Inside the Novee Gym: Training, Benchmarking, and Controlling Offensive AI That Thinks Like an Attacker
Register NowInside the Novee Gym: Training, Benchmarking, and Controlling Offensive AI That Thinks Like an Attacker
Register NowMindgard secures your AI model well, but attackers rarely stop at the model – they move through every part of the application attack surface, where Mindgard doesn’t test and where real breaches actually happen.
Mindgard is strong at model-layer security, but testing the model in isolation means it never reaches the application around it – and doesn’t close the loop.
AI model layer only
Model-risk findings
Open loop
Undisclosed pricing
AI penetration testing is only as valuable as the risk it can actually reach. Model coverage matters, but so do the applications around it – and proof you can act on.
Covers what Mindgard can't
Validated findings
Tailored remediation
Context that compounds
Business-logic depth
Predictable pricing.
| Capability | Novee AI Pentesting | Mindgard |
|---|---|---|
| Approach | Builds a deep understanding of your application before testing. Maps roles, workflows, APIs, and business logic into an Asset Intelligence Model – so every test is grounded in how your application actually works, not just the model layer in isolation. |
Tests the AI model in isolation. No persistent model of how the application works, what roles exist, or how components relate. The whole application stays untested. |
| Depth | Finds high-impact business logic flaws, authorization gaps, and multi-step exploit chains. Reasons about how the application is supposed to work – not just what’s on a checklist. |
Tests model behavior – prompt injection, jailbreaks, model extraction, adversarial attacks. It doesn’t reason about application business logic, so authorization gaps and workflow abuse never get tested. |
| Scope | An Asset Intelligence Model (AIM) builds a living model of how your application is supposed to work – including API documentation, OpenAPI and Swagger specifications, natural language documentation, and source code (if provided). The system also crawls the application itself, opening multiple browser instances to interact with every discoverable endpoint and page. |
AI model and agent testing; no broad application coverage. |
| Context & Memory | Asset Intelligence Model compounds with every cycle. Coverage expands rather than resets – each run gets more targeted, covering roles, workflows, and business rules systematically. |
No compounding context across model test runs. Each run starts fresh, and nothing is carried forward. |
| Continuous Testing | Test on-demand or when changes are introduced, with coverage that deepens every cycle. |
Offers continuous AI-model monitoring. |
| Regression Testing | Automatic retesting to confirm fixes and prevent regressions. Tailored guidance specific to your stack, automatically confirmed. |
No closed-loop remediation, so no automatic verification that fixes hold. |
| False Positive Triage | Every finding validated with working exploit, replication steps, and a PoC script. |
Delivers model-risk findings, not working application exploits. |