Run-Time Model Scanning

Run-Time Model Scanning

Mindgard scans AI models for both security vulnerabilities and safety risks, identifying exploitable behaviors, policy violations, and harmful outputs. By analyzing how models behave in real-world scenarios, it provides clear, actionable insight to strengthen both security posture and safe deployment.

Protect your AI models with continuous monitoring and advanced security testing. Mindgard’s Run-Time Model Scanning identifies vulnerabilities, analyzes risks, and integrates seamlessly into your workflows to keep your AI investments secure and compliant.

Connect Mindgard to your models for run-time scanning. The process supports a variety of frameworks and deployment environments.

Comprehensive tests on your AI model include adversarial attacks and configuration checks, to identify weaknesses in real-time. Schedule and run with just one click.

Get a detailed view of scenarios and threats to your AI. Aggregate and analyze findings, mapping risks to OWASP and MITRE ATLAS for actionable insights.

Integrate results into your existing systems for streamlined monitoring and incident response. Gain immediate visibility into your AI security posture.

Leverage Mindgard’s recommendations to remediate vulnerabilities and strengthen defenses, ensuring your AI system stays resilient and compliant.

• Offline AI Risk Profiling
• Run-Time Model Testing
• Continuous Threat Monitoring and Updates

Together, these processes ensure that both known and emerging threats are addressed, providing robust protection for your AI investments. Continuous monitoring ties everything together, enabling proactive threat detection and ongoing security assurance.

Find and remediate AI vulnerabilities only detectable at run time. Integrate into existing CI/CD automation and all SDLC stages.

Secure the AI systems you build, buy and use.

Extensive model coverage beyond LLMS, including image, audio and multi-modal.

Empower your team to identify AI risks that static code or manual testing cannot detect. Reduce testing times from months to minutes.

Whether you're just getting started with AI Security Testing or looking to deepen your expertise, our engaging content is here to support you every step of the way.

• Modern AI Red Teaming:
  Probabilities, Vulnerabilities, and Psychometrics — January 22, 2026

  A technical exploration of modern AI red teaming, examining how probabilistic behavior, classic vulnerabilities, and psychometric steering combine to create real-world AI security risk.

• Bringing AI Security into Your CI/CD with Mindgard — December 12, 2025

  Mindgard’s Git Hub Action example repository shows how to integrate automated AI security testing into CI/CD pipelines so every model or code change is validated against the latest Mindgard capabilities.

• Bypassing LLM guardrails: character and AML attacks in practice — December 10, 2025

  This study shows how simple character transformations and algorithmic evasion attacks can silently bypass six popular LLM guardrails, sometimes reaching one hundred percent evasion.

See how Mindgard exposes and fixes exploitable AI risk across your AI agents and systems.

Mindgard, the leading provider of AI security solutions, helps enterprises discover, assess, and defend their AI systems. Spun out from over a decade of AI security research at Lancaster University and headquartered in Boston and London, Mindgard combines AI red teaming with offensive security expertise and AI research to identify exploitable vulnerabilities in AI models, agents, and applications before attackers do.