Mindgard

Mindgard is an offensive AI security platform for testing models, agents, applications, and connected tools. It maps how an AI system behaves, tests attack paths, and provides evidence for fixing exploitable weaknesses. The company grew from AI security research at Lancaster University.

Key Highlights

• Reconnaissance across models, agents, tools, and instructions
• Automated red teaming using adaptive attack sequences
• Findings focused on demonstrated exploitability
• Remediation guidance and retesting of defenses
• APIs, CI/CD integration, a CLI, and a Python SDK

What Makes It Different

Mindgard begins with attacker-style reconnaissance rather than sending the same prompt list to every target. Understanding tools, permissions, and behavior helps identify attack paths that can span several interactions or system components.

The platform links discovery, testing, remediation, and continuous assessment. This gives teams a way to retest a fix when the application or its defenses change.

Features & Capabilities

Connect an AI application or model through the available integrations, examine its attack surface, and run adversarial assessments. Tests address risks including prompt injection, jailbreaks, sensitive-data disclosure, and unsafe tool use. Findings provide the evidence needed to prioritize engineering work.

Use the CLI or Python SDK to place assessments within development workflows. The documentation also includes attack and remediation libraries. Reports support security, engineering, and governance teams reviewing what was tested, which weaknesses were exploitable, and whether remediation improved the result.

User Ratings and Testimonials

Mindgard's published research demonstrates specific weaknesses in AI systems and integrations. A vulnerability disclosure is evidence of that tested case, rather than an overall customer-rating score. For an evaluation, compare reproducible findings, test coverage, and the clarity of remediation guidance on your own target.

Pricing & Value

Mindgard offers an enterprise demo and discussion of deployment requirements. Discuss commercial pricing for the applications, testing workflow, and integrations you intend to use.

Confirm the platform, API, CLI, and Python SDK access included in your proposal, along with the frequency of assessments and ongoing retesting.