phi3-cybersec-advisor-lora

Phi-3 Mini (LoRA Fine-Tuned on MITRE-STIX-CVE-ExploitDB Dataset)

Model Summary

This model is a fine-tuned version of microsoft/phi-3-mini-128k-instruct using LoRA (Low-Rank Adaptation) and 8-bit quantization for parameter-efficient training.

The fine-tuning dataset is jason-oneal/mitre-stix-cve-exploitdb-dataset-alpaca, which contains security-related instruction-response examples (CVE, STIX, ExploitDB context).

The goal of this model is to act as a cybersecurity knowledge assistant that can answer questions about CVEs, exploits, and related security topics.

• Base Model: microsoft/phi-3-mini-128k-instruct
• Fine-tuning Method: LoRA (8-bit PEFT with bitsandbytes)
• Dataset: jason-oneal/mitre-stix-cve-exploitdb-dataset-alpaca
• Languages: English
• Context Length: 128k tokens

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Intended Uses

• Designed for: cybersecurity Q&A, reasoning about vulnerabilities, exploits, and threat intelligence.
• Can be used for: research, learning, and prototyping of cyber threat assistants.

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Dataset

• Name: MITRE-STIX-CVE-ExploitDB Dataset (Alpaca format)
• Source: jason-oneal/mitre-stix-cve-exploitdb-dataset-alpaca
• Schema: Instruction–response pairs in Alpaca format
• Size Used: Up to 5,000 training samples (subset for efficiency)

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Training Procedure

• Frameworks: Hugging Face Transformers, PEFT, bitsandbytes

• Precision: 8-bit quantization (bnb.int8) + FP16 training

• Optimizer: AdamW

• Batch Size: 4 per device

• Epochs: 1

• Learning Rate: 3e-4

• Warmup Steps: 50

• Max Length: 1024 tokens

• LoRA Config:

  • r = 16
  • alpha = 16
  • dropout = 0.05
  • target modules: q_proj, k_proj, v_proj, o_proj, w1, w2, dense

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Evaluation

• Metric: Training loss (did not include a validation set in this run).
• Qualitative Evaluation: The model produces meaningful responses to security-related prompts, but further fine-tuning with eval sets is recommended.

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How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name = "sushanrai/phi3-cybersec-advisor-lora"  # replace with your repo
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Explain CVE-2021-44228 in simple terms"
output = pipe(prompt, max_new_tokens=300, do_sample=True)
print(output[0]["generated_text"])

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Ethical Considerations

• This model is trained on cybersecurity data and may produce outputs that describe exploits.
• Should only be used for research, learning, and defensive security purposes.
• Not intended for malicious use.

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Citation

If you use this model, please cite:

@misc{phi3_mitre_lora_2025,
  title={Phi-3 Mini LoRA Fine-Tuned on MITRE-STIX-CVE-ExploitDB Dataset},
  author={HackDMSV},
  year={2025},
  howpublished={\url{https://huggingface.co/sushanrai/phi3-cybersec-advisor-lora}}
}