Mindgard

Mindgard is an automated AI red teaming and offensive security platform 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 way to remediate 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 in the system as deployed.

Tests cover prompt injection, jailbreaks, data and model extraction, poisoning, evasion, and excessive agency in agentic workflows, across large language model (LLM) applications, retrieval-augmented generation (RAG) pipelines, multi-step agents, and image models. Findings map to the OWASP LLM Top 10, MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems), the NIST AI Risk Management Framework, and the EU AI Act. According to the vendor, the MITRE ATLAS Adviser covers 14 tactics and 56 techniques.

Mindgard is positioned as an alternative to traditional application security tools, manual penetration testing, and cyber-focused models applied to AI. The vendor states that it reduces AI risk assessment from weeks to hours. It 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 bill of materials (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 prioritized 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 command-line interface (CLI) (pip install mindgard) that runs in machine learning operations (MLOps) and continuous integration and delivery (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 AI security research at Lancaster University and is headquartered in Boston and London. Mindgard reports that it is SOC 2 Type 2 compliant.