Q-Trust CodeBERT — crypto-usage discovery classifier
Binary code classifier that answers "does this code use cryptography, and which
primitive?" — the discovery layer of the Q-Trust post-quantum migration
project (humoge7502/q-trust on GitHub). Fine-tuned from
huggingface/CodeBERTa-small-v1 on 12,462 real files (6,636 crypto-labeled,
34 repos, deterministic seed-42 split, repo-disjoint held-out set).
Measured results (never estimated)
| Metric | Value |
|---|---|
| Precision / Recall / F1 | 0.952 / 0.953 / 0.9525 (n=2,415) |
| Training | 4 epochs, CUDA, scripts/train_qtrust_all.py --real --hf-epochs 3 |
| Leakage audit | Split reproduced bit-identically; 0.4% cross-repo dupes, bounded below the +0.11 F1 gain |
Try it in the widget above, or in code:
from transformers import pipeline
clf = pipeline("text-classification", model="KRISHNAPURI/q-trust-codebert")
clf("EVP_PKEY_assign_RSA(pkey, rsa);")
# [{'label': 'LABEL_1', 'score': 0.99}] # LABEL_1 = crypto usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tok = AutoTokenizer.from_pretrained("KRISHNAPURI/q-trust-codebert")
model = AutoModelForSequenceClassification.from_pretrained("KRISHNAPURI/q-trust-codebert")
Training data: KRISHNAPURI/q-trust-datasets
(qtrust_ai/artifacts/real_datasets/code_corpus.json). Full lineage
(config + seed + metrics): reports/training_report_real.json in
KRISHNAPURI/q-trust-codebert
sibling files, and qtrust_ai/artifacts/benchmark_comparison.json in the
GitHub repo.
Scope and limits
This model finds crypto usage (recall 0.877 on CryptoAPI-Bench files); it is
not a misuse detector and makes no SOTA claim. Sibling artifacts in this repo:
GNN ranker (honest tie with the heuristic, τ-b 0.7168 vs 0.7210), RL agent
(tie), side-channel detector (54 real trace sets). See REPORTS and the
per-artifact notes in the repo card below.