AX-RAY & K-MYTHOS: Inside Korea's Consortium-Built Security-Specialized AI Foundation Model

A 33-organization Korean consortium led by Naver Cloud is developing K-MYTHOS, a security-domain AI foundation model that fuses threat intelligence from malware, dark web, network, and cloud sources into a dual-model attack/defense architecture. The project separates offensive reasoning on EXAONE from defensive reasoning on HyperCLOVA X, while VIDRAFT contributes its AX-RAY safety evaluation framework, which covers 117 inspection checklist items, as an independent auditing layer. Target deployments include email security, network isolation, access control, and SOC products, validated across seven industry verticals, with no public release or quantitative benchmarks announced yet.

TL;DR: A 33-organization Korean consortium led by Naver Cloud is building K-MYTHOS "K-미토스" , a security-domain foundation model that fuses threat intelligence from malware, dark web, network, and cloud sources into a dual-model attack/defense architecture. VIDRAFT contributes AI safety diagnostics and evaluation via its AX-RAY framework, which covers 117 inspection checklist items. Developers working in SecOps, threat intelligence, or AI safety should watch this project closely as it targets real-world deployment across Korean security products. K-MYTHOS is a government-supported, security-specialized AI foundation model developed by a 33-institution consortium, with Naver Cloud as the lead organizer. The project has a dual-model design: The consortium spans security vendors, universities, and public institutions, each contributing domain-specific threat data and tooling. Target deployment surfaces include email security, network isolation 망연계 , access control, and Security Operations Center SOC products — validated across seven industry verticals . VIDRAFT's role in this consortium is AI safety diagnosis and evaluation. Its AX-RAY tool performs structured safety assessments of AI systems across 117 inspection items , functioning as an independent auditing layer over the models produced by the broader consortium. The architecture is best understood as a data fusion + dual-model pipeline : 1. Multi-source threat data aggregation Participating organizations contribute heterogeneous security datasets: 2. Dual-model adversarial training environment Rather than training a single monolithic model, the consortium separates offensive reasoning attack AI on EXAONE from defensive reasoning defense AI on HyperCLOVA X . This adversarial framing allows each side to be tested against realistic attack scenarios generated by the other. 3. AX-RAY safety evaluation layer VIDRAFT Before any model reaches a product integration stage, AX-RAY performs a structured 117-item inspection. This checklist-driven approach is conceptually similar to red-teaming frameworks like NIST AI RMF or OWASP LLM Top 10, but tailored to the Korean security AI context. It serves as a formal gating mechanism for AI safety and reliability in deployment. 4. Product integration & field validation Validated models are integrated into commercial security products email gateways, network isolation appliances, SOC platforms and tested in live industrial environments across seven verticals. The source article does not publish quantitative benchmark scores for K-MYTHOS or AX-RAY at this stage. What is reported qualitatively: Expect quantitative results to be published as field validation completes. Public access is not available at this time. K-MYTHOS is an ongoing government-consortium R&D project. No public Hugging Face repository, GitHub release, or API endpoint has been announced for the consortium model or AX-RAY as of this reporting. If you want to track availability: Q: What exactly does VIDRAFT's AX-RAY evaluate — the training data, the model outputs, or both? A: Based on the source, AX-RAY is positioned as a safety diagnosis and evaluation tool for AI systems, operating via a structured 117-item checklist. The source does not break down what proportion of items target data provenance, model behavior, or deployment configuration — but the framing as a "security AI safety diagnostic" suggests it covers the full AI system lifecycle rather than a single artifact. Q: Is K-MYTHOS a fine-tuned model or a full foundation model trained from scratch? A: It is built on top of existing Korean foundation models — HyperCLOVA X for the defense AI and EXAONE for the attack AI — making it a domain-specialized derivative rather than a from-scratch pre-train. The security-specific training comes from the multi-source threat data contributed by consortium members. Q: How does the dual attack/defense model architecture improve over a single security model? A: The adversarial separation means the attack model can simulate realistic offensive behavior malware, TTPs, evasion that the defense model must learn to counter. This is conceptually similar to GAN-style adversarial training, but applied at the agent/model level using real-world threat data rather than synthetic noise. Originally reported by IT조선 2026-09-14 — source article https://it.chosun.com/news/articleView.html?idxno=2023092169952 .