Elite AI hackers aren’t born. They’re trained.
Elite AI hackers aren’t born. They’re trained.
Tenzai has a strong harness and a fast runtime, but it stops at the finding: no stack-specific remediation, no automatic retesting, and no customer track record. Novee finds vulnerabilities that matter, proves every one is real, and verifies the fix held.
A strong harness and fast findings don’t close the loop in the same way a verified fix does.
No proprietary offensive model.
Stops at the finding.
Lighter validation.
Siloed from the tools devs use.
No external proof of value.
AI penetration testing is only as valuable as what it uncovers, how confidently you can act on it, and whether the fix actually held.
A proprietary offensive model.
Validation built for zero false positives.
A closed loop to a verified fix.
Built into your workflow.
Predictable per-asset pricing.
Proven with security teams.
| Capability | Novee AI Pentesting | Tenzai |
|---|---|---|
| Coverage | Web apps, APIs, mobile apps, and AI agents/LLMs across your external footprint. |
Web, API, and AI applications |
| Application context and depth | 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 what’s visible on the surface. |
Maps the target as actors, instructions, tools, credentials, guardrails, state transitions, and HTTP endpoints, with knowledge that compounds across runs. |
| Offensive model and harness | Proprietary harness and proprietary offensive reasoning model, post-trained on real attacker tradecraft and orchestrated with best-in-class frontier models selected per task (multi-model). |
No proprietary model. Optimizes an agent harness that orchestrates general models’ reasoning and execution; improvements target the harness and agents, not a model of its own. |
| Validation architecture | Three independent agents (a finder, a validator, and a blind re-validator with no context from the first two) plus deterministic checks where possible. If any stage fails, the finding is never reported. |
A single validator reproduces the finding; evidence includes the captured conversation, HTTP transcripts, and the validator’s reproduction. No second blind agent or deterministic confirmation described. |
| 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. |
Delivers PoCs and reproduction steps, but its materials don’t describe stack-specific remediation or automatic retesting. |
| Continuous, change-triggered testing | Runs on demand or automatically when code ships via CI/CD. No scheduling, tokens, or human intervention. Coverage deepens each cycle. |
Continuous, but no scheduling or change-triggered workflow to operationalize it. |
| Workflow & CI/CD integration | Native CI/CD and change-triggered workflows. Connected to CI/CD, fixes drop to the code level, aligned to your codebase. |
No CI/CD integration or developer-workflow integrations (e.g., GitHub, Jira). |
| Pricing | Predictable per-asset pricing. Depth and frequency don’t increase cost. |
No pricing publicly described. |