Elite AI hackers aren’t born. They’re trained.
Elite AI hackers aren’t born. They’re trained.
Shinobi is built for developer pipelines, with core CI/CD integrations, but it runs single-pass validation and has no proprietary model. Novee proves exploitability with independent validation and deterministic checks, and compounds context every cycle.
Aikido’s new pentesting offering is real but shallow, focused on compliance and targeting developers, not security teams. It’s a mechanism to generate SOC 2/ISO 27001 PDF reports.
No proprietary offensive model.
Single-pass validation architecture.
No compounding context across runs.
Business logic depth is unproven.
Retesting, no stack-specific fixes.
Autonomous testing is only as valuable as what it proves, how confidently you can act, and whether the fix holds.
A proprietary offensive AI stack.
Validation built for zero false positives.
A closed loop to a verified fix.
Context that compounds every cycle.
Predictable per-asset pricing.
Coverage including AI apps
| Capability | Novee AI Pentesting | Shinobi |
|---|---|---|
| Offensive AI stack | Proprietary offensive reasoning model, post-trained on real attacker tradecraft and orchestrated using a proprietary harness with best-in-class frontier models selected per task (multi-model). Optimized with every recurring test cycle. |
No proprietary model. Relies on third-party models, so its offensive reasoning is capped by whatever those base models provide. |
| Validation architecture | Three independent agents (a finder, a validator, and a blind re-validator with no shared context) plus deterministic checks where possible. If any stage fails, the finding is never reported. |
Single-pass architecture, no independent confirmation between a false positive and your team. |
| Testing depth | Reasons through your application and chains business logic flaws into real attack paths, the way a human attacker would. |
Claims business logic coverage in blog posts, but with no proprietary model that depth is hard to prove. |
| Compounding application context | Builds an Asset Intelligence Model of roles, workflows, APIs, and business logic that deepens every cycle. |
Starts fresh on each assessment; context does not compound across runs. |
| Closed-loop remediation & retesting | Remediation tailored to your WAF, backend, and codebase, not generic OWASP. Automatic retesting confirms the fix held and flags new risk the change introduced. |
Retests fixes, but the remediation isn’t stack-specific. |
| Coverage | Web apps, APIs, mobile apps, and AI agents/LLMs across your external footprint |
Web, REST API, GraphQL, gRPC, and mobile (Android, iOS). No AI agent or LLM testing. |
| Continuous, change-triggered testing | Runs on demand or automatically when code ships via CI/CD. No scheduling, tokens, or human intervention. |
Runs continuously, triggered through CI/CD integration when code ships. |
| Workflow and CI/CD Integration | Native CI/CD and change-triggered workflows, plus Jira and GitHub. Fixes drop to the code level, aligned to your codebase. |
Broad CI/CD integrations |