Mindgard

Mindgard is an attacker-aligned AI security platform delivering continuous automated red teaming and security testing for AI models, agents, and applications. Acting as an autonomous red teamer, it emulates real adversary workflows end to end: reconnaissance (a March 2026 module discovers guardrails, system prompts, tools, integrations, and external services to map the production AI attack surface, including shadow AI), exploitation planning, and execution, testing complete AI systems rather than isolated models and chaining attacks the way adversaries do (prompt injection into unauthorized tool calls into system-prompt disclosure). The DAST-AI engine runs thousands of attack scenarios mapped to MITRE ATLAS and the OWASP Top 10 for LLMs against LLMs, agents, computer vision, audio, and multimodal targets, covering prompt injection, jailbreaks, model extraction, data poisoning, agent misuse, and unauthorized data access; the ATLAS Adviser standardizes red-team reporting and risk assessment. Runtime Threat Detection and Response extends testing into holistic real-time protection in production. Deployment is developer-native: a GitHub Action and CLI pull the latest attack techniques on every pipeline run, Burp Suite integration serves offensive practitioners, and testing requires only an inference or API endpoint. A services layer adds expert-led red teaming, an AI Security Expert subscription, technical account management, and AI security training (AI Academy). Spun out of Lancaster University on a decade of research with 11 PhDs on staff, SOC 2 Type II certified and GDPR compliant, Mindgard's research team has publicly demonstrated vulnerabilities in the Zed IDE and extracted Grok 4's system prompt. Within the lane it is the bridge between the offensive cluster and the AI-agent-security cluster: offensive tradecraft aimed at AI targets, with the most developer-native deployment surface of any red-team entrant.