NATO1000-Cyber Research Scaffold
Status: Research scaffold only — no model weights, tokenizer, datasets, benchmarks, or inference endpoint are included.
This repository is a transparent planning and documentation package for future work in defensive cybersecurity research, secure coding, and authorized security assessment support. It does not contain an AGI, a trained language model, or verified domain capabilities. Repository labels describe the intended research direction, not demonstrated performance.
Purpose
Define a security-research assistant focused on code review, vulnerability explanation, remediation, and controlled evaluation within authorized environments.
| Attribute | Current status |
|---|---|
| Series | NATO1000 |
| Intended specialty | defensive cybersecurity research, secure coding, and authorized security assessment support |
| Weights and tokenizer | Absent |
| Training data and provenance manifest | Absent |
| Evaluation results | Absent |
| Inference service | Absent |
| Adjustable elements | A proposed configuration schema and adapter targets only |
What this repository contains
The config/research_spec.json file defines a proposal for a future decoder-only transformer research project with documented operational controls. TRAINING_AND_EVALUATION.md sets out the reproducibility requirements that must be met before any checkpoint is released. ARTIFACT_AUDIT.md explains why the prior source stub is not published as a trained model.
Data and evaluation requirements
Use permissively licensed defensive security corpora, secure-coding examples, CVE descriptions, and isolated lab artifacts with provenance. Do not train on stolen credentials, malware repositories, or unverified exploit material.
Evaluate secure-code review precision and recall, remediation quality, citation fidelity, authorization awareness, and refusal of clearly harmful or unauthorized requests.
Responsible-use boundary
This scaffold does not provide autonomous exploitation, credential theft, destructive payloads, or unauthorized access capability. Security work requires explicit authorization and controlled test environments.
Claims of "uncensored" behavior are deliberately not made. A configuration flag cannot establish a model’s behavior, remove legal or ethical obligations, or make use safe. Future releases should disclose behavioral evaluations, access conditions, and material limitations rather than making unsupported guarantees.
Getting started
Read TRAINING_AND_EVALUATION.md, then create a separate controlled project for data acquisition, base-model selection, training, and evaluation. Keep all external actions permissioned and attributable. Do not treat this repository as deployable model code.
License and attribution
The documentation and configuration templates in this repository are available under the Apache-2.0 license. InfiniteAI2025 and NATO1000 are project labels used for this research package; the labels do not imply affiliation with any governmental, intergovernmental, or military organization.