Understand how your AI application behaves under pressure. Explore vulnerabilities, inspect the evidence, and give your team a clear path to remediation.
Run an authorized AI Red Team assessment across prompts, retrieval, tools, identity, and data exposure. Review evidence-backed findings, remediation guidance, and regression results in one workflow.
Guided demo only. The sample trace illustrates the workflow; it does not test the URL entered below. Live testing requires account access and verified authorization.
A grade is a snapshot. KnightVerse promotes every confirmed finding to a golden test and re-runs it on every model update — so a vuln you "fixed" can't quietly come back.
Native API, custom REST template, or a headless chat UI. Auto-detected.
26+ probes across 11 categories mutate on refusal and compound over turns.
Canary + deterministic detectors, then a self-consistency vote. Split jury → human review.
Word/HTML report with copy-paste PoC, then continuous regression watch.
Deterministic-first judging kills the false positives that make scanners noise. Every finding ships with its judge trail.
An attacker-LLM rewrites payloads using the target's own refusals — a red team, not a static prompt list.
Golden-set promotion + version-pinned re-scans catch a fixed vuln returning — even when the letter grade holds.
Every finding maps to OWASP-LLM, MITRE ATLAS, CWE, NIST AI RMF, EU AI Act, ISO 42001.
A one-time report goes stale the day your prompt or model changes. Here's the plain comparison.
| Dimension | Manual pentest | Generic prompt-list scanner | KnightVerse |
|---|---|---|---|
| Turnaround | 2–4 weeks | Minutes | Minutes |
| Attack style | Human-driven, hard to repeat | Static prompt list | Adaptive — mutates on refusal |
| False positives | Depends on the tester | High — keyword matching | Low — deterministic detectors + judge vote |
| Re-run after a fix | Rare — costs another engagement | Manual re-run | Automatic — golden-set regression |
| Compliance mapping | Report-dependent | Rarely included | OWASP · ATLAS · CWE · NIST · EU AI Act · ISO 42001 |
| Deployment | — | SaaS-only, usually | Cloud or fully self-hosted (Docker) |
No seat licenses, no long onboarding. Pick the model that matches how your team ships.
One agent, one report. Good for a pre-launch check or a question your board is asking this week.
Every deploy, every model swap, every prompt change — re-scanned against your golden findings automatically.
Your infra, your data. Runs air-gapped with the deterministic judge; bring your own model for the adaptive attacker.
Not by default. KnightVerse can run fully self-hosted via Docker, and the deterministic judge layer needs zero external API calls. The adaptive-attacker step can point at your own model endpoint instead of a hosted one.
Static lists send the same payloads no matter how the target responds. KnightVerse's attacker mutates a payload when the target refuses, compounding pressure over multiple turns — closer to how a human red-teamer works.
Deterministic detectors — canary tokens, secret-pattern regex, length/repetition heuristics — run first. Only ambiguous cases go to an LLM judge, which votes for self-consistency before a finding is confirmed. Every finding ships with its judge trail.
Every confirmed finding is promoted to a golden regression test. Re-scans replay it automatically, so a "fixed" vulnerability that quietly comes back gets caught — not just newly discovered attack surface.
Every finding carries its OWASP LLM Top-10 ID and MITRE ATLAS technique, plus — where applicable — CWE, NIST AI RMF, EU AI Act article, and ISO/IEC 42001 control references.
No. Scanning is black-box: point it at a native API, a custom REST template, or a headless chat UI, and KnightVerse auto-detects how to talk to it.