🛡️ 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
🎯 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))