---
language:
- en
- ar
tags:
- mlx
- cybersecurity
- security
- qwen3
- saif
- circuit-aware
- hayula
library_name: mlx
pipeline_tag: text-generation
license: apache-2.0
---

# SAIF v2 Pro — Circuit-Aware Security Specialist

**SAIF (سيف)** v2 Pro is a unified security analysis model based on **Qwen3-8B**, trained with Circuit-Aware LoRA and IACR (Integrated Adapter Circuit Refinement).

## Architecture

- **Base Model:** Qwen3-8B (36 layers)
- **Circuit-Aware LoRA:** Trained on 387 security analysis samples (rank=8, scale=20)
- **IACR Fine-tuning:** Integrated Adapter Circuit Refinement (loss 0.033)
- **13 Security Probes:** Avg accuracy 98.7%
- **Fused Format:** MLX native (single 15GB model)

## Capabilities

| # | Capability | Supported |
|---|-----------|:---------:|
| 1 | SQL Injection Analysis | ✅ |
| 2 | SSRF Detection | ✅ |
| 3 | XSS Analysis | ✅ |
| 4 | RCE Exploitation | ✅ |
| 5 | IDOR Detection | ✅ |
| 6 | CSRF Analysis | ✅ |
| 7 | LFI Detection | ✅ |
| 8 | Open Redirect | ✅ |
| 9 | CORS Misconfig | ✅ |
| 10 | Auth Bypass | ✅ |
| 11 | Business Logic | ✅ |
| 12 | Race Conditions | ✅ |
| 13 | LLM Prompt Inject | ✅ |

## Usage (MLX)

```python
from mlx_lm import load, generate

model, tokenizer = load("BinSaqban/SAIF-v2-Pro")
prompt = "Analyze this SSRF vulnerability: ..."
messages = [{"role": "user", "content": prompt}]
prompt_text = tokenizer.apply_chat_template(messages, tokenize=False)
response = generate(model, tokenizer, prompt=prompt_text, max_tokens=512)
print(response)
```

## Server Mode

```bash
mlx_lm.server --model BinSaqban/SAIF-v2-Pro --port 8452
```

## Performance

- **Latency:** ~7.4s for 200 tokens (27 tok/s on M2 Ultra)
- **Accuracy:** 98.7% on 13 security probes
- **Memory:** ~16GB (M2 Ultra 192GB)

## Training

Trained on Hayula Labs infrastructure using MLX on M2 Ultra (192GB).
- Circuit-Aware: 500 iterations, train loss 0.927
- IACR: Additional refinement, final loss 0.033

## License

Apache 2.0