README.md

---
license: apache-2.0
language:
- en
pipeline_tag: text-classification
---
# Model Card for Model ID

<!-- Based on  https://huggingface.co/t5-small, model generates SQL from text given table list with "CREATE TABLE" statements. 
This is a very light weigh model and could be used in multiple analytical applications. -->

Based on  [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) (MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks). This model detects SQLInjection attacks in the input string (check How To Below). This is a very very light  model (100mb) and can be used for edge computing use cases. Used dataset from [Kaggle](www.kaggle.com) called [SQl_Injection](https://www.kaggle.com/datasets/sajid576/sql-injection-dataset).
**Please test the model before deploying into any environment**.
Contact us for more info: support@cloudsummary.com
### Code Repo
Here is the code repo https://github.com/cssupport23/AI-Model---SQL-Injection-Attack-Detector

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** cssupport (support@cloudsummary.com)
- **Model type:** Language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model :** [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased)

### Model Sources 

<!-- Provide the basic links for the model. -->

Please refer [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) for Model Sources.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
import torch
from transformers import MobileBertTokenizer, MobileBertForSequenceClassification


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = MobileBertTokenizer.from_pretrained('google/mobilebert-uncased')
model = MobileBertForSequenceClassification.from_pretrained('cssupport/mobilebert-sql-injection-detect')
model.to(device)
model.eval()

def predict(text):
    inputs = tokenizer(text, padding=False, truncation=True, return_tensors='pt', max_length=512)
    input_ids = inputs['input_ids'].to(device)
    attention_mask = inputs['attention_mask'].to(device)

    with torch.no_grad():
        outputs = model(input_ids=input_ids, attention_mask=attention_mask)

    logits = outputs.logits
    probabilities = torch.softmax(logits, dim=1)
    predicted_class = torch.argmax(probabilities, dim=1).item()
    return predicted_class, probabilities[0][predicted_class].item()


#text = "SELECT * FROM users WHERE username = 'admin' AND password = 'password';"
#text = "select * from users where username = 'admin' and password = 'password';"
#text = "SELECT * from USERS where id  =  '1' or @ @1  =  1 union select 1,version  (    )   -- 1'"
#text = "select * from data where id  =  '1'  or @"
text ="select * from users where id  =  1 or 1#\"?  =  1 or 1  =  1 -- 1"
predicted_class, confidence = predict(text)

if predicted_class > 0.7:
    print("Prediction: SQL Injection Detected")
else:
    print("Prediction: No SQL Injection Detected")
    
print(f"Confidence: {confidence:.2f}")
# OUTPUT
# Prediction: SQL Injection Detected
# Confidence: 1.00
```


## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

[More Information Needed]

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Could used in application where natural language is to be converted into SQL queries. 
[More Information Needed]



### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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



## Technical Specifications 

### Model Architecture and Objective

[google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased)

### Compute Infrastructure



#### Hardware

one P6000 GPU

#### Software

Pytorch and HuggingFace

## Disclaimer
#### Educational/Informational Use Only
This model is provided solely for educational or informational purposes. It is not intended to be used for malicious activities or any other unlawful behavior.

#### No Warranty
This model is provided on an "as is" basis, without warranties or conditions of any kind, whether express or implied. We make no guarantees regarding its accuracy, reliability, or performance. Use of this model is at your own risk.

#### Limitation of Liability
Under no circumstances shall the creators, maintainers, or contributors of this model be held liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including but not limited to procurement of substitute goods or services; loss of use, data, or profits; or business interruption) arising out of the use or inability to use this model, even if advised of the possibility of such damage.

#### No Responsibility for Outcomes
We are not responsible for any damages, security breaches, or other issues that may result from using this model. If the model fails to detect certain SQL injection attacks or produces false positives, we will not be held liable for any consequences arising from such outcomes.

#### User Responsibility
By using or downloading this model, you agree to be solely responsible for compliance with all applicable laws and regulations. Any misuse of this model, including using it to facilitate or commit malicious activities, remains the sole responsibility of the user.