Paper 2026/1175

AI-based KCMVP Pre-certification System: A Hybrid Model of Rule-based Detection and LLM Semantic Analysis

Abstract

The Korean Cryptographic Module Validation Program (KCMVP) is a national certification system that verifies the security and conformity of cryptographic modules deployed in government and public institutions. The current process typically takes about one and a half years, during which frequent supplement requests and the resulting retesting cycles substantially raise costs and delay schedules. To address this, we propose an AI-based pre-certification framework that combines rule-based deterministic detection (L1), RAG-based guideline-evidence retrieval (L2), and LLM-based final decision (L3) into a funnel-shaped pipeline that progressively reduces false positives. L1 applies more than 170 YAML inspection rules across four pattern types (missing, regex, semantic, ast) to perform deterministic detection. L2 retrieves and attaches KCMVP guideline evidence to each violation through multi-stage RAG search, and L3 employs Gemini 2.5 Flash-Lite to make context-aware decisions on false-positive candidates. In an initial evaluation on 128 Ground Truth cases derived from the KISA LEA code, the system detected all 128 cases, achieving 100% recall, while L3 correctly removed 9 of 46 FP candidates (19.6%) without inducing any false negatives (FN), confirming the stepwise refinement effect of the funnel structure. A blind verification on a certified commercial cryptographic module (~14.5 KLOC) yielded a low detection frequency of 0.58 cases per 1,000 lines of code, supporting the system’s practicality in real environments.