Snapshot
Continue to About ↓- What it does
- Mindgard is an AI security platform that acts as an autonomous, attacker-aligned red team for AI models, agents, and applications.
More detail
- It maps AI attack surfaces, runs adaptive adversarial tests, validates which weaknesses are actually exploitable, and provides remediation guidance and continuous retesting as systems change.
- It is built for security and engineering teams accountable for AI systems in development and production.
- Best for
- Security and engineering teams — application security, product security, red teams, AI/ML engineering, and governance, risk, and compliance — responsible for AI systems in development and production.
More detail
- Especially useful for organizations that need AI risk visibility but lack specialist AI security staff in-house.
- Pricing
- Pricing is quote-based and not published: Mindgard states plans are scoped to the AI systems under test and the capabilities required, provided on request via demo or consultation.
More detail
- A free "Get Started" route and an open-source CLI for testing individual models exist, but the scope of the free tier i
- Main limitation
- Individual developers, hobbyists, or teams wanting a self-serve tool with published per-seat pricing, and organizations with no security workflow to integrate findings into.
More detail
- Teams seeking only runtime guardrails or firewalls — Mindgard tests whether such controls work rather than replacing them.
- Record
- Living and monitored
- Reviewed
- Evidence
- Public web research
- Machine access
- Structured profile ↗
About
- Mindgard is an AI security platform that acts as an autonomous, attacker-aligned red team for AI models, agents, and applications.
- It maps AI attack surfaces, runs adaptive adversarial tests, validates which weaknesses are actually exploitable, and provides remediation guidance and continuous retesting as systems change.
- It is built for security and engineering teams accountable for AI systems in development and production.
What it helps with
- Traditional AppSec and static code analysis cannot detect AI-specific risks such as prompt injection, jailbreaks, agenti
- Manual AI security assessment takes weeks and requires scarce specialists.
- Guardrails and firewalls are often deployed but unverified; teams need evidence of where controls hold, degrade, or fail
- AI systems change constantly, making one-off assessments stale and complicating continuous compliance evidence.
- Unmanaged or shadow AI assets and attack surfaces go undiscovered within products and infrastructure.
- Security teams need prioritization of exploitable vulnerabilities rather than large volumes of generic test results.
Capabilities
A practical view of what is available and where limitations apply.
| Offering | Available | Detail |
|---|---|---|
| AI discovery and reconnaissance | Yes | Agent-native recon maps models, agents, tools, instructions, behaviors, guardrails, system prompts, MCP/A2A servers, and shadow AI. |
| AI red teaming and adversarial testing | Yes | Adaptive, multi-turn, agentic attack chains including prompt injection, jailbreaks, system-prompt/data extraction, poisoning, and agentic tool abuse. |
| Risk validation and prioritization | Yes | Validates which weaknesses are actually exploitable and explains potential impact rather than producing generic results. |
| Remediation guidance and retesting | Yes | Provides evidence and guidance to close gaps, then retests to verify defenses remain effective. |
| Runtime protection and threat response | Yes | Listed platform capabilities include AI runtime protection, AI threat response, and automated agent hardening. |
| Governance and compliance reporting | Yes | Maps findings to frameworks such as OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, and the EU AI Act, with GRC evidence and executive reporting. |
| CI/CD and developer tool integrations | Yes | Deployable via APIs, CI/CD pipelines, Burp Suite extension, or single-click, with findings routed into existing security tooling and ticketing. |
| Command-line interface | Yes | Python-based CLI with recon and attack sub-commands; an open-source CLI is available for testing individual models. |
| Vulnerability intelligence database | Yes | Built on 150+ publicly disclosed AI vulnerabilities from Mindgard research feeding its proprietary attack knowledge base. |
| Published transparent pricing | No | Quote-based only; no dollar figures disclosed for enterprise tiers and free-tier scope not clearly quantified. |
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Evidence
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- Verification
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- The owner has not manually confirmed every field.
- Last reviewed
- Primary source
- mindgard.ai
Technical profile details
Record ID
5637dae4-6548-4660-b1a3-919037aa466cCanonical URLhttps://profiles.scribble.network/listing/mindgard-ai-security-platform-5637dae4