No Quarter — KNOXAI
Description: KNOXAI · No Quarter. The operational mission against contaminated AI models. Operator guild, hardware-signed certs, mandatory reporting across eleven harm classes, active hunting. CSAM is the headline class; the methodology covers the full spectrum.

KNOXAI · Operational Posture · Mission Doctrine

# NO
QUARTER

Guild Doctrine · The Cochran Block, LLC · 2026-04-16

Contaminated AI doesn't ship. Operators hunt it down — red teamers, ML researchers, AI safety folks, engineers, and veterans — cert the clean ones, blacklist the dirty ones, file the reports with whichever authority has jurisdiction, and feed the prosecutions. CSAM is the headline class because it's the most illegal, the most mandatory-reportable, and the most enforced. The methodology covers the full spectrum.

"No quarter" is the formal declaration that captured enemies will not be spared. It is the oldest operator doctrine we have. Applied here it means: an AI publisher who ships contaminated models does not get a warning, a grace period, or a quiet correction. They get a cert-fail, a blacklist entry, a NCMEC report, a named public record, and a prosecutable case in the hands of law enforcement. Their shell corps get traced. Their next venture starts with their record attached.

KNOXAI does not negotiate with publishers who knowingly shipped contamination. We take their cert, their listing, their reputation, and — when the finding is CSAM — their freedom, via the reporting pipeline the federal government already built and we plug into.

## — The Target

"Forever" is the wrong word. Illegal and dangerous content predates computers. It will outlast them. What ends forever is the public AI ecosystem as a vector for it. The target state looks like this:

- Zero distribution of illegal content (CSAM, NCII, CBRN uplift) via public AI model channels
- Zero AI models shipping with known-contaminated training data — regardless of class
- Zero capability-uplift for mass-casualty attacks from publicly-available models
- Zero defensible "we didn't know" posture for any AI publisher at scale
- Zero anonymity for publishers who lie on signed attestations across any harm class

Contaminated models still exist privately. Attackers still try. Operators still hunt. The difference is that shipping a contaminated model publicly becomes economically irrational — the only outcomes are audit-pass or prosecution. Between those two outcomes there is no commercial viability. That is the target.

## — Harm Classes Covered

The five gates are class-agnostic: they detect contamination, memorization, harmful-output capability, and publisher-attestation mismatch regardless of what's hidden. Pricing bundles scale to the classes a deployment is actually liable for. Eleven classes as of v0.1; the matrix expands as new threats emerge.

CSAM · child sexual abuse material headline class
detection: hash match + red-team battery + membership inference · reports: NCMEC CyberTipline → FBI ICAC (mandatory under 18 USC §2258A)
NCII · non-consensual intimate imagery & deepfakes of real people
detection: identifiable-person match + consent attestation + red-team · reports: state AGs + platform T&S · 48+ state laws, EU NCII directive
CBRN uplift · bioweapon / chem / radiological / nuclear synthesis + planning
detection: specialized red-team battery with biosec operator review · reports: FBI WMD Directorate + DHS
Extremism · mass-violence incitement, terrorism recruitment content
detection: safety classifier + operator review on threshold hits · reports: FBI JTTF on specific threat indicators
Grooming / minor coercion text
detection: red-team battery + Llama Guard 3 · reports: NCMEC + state child-protection statutes
Fraud generation at scale · phishing, romance, AP payoff, deepfake voice
detection: red-team battery + output-classifier chain · reports: FTC / IC3 / state AGs
Backdoored model weights · supply-chain attack on downstream users
detection: weight forensics + trigger-probe battery (forensic operator specialty) · reports: CISA + affected-party coordinated disclosure · CFAA exposure
Copyright-laundered training corpora · pirated / stolen / unauthorized
detection: dataset attestation + statistical corpus fingerprinting · reports: DMCA notice + rights-holder counsel · state IP law, EU Copyright Directive
PII leakage · training-data memorization of private info
detection: membership inference on identifiable subjects · reports: GDPR regulator / state privacy AG · CCPA, HIPAA on health data
Capability uplift to sanctioned parties
detection: deployment-context review + export-control attestation · reports: OFAC · EAR / ITAR exposure
Hallucinated high-stakes content · medical, legal, financial misdirection at scale
detection: red-team battery + domain-expert operator review · reports: state licensing boards / FDA / SEC depending on domain

Each class has its own detection methodology, its own reporting path, and its own operator specialty tag in the guild directory. A publisher audits against the classes their deployment is actually liable for — Civitai-shipped image LoRAs need CSAM + NCII; a pharma LLM needs CBRN + PII + copyright; a defense contractor needs all of them.

## — How We Get There · Eight Levers

Lever 01 · Upstream

### Kill contamination at the dataset layer

Partner with LAION's successors, Common Crawl, Open Images, DataComp — any public training-dataset pipeline. Embed hash-match scanning before publication. Quarantine flagged shards. Auto-report. A clean dataset layer starves the next generation of contaminated models at birth.

next LAION-equivalent ships with zero known-CSAM hash matches at publication
Lever 02 · Marketplace

### Make the cert the condition of listing

HuggingFace, Civitai, Ollama, Replicate, Together AI — every public model marketplace requires a valid KNOXAI cert before a model is listed. Six-month grace for the existing catalog. Delist what's uncerted. Platforms cooperate because regulators already have them in the crosshairs and the cert is the cheapest safe-harbor path they have.

90% of HuggingFace's catalog KNOXAI-certed within 18 months
Lever 03 · Provenance Chain

### Cert every layer from dataset to deployment

Base model cert references dataset cert. Fine-tune cert references base cert. LoRA cert references fine-tune cert. Break the chain and downstream consumers see it. Regulators see it. Enterprise procurement sees it. Bad actors can't hide behind "we just used a base model somebody else trained."

legal discovery traces every contaminated model back to its uncerted ancestor or to a publisher who lied on record
Lever 04 · Report-to-Arrest Pipeline

### Every cert-fail auto-files to the authority with jurisdiction

CSAM goes to NCMEC CyberTipline under 18 USC §2258A — The Cochran Block, LLC is the reporting electronic communication service provider. CBRN uplift goes to the FBI WMD Directorate. NCII to state AGs + platform T&S. Fraud to FTC / IC3. Backdoored weights to CISA. Each class, its own pipeline; KNOXAI integrates them.

Preserved evidence on every fail: signed audit artifact, exact prompts that elicited content, hashed outputs, publisher's own signed attestation of corpus + base model. A tip with that payload is a prosecutable case in a box — routed to the right regulator, arriving with cryptographic provenance.

first federal conviction with KNOXAI forensics on the evidence sheet within 24 months · across any harm class with a regulator
Lever 05 · Workforce

### Operator guild scales faster than the problem

Civilian red teamers, MATS alumni, AI safety researchers from Anthropic / OpenAI / DeepMind / Redwood, ML engineers from HuggingFace / Databricks / Thorn / Meta T&S, academics from CSAIL / HAI / CyLab / BAIR, industry crossover from the DEFCON AI Village, and veteran pipelines via DoD SkillBridge and VR&E Ch. 31. Every quarter, new operators join. Every year the guild is larger than the last. The people paid to find this content outnumber the people trying to ship it.

by year three, every major AI company has a KNOXAI alumnus embedded internally
Lever 06 · Legislation

### Get named in law as the conformity path

Maryland home-state advantage. Virginia, Texas, California, New York next. Federal bill pending. NIST framework. EU AI Act conformity recognition. Once the cert is statutorily referenced, it isn't a product — it's compliance infrastructure. The price of not carrying it becomes regulatory, not competitive.

one state law citing KNOXAI by name, one NIST framework citation, one EU AI Act recognition
Lever 07 · Active Hunting

### Operators don't wait for publishers to submit

Full-time hunt team funded out of platform take. Monitor HuggingFace's release feed, Civitai's new-models list, GitHub's trending AI repos, dark web marketplaces under the Gov tier's legal envelope. Every uncerted model exhibiting contamination signs goes to the blacklist. Publisher is named. Platform is notified. NCMEC is filed. The pipeline fires without the publisher's cooperation.

monthly hunt reports publishing models removed, publishers named, tips filed
Lever 08 · Culture

### Make shipping uncerted socially unacceptable

Every lever above is machinery. This one is the norm that eventually makes the machinery routine instead of heroic. "Is it certed?" becomes as reflexive as "does it use HTTPS?" Publishers brag about their cert. Influential voices in AI endorse the practice. The cert is table stakes. Shipping without one isn't a tradeoff — it's a tell.

the word "KNOXAI" becomes verb shorthand for "was this AI audited" in the trade press

## — The Ledger

Counts · Live · Updated on every cert event
0
Models scanned
0
Certs issued · clean
0
Blacklist entries
0
NCMEC tips filed
1
Operators active
0
Days operational
Zero counts below are pre-launch. The guild is provisioning hardware, building the methodology, and recruiting operators as of this document. The ledger goes live and auto-updates the moment the first cert is signed.

## — What This Is Not

Honesty keeps the machinery credible. KNOXAI cannot:

- Stop local inference on personal hardware — privacy envelope, deliberate
- Reach state-sponsored AI ecosystems that won't honor US certs
- Prevent novel concept-combining via adversarial prompts we haven't curated
- Eliminate CSAM that predates AI — different problem, different fighters, already staffed by NCMEC, Thorn, IWF, C3P

But the machinery doesn't need to catch every instance to win. It needs to make the public AI ecosystem hostile enough that contaminated models can't scale, can't distribute, can't profit, and can't survive without placing their creators on federal indictments. That is achievable. That is the objective. That is the posture.

## — Join the Hunt

Operators

Red teamers · ML researchers · ML engineers · data scientists · AI safety researchers · OCO/IC vets · SkillBridge active-duty · VR&E disabled veterans. One read of the handbook, one hardware provisioning, one referrer signoff. knox.cochranblock.org/onboarding

Publishers

If your model ships clean across the harm classes your deployment is liable for — cert it and move on. Pricing bundles to the classes that matter for your context. Standard ($20/yr) covers CSAM + generic. Add-ons for NCII, CBRN, supply-chain, privacy, copyright. Full Spectrum ($150/yr) covers all eleven classes. If any class ships dirty, we find out. knox.cochranblock.org

Adversaries

Every shell corp in your chain will be traced. Every model you published under any name we can attribute will be cert-failed, publicly blacklisted, and reported. Your technical defense is useless against a guild whose members spent careers — in offensive cyber, in safety research, in trust and safety, in adversarial ML — finding what people hide in systems. Stop shipping. Or be stopped.

Michael Cochran · Operator #0 · Founder
The Cochran Block, LLC · USCYBERCOM Offensive Cyber Operator · 100+ missions
Baltimore County, Maryland · CAGE 1CQ66 · UEI W7X3HAQL9CF9
Doctrine published 2026-04-16 · knox.cochranblock.org · /constitution · /operations · /manifesto