Purpose
Mindgard is used to discover, assess, and harden the security risks of AI systems, covering AI models, AI Agents, and applications built on them. Its core approach is "attack to defend": first conduct reconnaissance and exploitation against AI systems like an attacker, then use that to verify and fix high-impact vulnerabilities.
Main Capabilities
- AI Vulnerability Discovery: In an autonomous red-team manner, use attacker-style reconnaissance and vulnerability intelligence to map and protect the AI attack surface.
- Continuous Assessment: Expose guardrail gaps, shorten security reviews from weeks to hours, and help maintain compliance.
- AI Agent Assessment and Security Scanning: Conduct security testing against Agents.
- AI-BOM and Shadow AI Risk Exposure: Identify asset inventories and risks of unauthorized AI use.
- Automated AI Infrastructure Crawling: Automatically discover AI infrastructure.
- AI Attack Surface Enumeration: Sort out entry points and exposed surfaces that can be attacked.
- Psychometric Agent Profiling: Perform profile analysis on Agents.
- Agent Profiling and Guardrail Breaking: Test and break through Agents' protective boundaries.
- AI Risk Compliance Reporting: Generate compliance reports.
- Runtime AI Protection and Response: Provide protection and response in production environments.
- Automated AI Agent Hardening: Automatically execute hardening operations.
- Defensive Auditing and Practical Hardening: Audit and strengthen defense systems.
- AI Reconnaissance and Attack Library / GRC: Provide resources and capabilities in directions such as AI security research, zero-day exploits, AI applications, AI infrastructure, and Agentic workflows.
How to Use
Mindgard can be used with your existing AI systems, Agents, and workflows. The platform provides APIs, CI/CD support, and can integrate with various enterprise AI workflows, covering production environments and infrastructure from open-source models to managed AI platforms. Specific installation commands, code examples, and detailed onboarding steps are not yet provided in the existing materials.
Applicable Scenarios
Suitable for organizations that need continuous mapping, red-team testing, guardrail verification, runtime protection, and compliance reporting for the attack surface of AI models, AI Agents, and their applications, especially suitable for embedding AI security assessment into CI/CD processes, and on multi-model platforms and self-developed infrastructureUnified governance of AI risk.
Limitations and Background
Mindgard originates from more than ten years of AI security research at Lancaster University in the UK, is headquartered in Boston and London, and its researchers have publicly discovered vulnerabilities in leading AI systems such as Grok, ChatGPT, and Google Antigravity, and it claims to have the world's largest AI security laboratory. The existing materials do not provide information such as specific pricing, free quotas, platform technical details, the list of supported models, and service levels.