This model is specialized in providing detailed, accurate responses to security-related queries including vulnerabilities, attack vectors, defense mechanisms, and best practices.

📋 Model Details

Property	Value
Base Model	mistralai/Mistral-7B-Instruct-v0.1
Fine-tuning Method	LoRA (r=8, α=16)
Training Data	24 security Q&A pairs (JSONL format)
Model Size	7B parameters (base)
LoRA Adapter Size	~50-100 MB
Framework	Transformers + PEFT
License	Same as Mistral (Apache 2.0)

🎯 Use Cases

This model is designed for:

Security Education - Learning about vulnerabilities and defenses
Vulnerability Assessment - Understanding attack vectors
Security Best Practices - Implementation recommendations
Threat Analysis - Explaining security concepts
Compliance Questions - Security-related compliance topics

✅ What It Does Well

Explains common security vulnerabilities (SQL injection, XSS, CSRF, etc.)
Provides defense mechanisms and mitigation strategies
Discusses security best practices and standards
Analyzes threat models and attack scenarios
Recommends secure coding practices

⚠️ Limitations

Trained on limited dataset (24 examples) for demonstration purposes
May not cover all specialized security topics
Should be used as educational supplement, not primary security advisor
Responses should be validated against official security documentation

🚀 Quick Start

Installation

bash

# Install required packages
pip install transformers peft torch

# (Optional) For GPU support
pip install torch --index-url https://download.pytorch.org/whl/cu118

Basic Usage

python

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

# Load the model
model = AutoPeftModelForCausalLM.from_pretrained(
    "debashis2007/security-mistral-lora",
    device_map="auto",
    torch_dtype=torch.float16,
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

# Prepare input (Mistral format)
prompt = "[INST] What is SQL injection and how do you prevent it? [/INST]"
inputs = tokenizer(prompt, return_tensors="pt")

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

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

Advanced Usage with Custom Settings

python

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
import torch

# Load model with specific settings
model = AutoPeftModelForCausalLM.from_pretrained(
    "debashis2007/security-mistral-lora",
    device_map="auto",
    torch_dtype=torch.float16,
    load_in_8bit=True,  # Optional: 8-bit quantization for memory efficiency
)

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

# Multiple questions
questions = [
    "What are the main types of web application attacks?",
    "How do you implement CSRF protection?",
    "Explain the principle of least privilege",
]

for question in questions:
    prompt = f"[INST] {question} [/INST]"
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_length=512,
            temperature=0.7,
            top_p=0.95,
            do_sample=True,
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print(f"Q: {question}\nA: {response}\n" + "="*60 + "\n")

📊 Training Details

Training Configuration

Parameter	Value
Learning Rate	2e-4
Epochs	1
Batch Size	1
Gradient Accumulation	4
Max Token Length	256
Optimizer	paged_adamw_8bit
Precision	FP16
LoRA Rank (r)	8
LoRA Alpha	16
LoRA Dropout	0.05
Target Modules	["q_proj", "v_proj"]

Training Environment

Platform: Google Colab
GPU: NVIDIA T4 (16GB VRAM)
Training Time: ~10-12 minutes
Framework: Transformers 4.36.2 + PEFT 0.7.1
Memory Optimization: 4-bit quantization + gradient checkpointing

Dataset

Format: JSONL (JSON Lines)
Size: 24 security Q&A pairs
Topics:
- SQL Injection
- Cross-Site Scripting (XSS)
- Cross-Site Request Forgery (CSRF)
- Authentication & Authorization
- Encryption & Hashing
- Security Best Practices
- Vulnerability Assessment
- Threat Modeling

Example data point:

json

{
  "instruction": "What is SQL injection and how do you prevent it?",
  "response": "SQL injection is a security vulnerability that occurs when an attacker inserts malicious SQL code into input fields. It exploits improperly validated or unescaped user input. Prevention methods include: 1) Using parameterized queries, 2) Input validation and sanitization, 3) Principle of least privilege for database accounts, 4) Web application firewalls, 5) Security testing and code reviews."
}

💡 Usage Examples

Example 1: Security Vulnerability Explanation

python

prompt = "[INST] What is a buffer overflow vulnerability? [/INST]"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Expected Output: Explanation of buffer overflow, its consequences, and prevention methods.

Example 2: Best Practice Recommendation

python

prompt = "[INST] What are the best practices for password storage? [/INST]"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Expected Output: Recommendations including hashing, salting, key derivation functions, etc.

Example 3: Attack Scenario Analysis

python

prompt = "[INST] How would an attacker exploit an unpatched software vulnerability? [/INST]"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Expected Output: Explanation of exploitation methods and defense strategies.

⚙️ Model Architecture

The model uses:

Base: Mistral 7B Instruct v0.1
Adaptation: LoRA (Low-Rank Adaptation)
Quantization: 4-bit (during training)
Key Modifications:
- Q and V projections adapted with LoRA
- Gradient checkpointing for memory efficiency
- Flash Attention 2 for faster inference (when available)

LoRA Details

python

LoraConfig(
    r=8,                          # Rank
    lora_alpha=16,                # Scaling factor
    lora_dropout=0.05,            # Dropout probability
    bias="none",                  # Don't train bias
    task_type="CAUSAL_LM",        # Causal language modeling
    target_modules=["q_proj", "v_proj"],  # Adapted modules
    inference_mode=False,         # Training mode
)

🔍 Evaluation

Model Performance

The model was evaluated on:

Accuracy: Factual correctness of security information
Relevance: Appropriateness of responses to queries
Clarity: Comprehensibility of explanations
Completeness: Coverage of important security concepts

Known Issues

Limited training data may result in incomplete responses for edge cases
Responses should be verified against official security documentation
Not suitable as primary security advisory tool
May require fine-tuning with domain-specific data for production use

🛠️ Fine-tuning This Model

To fine-tune this model further on your own data:

python

from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
from datasets import load_dataset

# Load base model with adapter
model = AutoPeftModelForCausalLM.from_pretrained("debashis2007/security-mistral-lora")

# Merge with base model if you want to continue training
model = model.merge_and_unload()

# Or create new LoRA config for additional training
lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

model = get_peft_model(model, lora_config)

# Define training arguments
training_args = TrainingArguments(
    output_dir="./security-mistral-lora-v2",
    num_train_epochs=3,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    fp16=True,
    save_steps=10,
    logging_steps=5,
)

# Create trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
)

# Train
trainer.train()

📚 Resources

Documentation

Related Models

Mistral 7B - Base model
Mistral 7B Instruct - Instruction-tuned base
LLaMA 2 7B - Alternative base model
Phi-2 - Smaller alternative

⚖️ License & Attribution

This model is based on:

Mistral 7B: Licensed under Mistral AI Research License Agreement

Modifications using LoRA are provided as-is. Please comply with the original Mistral license.

Citation

If you use this model, please cite:

bibtex

@misc{security-mistral-lora,
  title={Security-Focused Mistral 7B LoRA},
  author={debashis2007},
  year={2024},
  howpublished={\url{https://huggingface.co/debashis2007/security-mistral-lora}}
}

🤝 Contributing

Found an issue or have suggestions? Feel free to open an issue on the model repository.

Ways to Contribute

Report bugs or issues
Suggest improvements to prompts or responses
Provide additional training data
Contribute fine-tuning scripts
Help with documentation

⚠️ Disclaimer

This model is for educational and research purposes only.

Responses should not be used as the sole basis for security decisions
Always validate against official security documentation
Consult with security professionals for production systems
The developers assume no liability for misuse or harmful outputs

📧 Contact

For questions about this model:

HuggingFace: @debashis2007
Model: security-mistral-lora

📈 Version History

Version	Date	Changes
v1.0	2024-12	Initial release with 24 security examples

🎓 Educational Use

This model is part of a security-focused AI training project. It demonstrates:

LoRA fine-tuning on domain-specific data
Memory-efficient training on consumer GPUs
Deploying custom LLMs on HuggingFace Hub
Building security-focused AI applications

Last Updated: December 2024
Model Status: Active
Maintained By: debashis2007