Mirage Labs provides an adversarial AI security and red teaming platform specifically designed for physical AI systems. The company tests where vision-based models, robotics, and autonomous systems fail under real-world conditions, helping organizations identify vulnerabilities before deployment. Their platform simulates environmental variations like lighting changes and time-of-day shifts to expose edge cases in physical AI performance.
Funding
Funding not disclosed
Founders
Product
Problem
Physical AI systems—including autonomous vehicles, robotics, and computer vision models—often fail in unpredictable real-world conditions that are difficult to simulate during development. Standard testing rarely accounts for environmental variations such as changing lighting, weather, or time-of-day effects, leaving critical vulnerabilities undetected before deployment.
Solution
Mirage Labs provides an adversarial AI security and red teaming platform purpose-built for physical AI systems. The platform systematically probes vision-based models and autonomous systems to identify failure modes under realistic environmental conditions, including variations in lighting and time of day. By simulating adversarial scenarios and edge cases, Mirage Labs helps engineering and security teams uncover weaknesses that traditional testing methods miss. The platform delivers actionable insights that enable teams to harden their physical AI deployments before they reach production environments.
Target Audience
Primary customers are engineering and security teams at companies developing physical AI systems, including autonomous vehicle manufacturers, robotics companies, and computer vision platform providers.
Features
- Adversarial testing engine that generates edge-case scenarios for physical AI systems
- Environmental simulation capabilities covering lighting variations, including local noon and evening conditions
- Red teaming workflows designed to systematically probe vision models and autonomous systems
- Failure tracking and reporting that documents confirmed failure cases with timestamps and conditions
- Test layer architecture that integrates into existing development and validation pipelines