Mindgard
Claim this toolAutomated AI red teaming to find and fix security risks
Paid plans only Visit Website Paid Visit Website
Tracked since 2026
The Bottom Line
- Price
- Paid plans only. Compare plans
Key facts
- Automates AI red teaming and security testing to find vulnerabilities.
- Provides continuous analysis and runtime protection for AI systems.
- Maps and secures the AI attack surface using attacker-style reconnaissance.
Pros
- Profiles AI systems like attackers do, mapping models, agents, and tools for higher-impact vulnerability detection.
- Surfaces exploitable AI security flaws with real exposure, not just low-impact findings.
- Integrates extensive advanced AI security research and expertise directly into the platform.
- Deploys rapidly through CI/CD, Burp Suite, or single-click options, providing actionable insights without requiring specialist AI security expertise.
- Compatible with a wide range of AI systems, agents, models, guardrails, and applications, securing both open-source and managed AI platforms.
Cons
- Requires a demo to understand specific pricing and deployment details.
- Focuses heavily on offensive security, which might require a shift in mindset for some organizations primarily focused on defensive strategies.
What is Mindgard?
Mindgard is an AI security platform that provides automated AI red teaming and security testing to discover, assess, and defend AI systems and agents. It acts as an autonomous red teamer, using attacker-style reconnaissance to reveal how adversaries exploit AI agents and systems, exposing safety and risk implications. The platform offers continuous analysis and runtime protection to help organizations find, fix, and stop attacks before they cause real-world impact.
Mindgard is designed for enterprises integrating AI technologies into production environments, offering solutions to secure AI models, agents, and applications across the entire AI lifecycle. It provides visibility and protection against probabilistic and opaque AI behaviors that introduce exploitable risks, helping organizations understand exposure, assess exploitability, and reduce risk from evolving, agentic AI threats. The platform is built on extensive AI security research, embedding offensive and defensive expertise directly into its capabilities.
Key Features
- AI Agent Evaluation & Security Scanning
- Automated AI Infrastructure Crawling
- AI Attack Surface Enumeration
- Psychometric Agent Profiling
- AI Agent Fingerprinting & Bypassing
- AI Red Teaming
- Agent Security Testing
- AI Security Risk Compliance Reporting
Pricing
Paid
Mindgard offers paid plans. Visit their website for current pricing details.
Mindgard by the numbers
| Metric | Mindgard | Peer median | Position (1 is best) |
|---|---|---|---|
| Free plan | No | 74% have one | n/a |
| Listed as an alternative to | 8 tools | 2 | #72 of 393 (joint) |
| Compare-page visits, last 30 days | 33 visits | 32 | #50 of 112 (joint) tools with 20+ compare-page visits |
Listed as an alternative to: #77 on 2026-10-16 to #72 on 2026-11-02 (rank among 393 tools, 1 is best).
Each position is counted among the tools that have data for that row, as noted under it. All Testing & QA data · How we compute this. Advertising and placement fees are not inputs to any position. Visit counts include visits from every source.
Cite this: Toolradar, “Mindgard by the numbers”, Nov 2026.
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Mindgard FAQ
How does Mindgard's agent-native reconnaissance differ from other AI security approaches?
Mindgard's agent-native reconnaissance profiles AI systems by mapping models, agents, tools, and behaviors in the same way attackers would, prior to attack execution. This method is designed to reveal higher-impact vulnerabilities more quickly than broader, prompt-heavy security approaches.
Can Mindgard identify vulnerabilities in leading AI models and platforms?
Yes, Mindgard has a track record of identifying vulnerabilities in leading AI systems and platforms, with over 100 public disclosures across major AI and big tech systems, including those from prominent AI developers.
What kind of AI security risks does Mindgard help mitigate beyond just model vulnerabilities?
Mindgard addresses AI security as a system problem, mitigating risks across models, prompts, system instructions, agents, tools, applications, APIs, and data flows. It focuses on the entire AI attack surface, recognizing that attackers exploit the surrounding systems, not just models or agents in isolation.
How quickly can an organization deploy Mindgard and start receiving actionable AI security insights?
Organizations can deploy Mindgard in minutes through various methods, including CI/CD pipelines, Burp Suite, or a single-click deployment. This rapid deployment provides actionable AI security insights without requiring in-house specialist AI security expertise.
Does Mindgard offer solutions for AI governance and compliance reporting?
Yes, Mindgard includes features for AI Security Risk Compliance Reporting and AI Governance & Compliance, helping organizations meet regulatory requirements and maintain oversight of their AI systems' security posture.
How does Mindgard ensure that the identified risks are truly exploitable and not just noise?
Mindgard focuses on detecting exploitable risks by surfacing AI security flaws that create real exposure. Its methodology, born from extensive AI security research, is designed to identify high-impact vulnerabilities rather than generating low-impact findings or false positives.
What is the role of the AI Recon & Attack Library within the Mindgard platform?
The AI Recon & Attack Library is a core component that provides access to AI security research and zero-day exploits. This library informs Mindgard's autonomous red teaming capabilities, enabling it to simulate sophisticated attacker techniques and discover novel vulnerabilities.
Source: mindgard.ai