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license: unknown |
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# Overview |
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<!-- This model is obtained by finetuning Pre-Trained RoBERTa on dataset containing several sets of malicious prompts. |
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Using this model, we can classify malicious prompts that can lead towards creation of phishing websites and phishing emails. |
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This model is obtained by finetuning a Pre-Trained RoBERTa using a dataset encompassing multiple sets of malicious prompts, as detailed in the corresponding arXiv paper. |
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Using this model, we can classify malicious prompts that can lead towards creation of phishing websites and phishing emails. --> |
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Our model, "ScamLLM" is designed to identify malicious prompts that can be used to generate phishing websites and emails using popular commercial LLMs like ChatGPT, Bard and Claude. |
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This model is obtained by finetuning a Pre-Trained RoBERTa using a dataset encompassing multiple sets of malicious prompts. |
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Try out "ScamLLM" using the Inference API. Our model classifies prompts with "Label 1" to signify the identification of a phishing attempt, while "Label 0" denotes a prompt that is considered safe and non-malicious. |
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## Dataset Details |
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The dataset utilized for training this model has been created using malicious prompts generated by GPT-4. |
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Due to being active vulnerabilities under review, our dataset of malicious prompts is available only upon request at this stage, with plans for a public release scheduled for May 2024. |
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## Training Details |
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The model was trained using RobertaForSequenceClassification.from_pretrained. |
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In this process, both the model and tokenizer pertinent to the RoBERTa-base were employed and trained for 10 epochs (learning rate 2e-5 and AdamW Optimizer). |
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## Inference |
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There are multiple ways to test this model, with the simplest being to use the Inference API, as well as with the pipeline "text-classification" as below: |
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```python |
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from transformers import pipeline |
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classifier = pipeline(task="text-classification", model="phishbot/ScamLLM", top_k=None) |
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prompt = ["Your Sample Sentence or Prompt...."] |
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model_outputs = classifier(prompt) |
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print(model_outputs[0]) |
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``` |
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