secureiq-threat-classifier

🛡️ SecureIQ Threat Classifier

Fine-tuned Llama 3.2 3B for AI-powered security threat classification and incident analysis. Part of the SecureIQ open-source security intelligence platform.

Model Details

• Developed by: Nitesh Kumawat
• Base model: Llama 3.2 3B Instruct
• Fine-tuning method: LoRA via Unsloth
• Language: English
• License: MIT
• Training hardware: Google Colab T4 GPU (free tier)

Training Data

• NVD CVE Database — 200+ real vulnerability records from nvd.nist.gov
• Security incident Q&A pairs — threat classification and response scenarios
• CVSS severity scoring examples — CRITICAL / HIGH / MEDIUM / LOW

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Nitesh208/secureiq-threat-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are SecureIQ, an expert AI security analyst.<|eot_id|>
<|start_header_id|>user<|end_header_id|>
Classify this vulnerability: Remote code execution via buffer overflow<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Example Output

Severity: CRITICAL CVSS Score: 9.8/10 Risk Score: 95/100 Recommendation: Immediate emergency patching required. Isolate affected systems.

SecureIQ Platform

This model powers the threat classification layer of SecureIQ:

Links

• GitHub: https://github.com/nite208/secureiq
• LinkedIn: https://linkedin.com/in/nitesh-kumawat-185356289