vanshkamra12/CyberSecurity-Model

🛡️ CyberThreat Intel LLM (Phi-3-mini Fine-Tuned)

This is a fine-tuned version of Microsoft's Phi-3-mini-4k-instruct, optimized specifically to act as a Cybersecurity Threat Analyst. It takes raw CVE vulnerability data and generates professional, structured threat intelligence reports.

▶️ Try the Live Demo: CyberThreat Intel Analyzer (Hugging Face Space)

💻 Code & Dataset: GitHub Repository

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🎯 What it does

Feed the model a raw CVE description, CVSS score, and vendor, and it will generate a comprehensive report including:

• Executive Summary (Plain English explanation)
• Technical Analysis (Vectors, complexity, privileges)
• Indicators of Compromise (IOCs)
• MITRE ATT&CK Mappings
• Risk Assessment
• Remediation Steps
• Detection Rules (YARA/Sigma)

🧠 Model Details

• Base Model: Phi-3-mini-4k-instruct (3.8B parameters)
• Training Method: QLoRA (4-bit quantization) with Unsloth
• Trainable Parameters: 29.8M (0.78% of total)
• Training Data: 471 synthetic instruction-tuning pairs generated using Llama 3.1 8B from raw NIST NVD CVE data.
• Final Training Loss: 0.337

🚀 How to use in Python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "vanshkamra12/CyberSecurity-Model"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    device_map="auto"
)

prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
Analyze the following vulnerability data and produce a structured threat intelligence report.

### Input:
CVE ID: CVE-2024-21762
Description: A out-of-bound write vulnerability in FortiOS SSL VPN allows a remote unauthenticated attacker to execute arbitrary code or commands via specially crafted HTTP requests.
CVSS Score: 9.8 CRITICAL
Vendor: Fortinet

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1000, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))