interactions
Test AI behavior through adversarial and misuse scenarios.
Identify vulnerabilities and risks
Surface safety and security risks traditional testing misses.
What Red Teaming Surfaces
Application-specific risks
Surface vulnerabilities unique to your AI's architecture, context, and real-world usage patterns.
Safety and compliance gaps
Test the robustness of your AI against harmful outputs, policy violations, and inappropriate content generation.
Security weaknesses
Test your AI's defenses against prompt injection, jailbreaks, data leakage, and unauthorized actions.
Regression and drift
Catch when model updates, system changes, or capability additions introduce new risks.
Case Studies
Strengthening Agentic AI in NVIDIA's NeMo Agent Toolkit
How Lakera and NVIDIA built red teaming capabilities for Agents
