security-llama2-lora

A fine-tuned LoRA (Low-Rank Adaptation) model based on LLaMA 2 7B for security-focused Q&A, threat modeling, and OWASP guidance.

🎯 Model Overview

This model is optimized for security-related questions and provides responses on:

• OWASP Top 10 vulnerabilities
• Threat modeling and risk assessment
• API security best practices
• Cloud security considerations
• Incident response procedures
• Cryptography and secure coding
• Web application security

📊 Model Details

Attribute	Value
Base Model	meta-llama/Llama-2-7b-hf
Model Type	LoRA (Low-Rank Adaptation)
Total Parameters	6.7B (base model)
Trainable Parameters	~13.3M (0.2%)
Training Framework	HuggingFace Transformers + PEFT
Precision	FP16
Model Size	~50-100MB (LoRA adapters only)
License	LLaMA 2 Community License

📦 Files Included

security-llama2-lora/
├── adapter_model.bin           # LoRA weights (main model file)
├── adapter_config.json         # LoRA configuration
├── config.json                 # Model configuration
├── tokenizer.model             # LLaMA 2 tokenizer
├── tokenizer_config.json       # Tokenizer settings
├── special_tokens_map.json     # Special token mappings
└── README.md                   # This file

🚀 Quick Start

Installation

pip install transformers peft torch

Load the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base LLaMA 2 model
base_model_id = "meta-llama/Llama-2-7b-hf"
model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(base_model_id)

# Load security-focused LoRA adapters
model = PeftModel.from_pretrained(model, "debashis2007/security-llama2-lora")

# Move to GPU if available
model = model.to("cuda")

Generate Security Responses

import torch

# Example security question
prompt = "[INST] What is SQL injection and how do you prevent it? [/INST]"

# Tokenize input
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

# Generate response
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=256,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
    )

# Decode and print
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

📈 Training Details

Dataset

• Size: 24 security-focused Q&A pairs
• Categories:
  • OWASP security principles
  • Threat modeling techniques
  • API security best practices
  • Cloud security considerations
  • Incident response procedures
  • Cryptographic best practices
  • Web application security

Training Configuration

Parameter	Value
Epochs	1
Batch Size	1
Gradient Accumulation Steps	2
Learning Rate	2e-4
LoRA Rank (r)	8
LoRA Alpha	16
LoRA Dropout	0.05
Target Modules	q_proj, v_proj
Max Token Length	256
Optimizer	paged_adamw_8bit

Training Environment

• Platform: Google Colab
• GPU: NVIDIA T4 (16GB VRAM)
• Training Time: ~15 minutes
• Framework Versions:
  • transformers >= 4.36.2
  • peft >= 0.7.1
  • torch >= 2.0.0
  • bitsandbytes >= 0.41.0

⚡ Performance

Metric	Value
Model Size (LoRA only)	~50-100MB
Inference Speed	2-5 seconds/query (GPU)
Memory Usage (with base model)	~6-8GB VRAM
CPU Inference	Supported (slower, ~30-60 sec/query)

Inference Examples

Example 1: SQL Injection Prevention

Q: What is SQL injection and how do you prevent it?
A: [Model generates security-focused response]

Example 2: Threat Modeling

Q: Explain the STRIDE threat modeling methodology
A: [Model explains STRIDE with security examples]

Example 3: API Security

Q: What are the best practices for API security?
A: [Model provides comprehensive API security guidance]

🔧 Advanced Usage

Fine-tune Further

You can continue fine-tuning this model on your own security dataset:

from transformers import TrainingArguments, Trainer
from peft import get_peft_model, LoraConfig

# Load model with LoRA adapters
model = PeftModel.from_pretrained(base_model, "debashis2007/security-llama2-lora")

# Continue training...
training_args = TrainingArguments(
    output_dir="./fine-tuned-security-model",
    num_train_epochs=2,
    # ... other training args
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=your_dataset,
    # ... other trainer args
)

trainer.train()

Merge with Base Model

To create a standalone model (without needing base model):

# Merge LoRA with base model
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./security-llama2-merged")
tokenizer.save_pretrained("./security-llama2-merged")

📋 Limitations

1. Training Data: Model trained on only 24 examples - may have limited coverage
2. Accuracy: Security recommendations should be verified by domain experts
3. Legal Compliance: Not a substitute for professional security assessments
4. Bias: May reflect biases present in training data and base model
5. Outdated Information: Security landscape changes rapidly

⚠️ Important Notes

• Educational Purpose: This model is intended for educational and research purposes
• Professional Review: Always verify security recommendations from multiple authoritative sources
• Production Use: Not recommended for production critical systems without thorough testing
• License Compliance: Respects LLaMA 2 Community License terms

🔐 Security Best Practices

When using this model:

1. ✅ Verify Recommendations - Cross-reference with OWASP, security blogs, official docs
2. ✅ Consult Experts - Have security professionals review critical implementations
3. ✅ Keep Updated - Security threats evolve; update your knowledge regularly
4. ✅ Test Thoroughly - Test all security implementations in your environment
5. ✅ Monitor & Review - Continuously review security posture

📚 Related Resources

• LLaMA 2 Model Card
• PEFT Documentation
• HuggingFace Transformers
• OWASP Top 10

📝 Citation

If you use this model in your research, please cite:

@misc{security-llama2-lora-2024,
  author = {Debashis},
  title = {Security-Focused LLaMA 2 7B LoRA},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/debashis2007/security-llama2-lora}},
}

🤝 Support & Feedback

For issues, questions, or feedback:

• Open an issue on the model card
• Check existing discussions
• Share your use cases and improvements

📄 License

This model is subject to the LLaMA 2 Community License. Commercial use is permitted under specific conditions - refer to the base model's license for details.

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Created: December 2024
Base Model: Meta's LLaMA 2 7B
Fine-tuning: HuggingFace Transformers + PEFT
Training Platform: Google Colab