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QuantumindSSI/
01-quantum-resistant-crypto-analyzer

Name: 01-quantum-resistant-crypto-analyzer
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Quantum-Resistant Cryptographic Protocol Analyzer

A fine-tuned Small Language Model (SLM) that analyzes cryptographic protocol implementations and identifies quantum-vulnerable patterns, attack vectors, and NIST-aligned mitigations.

Model Details

Attribute	Value
Developer	QuantumIndSSI Ltd
Base Model	Qwen/Qwen2-0.5B-Instruct
Architecture	Transformer decoder (causal LM)
Fine-tuning Method	LoRA (Low-Rank Adaptation)
LoRA Rank	16
LoRA Alpha	32
License	apache-2.0

Intended Use

Automated quantum vulnerability scanning of protocol implementations (TLS, SSH, VPN, etc.)
Security audit assistance for classical-to-PQC migration planning
Developer education on quantum cryptanalysis risks
Edge deployment on Victron and other constrained hardware

Training Data

10,500+ synthetic cryptographic protocol analyses
Protocols: TLS/SSL, SSH, IPsec, WireGuard, WPA, S/MIME, OpenPGP, DNSSEC, Kerberos, Bitcoin, Ethereum, gRPC, MQTT, Bluetooth
Vulnerability types: Shor-vulnerable, Grover-amplified, HNDL, downgrade attacks, weak randomness, deprecated protocols, transition gaps
Attack vectors: Shor factoring, Shor DLP, Grover search, quantum collision finding, HNDL passive collection, quantum MITM

Evaluation Results

Metric	Target	Score
Perplexity	< 10.0	TBD
Vulnerability Detection Rate	> 85%	TBD
Attack Vector Recognition	> 80%	TBD
Edge Latency (CPU)	< 1000ms	TBD
Memory Footprint	< 4GB	TBD

Usage

python

model_id = "quantumindssi/01_quantum_resistant_crypto_analyzer"
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = """Analyze the following TLS 1.2 implementation for quantum-vulnerable patterns:
```python
context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
context.set_ciphers('RSA-AES256-GCM-SHA384')

Identify the vulnerability type, quantum attack vector, and recommend mitigations."""

inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True))


## Limitations

- Not a substitute for certified security consultants or formal verification
- Synthetic training data may not capture all real-world edge cases
- English only
- Analysis is heuristic; false positives/negatives are possible

## Hardware Requirements

| Target | RAM | Notes |
|--------|-----|-------|
| Cloud GPU | 4GB | FP16 inference |
| Workstation | 3GB | INT8 quantized |
| Victron Edge | 2-3GB | INT8/INT4 quantized, CPU |

## Citation

```bibtex
@misc{01_quantum_resistant_crypto_analyzer,
  title={Quantum-Resistant Cryptographic Protocol Analyzer},
  author={QuantumIndSSI Ltd},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/quantumindssi/01_quantum_resistant_crypto_analyzer}}
}

Contact

GitHub: https://github.com/QuantumindSSI
HuggingFace: https://huggingface.co/quantumindssi
Email: admin@quantumindssi.com

QuantumindSSI/01-quantum-resistant-crypto-analyzer