AI SecurityPenetration Testing & Attack Simulation
Mindgard Platform
Red teaming and runtime protection for AI models and agents, mapped to OWASP LLM Top 10 and ATLAS.
Mindgard Platform Overview
What it does
Mindgard is an AI security platform that tests and defends generative AI systems against adversarial attack. It operates as an autonomous red teamer, running attacker-style reconnaissance and exploitation against the models, agents, guardrails, and AI applications an organization builds or buys. The platform works across the full lifecycle, from discovering shadow AI and mapping the attack surface to validating exploitability and applying runtime protection, rather than scanning models in isolation.
How it works
Automated red teaming continuously maps, plans, and executes multi-step agentic attack workflows, emulating real adversary behavior across one-shot and multi-step interactions rather than single prompts. It evaluates complete AI systems, examining how agents, tools, connected services, and data sources interact, and surfaces high-impact vulnerabilities with attacker context and remediation guidance. Continuous assessment re-tests systems as models and configurations change. At runtime, context-driven guardrails harden system prompts and block prompt injection and agentic manipulation, with findings mapped to the MITRE ATLAS and OWASP LLM Top 10 frameworks.
Credentials and traction
Mindgard reports SOC 2 Type II compliance, indicated by an AICPA SOC 2 Type 2 badge on its site. Gartner named it a representative vendor for AI Security Testing in the 2025 Hype Cycle for Application Security. At the 2025 Cybersecurity Excellence Awards it won both Best Cybersecurity Startup and Best AI Security Solution. Its team has published more than 100 vulnerability disclosures across major AI and technology systems, anchoring its standing in the emerging AI red teaming market.
Key Capabilities
mapped to solution categoriesAutonomously plans and executes multi-step adversarial campaigns against AI systems, emulating real attacker workflows across reconnaissance, exploitation, and escalation rather than running a fixed checklist of tests.
Tests LLMs and AI applications against a library of direct and indirect prompt-injection and jailbreak techniques, reporting which payloads bypass system instructions and safety controls.
Attacks AI agents through their tools, memory, and connected services using multi-step techniques such as tool misuse, goal hijacking, and indirect injection, surfacing exploit paths unique to autonomous agents.
Discovers AI assets, including shadow models, agents, and inference endpoints, and maps the reachable attack surface to scope and target red-team campaigns. Offensive reconnaissance, distinct from posture inventory.
Re-runs red-team campaigns continuously and at release gates in the CI/CD pipeline as models, prompts, and configurations change, catching new exploit paths before and after deployment.
Reports validated AI vulnerabilities with reproduction evidence, attacker context, and remediation guidance, mapped to the OWASP LLM Top 10, MITRE ATLAS, EU AI Act, and NIST AI RMF for auditable AI risk reporting.
Attacks deployed guardrails, system prompts, and content filters to measure how reliably they block adversarial inputs, quantifying bypass rates rather than assuming the controls work.
Generates adversarial inputs across text, image, and audio modalities to test model evasion and misclassification, extending red teaming beyond text-only prompt attacks.
Routes high-value automated findings to specialist AI red teamers for manual exploitation, chaining, and depth beyond automated coverage, blending platform testing with human expertise.
Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.
Discovers AI model and inference endpoints and flags public exposure, weak authentication, default credentials, or excessive permissions as posture misconfigurations.
Compliance
certificationsIntegrations
compatible toolsImplementation & support
Info last updated on June 13, 2026
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