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 ![Mindgard](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_14658941bb9d783a4f24d59a1925c11e/mindgard.png "Mindgard")
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# Mindgard Discussions

Mindgard is an automated AI red teaming and offensive security platform purpose-built for AI systems and agents. It is designed for security and engineering teams accountable for AI in development and production who need visibility into the security and safety risks in their AI applications, and a practical way to close them. At the core of the platform is automated AI red teaming. Mindgard emulates real attacker behavior against models, agents, and applications: reconnaissance, attack surface mapping, exploitation, and pivoting through the tools, data, and workflows an AI system connects to. Rather than testing a model in isolation, it evaluates the complete system in context, so findings reflect vulnerabilities with real security impact. Tests cover prompt injection, jailbreaks, data and model extraction, poisoning, evasion, and excessive agency in agentic workflows, across LLM applications, RAG pipelines, multi-step agents, and image models. Findings map to the OWASP LLM Top 10, MITRE ATLAS (the MITRE ATLAS Adviser covers 14 tactics and 56 techniques), the NIST AI Risk Management Framework, and the EU AI Act. Unlike traditional AppSec tools, manual penetration testing, or cyber-focused models applied to AI, Mindgard reduces AI risk assessment from weeks to hours, pinpoints and validates defensive gaps, and supports continuous policy compliance as AI systems change. Each finding is delivered with evidence, attacker context, and remediation guidance, and testing runs continuously so results stay current as prompts, models, tools, and agents evolve. Typical use cases include pre-release testing of an LLM application, validating guardrails before an agent goes to production, continuous testing after a model or prompt change, and producing evidence for compliance and governance reviews. Platform capabilities: AI Discovery & Recon (discover shadow AI and agents, build an AI-BOM, and map the AI attack surface); AI Red Teaming (continuous, automated adversarial testing of AI agents and systems); AI Assessment (find and fix AI security and safety vulnerabilities with prioritised remediation); AI Runtime Protection (real-time detection and response with context-driven guardrails); Model Scanning (AI artifact scanning); AI Governance & Compliance (risk measurement and reporting over time); and AI Academy (guided labs and red team training for in-house teams). Mindgard is available as a web platform with an API and an open-source CLI (pip install mindgard) that runs in MLOps and CI/CD pipelines with configurable risk thresholds. Built-in targets include OpenAI, Anthropic, and Hugging Face endpoints, plus custom JSON APIs. Mindgard was founded in 2022 from more than a decade of AI security research at Lancaster University, is headquartered in Boston and London, has publicly identified 150+ vulnerabilities in leading AI systems, and is SOC 2 Type 2 compliant.

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