FROM THE CREATORS OF GANDALF

Expert-Led Adversarial Testing for AI Systems

Expert-led adversarial testing for LLM applications and agentic systems, delivered by the AI security researchers behind the world's most-played AI hacking game.

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Traditional testing wasn’t built for AI

Foundation models, retrieval pipelines, memory systems, and tool integrations introduce attack surfaces traditional application security testing was never designed to assess.

Our AI Red Teaming Services apply a dedicated adversarial methodology purpose-built for AI-native threats
Application-specific attack
paths
Attacker’s don’t run static datasets. They chain prompts, tools, and workflows to manipulate AI behavior
The Security Implication
Attackers may bypass safeguards, expose sensitive data, manipulate outputs, or influence downstream workflows in ways traditional testing cannot detect.
Unsafe agent behavior and tool misuse
Agents execute actions across connected systems. A manipulated decision can cascade into serious outcomes.
The Security Implication
Unsafe autonomy can lead to unauthorized actions, excessive permissions, tool misuse, and cascading failures across connected systems.
Human-validated security findings
Every engagement is led by AI security engineers who design, execute, and adapt attacks against your specific system, with advanced tooling accelerating their work.
The Security Implication
Every finding is reviewed by AI security specialists to reduce false positives and prioritize actionable remediation aligned to real business risk.

How our AI Red Teaming Service works

1

Scope the environment

Map AI architecture, workflows, tools, memory systems, and trust boundaries to build a targeted threat model and test plan.

2

Execute adversarial testing

Test with advanced prompt injection, jailbreaks, tool exploitation, and multi-turn attacks against real systems.

3

Deliver validated findings

Provide exploit validation, severity scoring, and remediation guidance.

Assessment coverage across the AI stack

Available for text, audio, image, and multi-modal systems.

LLM Applications

Prompt injection, RAG poisoning, insecure outputs, context manipulation, and multi-modal exploitation.

Agentic Systems

Goal hijacking, tool misuse, privilege escalation, indirect injection, and cascading failures.

Safety Controls

Jailbreaks, harmful outputs, policy bypass, unsafe responses, and responsible AI violations.

Organizational Risk

Business logic abuse, workflow manipulation, and architecture-specific attack paths unique to your environment.

Threat-informed testing methodology

Our attack methodology is continuously updated using proprietary threat intelligence and adversarial techniques derived from Gandalf — the world’s most-played AI hacking game with 85M+ prompts.

85M+
adversarial prompts analyzed

What your team receives

Executive risk summary
Clear insights for leadership with business impact.
Technical findings report
Validated vulnerabilities with reproduction steps, severity ratings, and affected workflows.
Remediation guidance
Actionable recommendations to improve security, guardrails, and operational controls.
Findings review session
Collaborative walkthrough to align on risks and remediation priorities.

Part of the AI Defense Platform

Before deployment
Identify weaknesses during development and testing.
Before release
Validate security controls, policies, and guardrails prior to production rollout.
Continuous improvement
Strengthen AI resilience as systems, models, and attack techniques evolve.

Case Studies

Strengthening Agentic AI in NVIDIA's NeMo Agent Toolkit

How Lakera and NVIDIA built red teaming capabilities for Agents

Learn more

Validate your AI systems against real-world attacks


AI Agent Security extends the AI Defense Plane to the agents organizations build and deploy, from discovery and governance to runtime protection.