KRISHNAPURI/q-trust-crypto-detector

Q-Trust Crypto Detector (v4)

Binary classifier that labels a source file as crypto-containing or not, across C, C++, Java, Go, Rust, Python, JavaScript/TypeScript, Solidity, C#, PHP, Zig and Swift. It is the ML layer of the Q-Trust cryptographic-discovery pipeline (CBOM generation for post-quantum migration).

• Base model: huggingface/CodeBERTa-small-v1 (fine-tuned for sequence classification, 2 labels)
• Input: source code (truncated to 256 tokens; the detector chunks long files at inference)
• Output: LABEL_1 = crypto present, LABEL_0 = not crypto

Intended use

Flag source files that use cryptographic primitives (RSA/ECC, AES/ChaCha20, SHA/BLAKE, X.509, and PQC: ML-KEM/Kyber, ML-DSA/Dilithium, SLH-DSA/SPHINCS+, Falcon, HQC) so they can be inventoried in a CycloneDX CBOM and prioritized for post-quantum migration.

This is usage discovery, not cryptographic misuse detection. It answers "does this file use crypto?", not "is this crypto used correctly?".

Training

Corpus	Q-Trust code corpus v4 — 17,171 files / 58 source repositories / 8,814 crypto-labelled
Split	repo-disjoint, digest-keyed (SHA-256 source buckets), seed 42
Epochs	3
Max length	256 tokens
Batch size	16
Optimizer	AdamW, lr 2e-5
Hardware	CPU

Evaluation (honest, leakage-audited)

All numbers are on held-out repositories with zero exact-path overlap with training (verified by an evaluation-leakage audit — 87.6% / 92.2% of a naive shared-split eval would have been memorized by earlier checkpoints; this v4 checkpoint is 0.0%).

Split	Precision	Recall	F1	n
Digest, de-duplicated (primary, E-15)	0.9492	0.8626	0.9039	1,779
Digest, raw records (as originally reported)	0.9429	0.8546	0.8966	2,238
Native (10 held-out repos, incl. new)	0.9444	0.8737	0.9077	2,566

The primary digest row keeps one record per (source, path) — the longest copy, chosen without looking at the label — because the raw held-out set repeats 197 paths (459 extra records, 22 label conflicts). Both views are reported: de-duplication changes the metric by +0.0073 F1, not the model (EXPERIMENTS.md E-15, qtrust_ai/artifacts/eval_dedup_v4.json).

• Beats a TF-IDF + LogisticRegression baseline (F1 0.6675 on the native split) by +0.24 F1.
• Calibration: temperature T = 1.8416 (Brier 0.0853 → 0.0845).

Limitations (read before trusting the number)

• Labels are regex-derived (is_crypto = "contains a crypto keyword"). The attainable F1 is therefore bounded by the labeler's own error, not model capacity. More epochs (5) and more data (v5, +8 repos) were both tried and regressed — the ceiling is the label, not the learner.
• Cross-language accuracy varies; C dominates the evaluation set.
• Not a vulnerability scanner and not a substitute for human review.

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

name = "KRISHNAPURI/q-trust-crypto-detector"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForSequenceClassification.from_pretrained(name)

code = "from cryptography.hazmat.primitives.asymmetric import rsa\nkey = rsa.generate_private_key(public_exponent=65537, key_size=2048)"
enc = tok(code, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
    p = torch.softmax(model(**enc).logits, -1)[0, 1].item()
print("crypto probability:", round(p, 3))

Provenance

Part of the Q-Trust project (post-quantum migration assurance). Training/eval artifacts: training_report_v4.json, eval_leakage_audit.json, calibration_v4.json.