metadata

base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
library_name: peft

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Model Details

Model Description

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Uses

Direct Use

To use the SQL Injection Classifier model, you can follow the code snippet below. This example demonstrates how to predict whether a given SQL query is normal or an injection attack.

from unsloth import FastLanguageModel
from transformers import AutoTokenizer

# Load the model and tokenizer
model_name = "unsloth/DeepSeek-R1-Distill-Llama-8B"
hf_token = "your hf tokens"

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_name,
    load_in_4bit=True,
    token=hf_token,
)

# Function for testing queries
def predict_sql_injection(query):
    # Prepare the model for inference
    inference_model = FastLanguageModel.for_inference(model)

    prompt = f"### Instruction:\nClassify the following SQL query as normal (0) or an injection attack (1).\n\n### Query:\n{query}\n\n### Classification:\n"
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

    # Use the inference model for generation
    outputs = inference_model.generate(
        input_ids=inputs.input_ids,
        attention_mask=inputs.attention_mask,
        max_new_tokens=1000,
        use_cache=True,
    )
    prediction = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
    return prediction.split("### Classification:\n")[-1].strip()

# Example usage
test_query = "SELECT * FROM users WHERE id = '1' OR '1'='1' --"
result = predict_sql_injection(test_query)
print(f"Query: {test_query}\nPrediction: {result}")

Downstream Use

This model can be integrated into applications requiring SQL injection detection, such as web application firewalls, database query analyzers, and security auditing tools. It can help identify and prevent potential vulnerabilities in SQL queries.

Out-of-Scope Use

This model should not be used for malicious purposes, such as testing vulnerabilities on unauthorized systems, or for making security decisions without human oversight. It is essential to understand that the model's predictions should be interpreted with caution and supplemented with additional security measures.

Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Framework versions

PEFT 0.14.0