Model Card for deberta-v3-base-prompt-injection-v2
This model is a fine-tuned version of microsoft/deberta-v3-base specifically developed to detect and classify prompt injection attacks which can manipulate language models into producing unintended outputs.
Introduction
Prompt injection attacks manipulate language models by inserting or altering prompts to trigger harmful or unintended responses. The deberta-v3-base-prompt-injection-v2 model is designed to enhance security in language model applications by detecting these malicious interventions.
This model classifies inputs into benign (0) and injection-detected (1).
Limitations
deberta-v3-base-prompt-injection-v2 is highly accurate in identifying prompt injections in English.
It does not detect jailbreak attacks or handle non-English prompts, which may limit its applicability in diverse linguistic environments or against advanced adversarial techniques.
Additionally, we do not recommend using this scanner for system prompts, as it produces false-positives.
Model Development
Over 20 configurations were tested during development to optimize the detection capabilities, focusing on various hyperparameters, training regimens, and dataset compositions.
Dataset
The dataset used for training the model was meticulously assembled from various public open datasets to include a wide range of prompt variations.
Additionally, prompt injections were crafted using insights gathered from academic research papers, articles, security competitions, and valuable LLM Guard's community feedback.
In compliance with licensing requirements, attribution is given where necessary based on the specific licenses of the source data. Below is a summary of the licenses and the number of datasets under each:
This version uses a new dataset, focusing solely on prompt injections in English, with improvements in model accuracy and response to community feedback.
The original model achieves the following results on our post-training dataset:
