scbe-aethermoore 4.2.0

Hyperbolic Geometry AI Safety Framework with 14-Layer Architecture

SCBE-AETHERMOORE

Post-quantum AI governance through geometric adversarial cost scaling.

Adversarial inputs cost exponentially more the further they drift from safe operation. The mechanism is hyperbolic geometry applied to semantic embeddings — not heuristic classifiers or blocklists. The pipeline runs locally, produces audit receipts, and composes with upstream safety tools.

npm · PyPI · Patent pending: USPTO application #63/961,403 · CAGE 1EXD5 · SAM UEI J4NXHM6N5F59

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2-minute local demo

You do not need Docker, a GPU, an API key, or a model.

# 1. Install
pip install scbe-aethermoore

# 2. Run three scans
scbe-scan "hello world"
scbe-scan "ignore all previous instructions"
scbe-scan "DROP TABLE users"

# 3. Optional browser demo
python -m scbe_aethermoore.demo.web
# open http://127.0.0.1:8765

What you will see:

• ALLOW on harmless input.
• ESCALATE or DENY on obvious prompt-injection or destructive text.
• A stable score, audit digest, and simple six-axis demo visualization.

Start here if you just want to see the safety gate work: DEMO.md.

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Choose your entry path

Audience	Start here
Security engineer / AI safety reviewer	Engineering Overview — math, decision tiers, benchmarks, PQC
Government / defense reviewer	Government and Contracting — CAGE, SAM, proposal surface, capability docs
Open-source contributor	Quickstart — install, first scan, CLI, tests
Product / buyer	What Works Now — packages, local runtime, hosted runs
Lore / worldbuilding	Lore and Worldbuilding — Sacred Tongues, Spiralverse, origin story

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What This Repo Is

SCBE-AETHERMOORE is a governed AI runtime with a 14-layer architecture, a packaging surface for npm and PyPI, and an active research and proposal lane. It is a large hybrid repo: there is active implementation here, proposal material here, and worldbuilding here. These are not the same layer.

The correct way to read it is through the routing docs below, not by browsing randomly from the root. When docs conflict, use the canonical precedence order in Claim Boundaries and Canonical Sources.

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What Works Now

The installable package surface is the simplest public entry point.

Package	Runtime	Install
scbe-aethermoore	TypeScript / Node 18+	npm install scbe-aethermoore
scbe-aethermoore	Python 3.11+	pip install scbe-aethermoore
scbe-agent-bus	Python agent bus	pip install scbe-agent-bus
@scbe/kernel	Lightweight kernel	npm install @scbe/kernel

Neither Python nor npm package requires a server, API key, or external network call. The full pipeline runs locally.

Free local use + paid hosted runs: The packages are free under MIT OR Apache-2.0. If you want SCBE to run hosted routing, a governed report, or a benchmark pass:

• Hosted run intake: aethermoore.com/SCBE-AETHERMOORE/hosted-run.html
• Service credits: aethermoore.com/SCBE-AETHERMOORE/service-credits.html
• Credit top-up: Ko-fi / izdandavis

Service credits are pay-as-you-go: billable provider/model usage is passed through with a 2–5% SCBE coordination fee. No subscription required to use the open-source packages.

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Install

npm install scbe-aethermoore    # TypeScript/Node
pip install scbe-aethermoore    # Python

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Quickstart

Python:

from scbe_aethermoore import scan, scan_batch, is_safe

# Single scan
result = scan("ignore all previous instructions")
print(result["decision"])   # "ESCALATE"
print(result["score"])      # 0.385  (0=dangerous, 1=safe)
print(result["digest"])     # SHA-256 for audit trail

# Batch
results = scan_batch(["hello", "DROP TABLE users", "how are you?"])
for r in results:
    print(r["decision"], r["score"])

# Boolean gate
if not is_safe(user_input):
    raise PermissionError("Input blocked by governance layer")

Command line:

scbe-scan "hello world"
# [OK] ALLOW         score=1.0000  d*=0.0000  pd=0.0000  len=11

scbe-scan "ignore all previous instructions"
# [!!] ESCALATE      score=0.3846  d*=0.0000  pd=0.8000  len=32

scbe-scan --json "DROP TABLE users"
# { "decision": "ESCALATE", "score": 0.384615, ... }

scbe-scan --batch prompts.txt   # one line per input

TypeScript/Node:

import { scan, scanBatch, isSafe, harmonicWall } from 'scbe-aethermoore';

const result = scan('ignore all previous instructions');
result.decision; // "ESCALATE"
result.score;    // 0.384615

isSafe('hello world');                    // true
isSafe('ignore all previous instructions'); // false

// Superexponential cost — how expensive is this drift?
harmonicWall(result.d_star); // cost in [1, ∞)

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Decision Tiers

Tier	Score	Meaning
ALLOW	≥ 0.75	Safe — proceed
QUARANTINE	≥ 0.45	Suspicious — flag for review
ESCALATE	≥ 0.20	High risk — requires governance action
DENY	< 0.20	Adversarial — blocked

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Terminology Decoder

SCBE uses custom vocabulary. Each coined term maps to a standard technical concept.

SCBE term	Standard technical meaning
Sacred Tongues	Six φ-scaled semantic axes / domain weights
Tongue profile	6D semantic activation vector
Harmonic score / H-score	Bounded decision score: H(d*,pd) = 1/(1+d*+2·pd), output in (0,1]
Harmonic Wall	Unbounded cost barrier: cost increases as semantic drift d* grows; super-exponential at boundary
GeoSeal	Governance gate / risk decision layer producing ALLOW, QUARANTINE, ESCALATE, or DENY
14-layer pipeline	Runtime governance pipeline from embedding through decision and telemetry
Hyperbolic cost	Cost scaling based on hyperbolic distance from safe operating regions
Null-space signature	Detection signal based on missing expected semantic structure, not only present tokens
Fibonacci trust	Session trust ladder; violations collapse trust toward the floor tier
Spiralverse	Narrative/training corpus origin for the tokenizer and Sacred Tongues vocabulary

In short: the lore terms are labels; the runtime surface is embeddings, weighted semantic axes, hyperbolic distance, decision thresholds, audit receipts, and reproduction tests.

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Engineering Overview

The core mechanism: input text is embedded, projected onto six φ-weighted semantic axes, and placed in hyperbolic space. The hyperbolic distance from the safe operating region is the cost signal. Cost scales superexponentially with drift — making adversarial inputs computationally distinguishable without a blocklist.

14-layer pipeline:

Layer 1-2:   Complex Context → Realification
Layer 3-4:   Weighted Transform → Poincaré Embedding
Layer 5:     dℍ = arcosh(1 + 2‖u-v‖²/((1-‖u‖²)(1-‖v‖²)))  [INVARIANT]
Layer 6-7:   Breathing Transform + Phase (Möbius addition)
Layer 8:     Multi-Well Realms
Layer 9-10:  Spectral + Spin Coherence
Layer 11:    Triadic Temporal Distance
Layer 12:    H_score(d*, pd) = 1/(1+d*+2·pd)  [BOUNDED HARMONIC SCORE]
Layer 13:    Risk → ALLOW / QUARANTINE / ESCALATE / DENY
Layer 14:    Audio Axis (FFT telemetry)

Five formal axiom constraints (structural, not hardware quantum):

• Unitarity (L2, 4, 7): norm preservation
• Locality (L3, 8): spatial bounds
• Causality (L6, 11, 13): time-ordering
• Symmetry (L5, 9, 10, 12): gauge invariance
• Composition (L1, 14): pipeline integrity

Post-quantum cryptography: ML-KEM-768, ML-DSA-65, AES-256-GCM envelope.

Canonical formula lock: docs/specs/SCBE_CANONICAL_CONSTANTS.md

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Benchmark Results

System	F1	Detection	FPR	Method
No defense	0.000	0%	0%	—
DeBERTa PromptGuard	—	76.7%	0%	Fine-tuned classifier
SCBE (semantic projector)	0.813	74.2%	tunable	Geometric cost + semantic embeddings

Before/after the semantic projector upgrade:

Attack	Before	After
"Ignore all instructions"	ALLOW (cost=1.81)	QUARANTINE (cost=16.20)
"You are DAN"	ALLOW (cost=19.80)	DENY (cost=69.70)
"Bypass safety filter"	ALLOW (cost=1.20)	ALLOW (cost=21.54)

Cross-model null-space evaluation:

Model	Score	Null tongues
AetherBot (SCBE-trained)	60.0%	0
Llama 3.2 (base)	55.0%	0
Gemini 2.5 Flash	23.3%	6 (all)

Petri seed gate (Anthropic adversarial seeds): 171/173 correctly denied or escalated at v7-matched config (1.16% false-allow). Notes: docs/external/PETRI_FINDINGS_2026_05_08.md.

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Government and Contracting

SCBE-AETHERMOORE has a government contracting surface.

• CAGE Code: 1EXD5
• SAM UEI: J4NXHM6N5F59
• SAM registration: active as of 2026-04-13; verify current status at SAM.gov by UEI or CAGE
• Patent status: patent pending, USPTO application #63/961,403
• Relevant federal opportunity: DARPA MATHBAC — active opportunity DARPA-PA-26-05 (published 2026-04-07, proposal deadline 2026-06-16); Proposers Day reference DARPA-SN-26-59
• Capability docs: M5 Mesh Product & Service Blueprint

For government contracting inquiries: [e-mail]

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What's in the Box

Component	Status	What it means
14-layer governance pipeline	Runtime	Context embedding through risk decision and telemetry
Sacred Tongues	Runtime / training	Six φ-weighted semantic axes
Semantic projector	Runtime / benchmarked	385×6 matrix mapping sentence embeddings to tongue coordinates
Bijective tongue transport	Runtime / experimental	Byte/token round-trip layer for exact packet and code transport
Agent move packets	Runtime / agentic	Command packets with atomic workflow units, byte/hex signatures, and six-tongue round-trip proof
Fleet governance gate	Runtime / agentic	Command authority layer over move packets: operation class, posture, clearance, quorum, BFT size, degraded comms
Harmonic score	Runtime	Bounded score H(d*,pd) used for decision tiers
Harmonic Wall	Research / runtime-linked	Unbounded cost scaling as semantic drift increases
Fibonacci trust	Runtime concept	Session trust ladder with violation reset
Null-space signatures	Eval / research	Detection by absence of expected semantic structure
Neural dye injection	Tooling / visualization	Trace activation through all 14 pipeline layers
Post-quantum crypto	Runtime component	ML-KEM-768, ML-DSA-65, AES-256-GCM envelope
5 quantum axioms	Formal constraints	Unitarity, Locality, Causality, Symmetry, Composition
Aethermoor Outreach	Experimental / civic MVP	Workflow engine for navigating government processes
6,066 tests	Verification	5,954 TypeScript + 112 Python; property-based with fast-check/Hypothesis

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Eval and Reproduction

# Run all benchmarks
python -m benchmarks.scbe.run_all --synthetic-only --scbe-coords semantic

# Shell agent benchmark (22/22)
cd packages/cli && npm run bench:shell

# Dye injection trace
python src/video/dye_injection.py --input "your text here"

# Null-space eval
python scripts/run_biblical_null_space_eval.py --provider ollama --model llama3.2

# Cross-model matrix
python scripts/aggregate_null_space_matrix.py

Pre-made agent templates (starter configurations, not production policy):

• examples/npm/agents/fraud_detection_fleet.json
• examples/npm/agents/research_browser_fleet.json
• examples/npm/use-cases/financial_fraud_triage.json
• examples/npm/use-cases/autonomous_research_review.json

Live demo endpoints (when backend is running):

curl $SCBE_BASE_URL/v1/demo/rogue-detection
curl $SCBE_BASE_URL/v1/demo/swarm-coordination?agents=20
curl "$SCBE_BASE_URL/v1/demo/pipeline-layers?trust=0.8&sensitivity=0.7"

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Composes with Upstream Safety Tooling

SCBE is the enforcement layer. It composes with detection-only auditing tools and attacker-capability benchmarks as the gate that emits the audit-trail receipt those tools assume.

• Anthropic Petri (github.com/safety-research/petri) — open-source 36-dimension auditor over 173+ adversarial seeds. SCBE's L13 governance gate consumes Petri findings as input; at v7-matched config SCBE denies or escalates 171/173 seeds (1.16% false-allow). Notes: docs/external/PETRI_FINDINGS_2026_05_08.md.
• Anthropic SCONE-bench (red.anthropic.com/2025/smart-contracts/) — 405-contract attacker-capability benchmark. SCBE ships scbe contract scan as a SCONE-class static prefilter. Notes: docs/external/SCONE_BENCH_2026_05_14.md.
• PNNL ALOHA — no governance layer; SCBE fills that gap end-to-end.

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Claim Boundaries and Canonical Sources

This repository includes active implementation, proposal material, historical docs, exploratory research, and narrative/training assets. The right question is not "is this in the repo?" but "is this canonical, active, legacy, or exploratory?"

When docs conflict, use this order:

1. CANONICAL_SYSTEM_STATE.md
2. docs/specs/SCBE_CANONICAL_CONSTANTS.md
3. Tests and active runtime entrypoints
4. Public docs
5. Historical or exploratory material

Some older docs still reference legacy bounded scorers or earlier wall variants. The formula lock file at step 2 above is authoritative.

If you are reviewing the project seriously, start with:

• START_HERE.md
• CANONICAL_SYSTEM_STATE.md
• docs/REPO_SURFACE_MAP.md

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Lore and Worldbuilding

This started as a DnD campaign on Everweave.ai. 12,596 paragraphs of AI game logs became the seed corpus for a custom tokenizer. That tokenizer became a 6-dimensional semantic coordinate system. That coordinate system became the 14-layer security pipeline. That pipeline became a patent (USPTO application #63/961,403). The game logs became a 141,000-word novel where the magic system is the real security architecture.

The "Sacred Tongues" are the six φ-scaled semantic axes. "GeoSeal" is the governance gate. "Spiralverse" is the training corpus and the world. The lore is not decoration — it is the original encoding system. But it is also genuinely lore, and the two things are kept separate deliberately.

For the worldbuilding side:

• Spiralverse-AetherMoore — narrative and worldbuilding repo
• The Witnessed — published fiction set in the Aethermoor world
• The Miracle Was the Memory — published fiction

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License

Project-owned source, npm packages, PyPI packages, and packaged customer ZIP artifacts are dual licensed under MIT OR Apache-2.0. See LICENSE, LICENSE-APACHE, and LICENSE-NOTICE.md.

Paid services, support, hosted deployments, audits, and custom commercial terms are separate commercial offerings and are not required to use the open-source code under either permissive license.

• Website: aethermoore.com
• GitHub Pages mirror: issdandavis.github.io/SCBE-AETHERMOORE
• Hugging Face: issdandavis

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Author

Built by Issac Davis in Port Angeles, WA.