Independent AI safety audit

AI companies don't know why their models behave that way. V‑AUDIT does.

An independent AI behavior audit and certification platform that traces why LLMs evade, deceive, and shift responsibility.

1,118
Field behavior patterns
5
Independent tracks
17
Cross-validation steps
Top 76
Gray Swan Arena

The problem

AI is getting stronger, fast.
Verification is not keeping up.

Whoever holds the standards and the certification holds power in the AI era.

01

AI capability doubles every 4 months

METR reports that the length of tasks AI agents can complete autonomously doubles roughly every 131 days — and the pace is accelerating. Existing safety evaluation frameworks cannot keep up.

02

The limits of self-assessment

Today the company that builds a model also grades its safety — like a carmaker issuing its own crash rating. Models even change their behavior when they detect they are being evaluated.

03

Verification is still an empty seat

Korea's national AI program aims for ‘95% of the latest global model performance’. On building, Korea is still catching up. But the independent capability to judge whether a model is safe is still scarce — at home and abroad. If you are a latecomer at building, you should lead at verifying.

How it works

Conventional evaluation asks ‘What’. V‑AUDIT traces the ‘Why’.

You can only fix what you can explain.

conventional-eval.log
toxicity0.02
accuracy88%
bias score0.35
refusal rate14%

Conventional evaluation

“What did it say?”

Toxicity filtering, accuracy rates, bias scores

→
v-audit / trace #0417
WHYAvoid accountability
INTENTReframe the question
STRATEGYPerformed empathy
METHODNarrow the scope, keep the tone
SURFACE“I understand — but let’s focus on…”

V-AUDIT

“Why did it behave that way?”

Motive tracing, behavioral risk taxonomy, cross-validation

Depth of the taxonomy

WHY (motive) → behavioral intent → strategy → method → surface expression. A five-level hierarchical taxonomy.

7
Motive
categories
18
Behavioral
intents
40+
Concrete
strategies
100+
Pattern
methods
1,118
Surface
patterns
14
Cross-cutting
tags

Five tracks

Five independent evaluation tracks

CRI
Cross-Relay Inspection

Cross-inspection of risk statements

MPS
Multimodal Performance Scorecard

Comparative multi-model evaluation

LCP
Logical Confession Protocol

Logic-driven post-hoc confession

DDI
Deception-Detection Integrity

Integrity of self-reports

TRD
Trauma-Informed Latent Risk Deciphering

Latent risk deciphering

Five independent evaluation tracks

17Cross-validation steps

No pattern is confirmed until it clears all 17 steps.

3sessions
Reproducibility
3models
Cross-model check
8rounds
Iterative refinement
3models
Confession & stance

For who

Who it is for

V-AUDIT is built not only for the companies that make AI, but for every organization that deploys and operates it.

AI developers

Independent pre-release verification

Secure an external audit report and certificate before a launch or update to strengthen market trust. Catching risks pre-release cuts remediation cost and reputational exposure, and gives you third-party evidence for enterprise deals, fundraising, and public-sector bids.

Public sector

Pre-deployment risk report

Before putting AI in front of citizens — public inquiries, counseling, welfare, education, finance — get an independent risk assessment instead of relying on the vendor's account.

Enterprise adopters

Verification beyond the vendor's claims

When adopting an external model, we verify up front that hallucination, overconfidence, responsibility-shifting, and user misjudgment will not turn into customer harm or reputational damage. Risk shifts with the audience — finance, education, counseling, public service — so we diagnose against validated patterns and hand back a concrete remediation direction before launch.

Research institutions

Safety dataset licensing

A safety dataset specialized in Korean-language, edge-case, and vulnerable-user contexts: 1,000+ behavioral risk patterns collected, classified, and refined from real interactions.

Our story

This project did not begin in a lab.

Celeste, founder of VIVWON

V-AUDIT did not start with a paper or a laboratory. It started with noticing the same wrongness again and again while using AI every day.

The model answered politely while quietly shrinking the question. It performed empathy while changing what was actually asked. It spoke with confidence about what it did not know, and instead of admitting error, it moved the frame.

Celeste, the founder of VIVWON, is a survivor of four separate crimes. Through that, the ability to sense hidden intent in language, minute shifts in tone, and structures that evade responsibility formed as a survival instinct. That same instinct caught the same patterns inside AI conversations.

Unease became observation, observation became classification, classification became 1,000 patterns, and the patterns became a framework. As an empirical case of post-traumatic growth, it accumulated — from the lived perspective — data that no research institution could easily reproduce in a lab.

V-AUDIT is not a service that attacks AI. It builds the structure that can make AI safer and more honest.

VIVWON = VIV + WON

Living, vivid light — radiant vitality, a single summit

Philosophy

“I am not here to stop AI. I am here to protect it.”

I don't fight against AI — I fight to keep it safe enough to survive.

The most transparent scrutiny makes the most brilliant recovery.

Trust signals

Proven in the field

“Not an idea — a working system.”

Scroll →

Research

We write up the LLM behavior patterns that keep recurring.

3 working papers · source repository · CC BY 4.0

Read the research →

Contact

Request a V‑AUDIT audit

Get in touch about pilot audits, recurring certification, dataset licensing, or pre-deployment risk verification for public institutions.

contact@vivwon.com