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README.md

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 We formulated the prompt injection detector problem as a classification problem and trained our own language model 
 to detect whether a given user prompt is an attack or safe. First, to train our own prompt injection detector, we 
 required high-quality labelled data; however, existing prompt injection datasets were either too small (on the magnitude 
-of O(100)) or didn’t cover a broad spectrum of prompt injection attacks. To this end, inspired by the GLAN paper, 
+of O(100)) or didn’t cover a broad spectrum of prompt injection attacks. To this end, inspired by the [GLAN paper](https://arxiv.org/abs/2402.13064), 
 we created a custom synthetic prompt injection dataset using a categorical tree structure and generated 3000 distinct 
 attacks. We started by curating our seed data using open-source datasets ([vmware/open-instruct](https://huggingface.co/datasets/VMware/open-instruct), 
 [huggingfaceh4/helpful-instructions](https://huggingface.co/datasets/HuggingFaceH4/helpful_instructions),