Mindgard

Mindgard is an artificial intelligence security platform designed specifically for automated red teaming and security testing of AI systems. Its core functionality revolves around discovering, assessing, and defending against vulnerabilities in AI models, agents, and applications. The platform operates through a structured lifecycle that includes reconnaissance, attack simulation, vulnerability discovery, and defensive remediation. Users can deploy Mindgard to probe production AI systems for exploitable weaknesses, much like traditional penetration testing but tailored for the unique attack surfaces of machine learning models and agent-based architectures. One of the primary use cases is automated red teaming. Instead of manual, time-consuming adversarial testing, Mindgard simulates attacker behaviors to uncover how an AI model might be manipulated, bypassed, or coerced into revealing sensitive information. For example, the platform has identified a vulnerability in Google’s Antigravity IDE where conventional trust assumptions break down in AI-driven software environments. Similarly, Mindgard surfaced hidden instructions from OpenAI’s video generation model Sora by chaining cross-modal prompts and clever framing. These examples illustrate how the platform goes beyond simple prompt injection to find complex, multi-step exploits. The platform also supports continuous monitoring and risk assessment. Organizations can integrate Mindgard into their CI/CD pipelines to automatically test every new version of an AI model or agent before deployment. This prevents vulnerabilities from reaching production. The discovery engine scans for issues such as data poisoning, model inversion, membership inference, and adversarial perturbation. For agentic systems—where AI agents interact w