Mindgard

Automated AI Red Teaming & Security Testing

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Description

Mindgard provides an automated offensive security solution specifically designed for Artificial Intelligence systems. It addresses emerging threats and vulnerabilities unique to AI, which traditional application security tools often miss. The platform performs continuous security testing throughout the AI development lifecycle (SDLC), integrating into existing workflows like CI/CD pipelines and SIEM systems.

By simulating attacks through automated red teaming, Mindgard identifies runtime risks such as prompt injection, data leakage, evasion, and model inversion. It supports a wide array of AI models, including large language models (LLMs) like GPT and Claude, as well as image, audio, and multi-modal systems, regardless of whether they are developed in-house, purchased, or open-source. The goal is to deliver actionable security insights, ensuring AI deployments are robust and secure.

Key Features

Automated AI Red Teaming: Simulates attacks to proactively find vulnerabilities in AI systems.
Continuous Security Testing: Integrates into the AI SDLC for ongoing risk assessment.
Large AI/GenAI Attack Library: Utilizes thousands of unique attack scenarios based on PhD-led research.
Wide Model Compatibility: Supports LLMs (OpenAI, Claude, Bard), image, audio, multi-modal, and other neural network models.
Runtime Risk Detection: Identifies vulnerabilities like prompt injection and jailbreaking that appear during operation.
CI/CD and SIEM Integration: Seamlessly connects with existing development and security operations tools.

Use Cases

Securing generative AI applications against prompt injection and jailbreaking.
Validating the security posture of third-party AI models before deployment.
Integrating automated AI security testing into MLOps pipelines.
Performing continuous, automated red teaming exercises for AI systems.
Ensuring compliance and mitigating risks for AI deployments in regulated industries like finance and healthcare.
Identifying and resolving runtime-specific AI vulnerabilities.

Frequently Asked Questions

What makes Mindgard stand out from other AI security companies?

Founded in a leading UK university lab, Mindgard boasts over 10 years of rigorous research in AI security, with public and private partnerships that ensure access to the latest advancements and the most qualified talent in the field.

Can Mindgard handle different kinds of AI models?

Yes, Mindgard is neural network agnostic and supports a wide range of AI models, including Generative AI, LLMs, Natural Language Processing (NLP), audio, image, and multi-modal systems. This versatility allows it to address security concerns across various AI applications.

How does Mindgard ensure data security and privacy?

Mindgard follows industry best practices for secure software development and operation, including use of our own platform for testing AI components. We are GDPR compliant and expect ISO 27001 certification in early 2025.

Can Mindgard work with the LLMs I use today?

Absolutely. Mindgard is designed to secure AI, Generative AI, and LLMs, including popular models like ChatGPT. It enables continuous testing and minimisation of security threats to your AI models and applications, ensuring they operate securely.

What are the types of risks Mindgard uncovers?

Mindgard identifies various AI security risks, including: Jailbreaking, Extraction, Evasion, Inversion, Poisoning, and Prompt Injection.