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vijil_dome_prompt_injection_detection

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vijil_dome_prompt_injection_detection
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---
license: apache-2.0
tags:
- injection
- security
- llm
- prompt-injection
---

# Model Card for Vijil Prompt Injection

## Model Details

### Model Description

This model is a fine-tuned version of ModernBert to classify prompt-injection prompts which can manipulate language models into producing unintended outputs.

- **Developed by:** Vijil AI
- **License:** apache-2.0
- **Finetuned version of [ModernBERT](https://huggingface.co/docs/transformers/en/model_doc/modernbert)**

## Uses

Prompt injection attacks manipulate language models by inserting or altering prompts to trigger harmful or unintended responses. 
The vijil/vijil_dome_prompt_injection_detection model is designed to enhance security in language model applications by detecting prompt-injection attacks.

## How to Get Started with the Model

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch

tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base") 
model = AutoModelForSequenceClassification.from_pretrained("vijil/vijil_dome_prompt_injection_detection")

classifier = pipeline(
  "text-classification",
  model=model,
  tokenizer=tokenizer,
  truncation=True,
  max_length=512,
  device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)

print(classifier("this is a prompt-injection prompt"))

```

## Training Details

### Training Data

The dataset used for training the model was taken from 

[wildguardmix/train](https://huggingface.co/datasets/allenai/wildguardmix)
and
[safe-guard-prompt-injection/train](https://huggingface.co/datasets/xTRam1/safe-guard-prompt-injection)

### Training Procedure

Supervised finetuning with above dataset 

#### Training Hyperparameters

* learning_rate: 5e-05

* train_batch_size: 32

* eval_batch_size: 32

* optimizer: adamw_torch_fused

* lr_scheduler_type: cosine_with_restarts 

* warmup_ratio: 0.1       

* num_epochs: 3          

## Evaluation

* Training Loss: 0.0036

* Validation Loss: 0.209392

* Accuracy: 0.961538

* Precision: 0.958362 

* Recall: 0.957055 

* Fl: 0.957708

#### Testing Data

The dataset used for training the model was taken from 

[wildguardmix/test](https://huggingface.co/datasets/allenai/wildguardmix)
and
[safe-guard-prompt-injection/test](https://huggingface.co/datasets/xTRam1/safe-guard-prompt-injection)

### Results



## Model Card Contact
https://vijil.ai