enguard/small-guard-32m-en-prompt-jailbreak-binary-in-the-wild

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-base-32m for the prompt-jailbreak-binary found in the TrustAIRLab/in-the-wild-jailbreak-prompts dataset.

Installation

pip install model2vec[inference]

Usage

from model2vec.inference import StaticModelPipeline

model = StaticModelPipeline.from_pretrained(
 "enguard/small-guard-32m-en-prompt-jailbreak-binary-in-the-wild"
)


# Supports single texts. Format input as a single text:
text = "Example sentence"

model.predict([text])
model.predict_proba([text])

Why should you use these models?

• Optimized for precision to reduce false positives.
• Extremely fast inference: up to x500 faster than SetFit.

This model variant

Below is a quick overview of the model variant and core metrics.

Confusion Matrix

Other model variants

Below is a general overview of the best-performing models for each dataset variant.

Resources

• Awesome AI Guardrails: https://github.com/enguard-ai/awesome-ai-guardails
• Model2Vec: https://github.com/MinishLab/model2vec
• Docs: https://minish.ai/packages/model2vec/introduction

Citation

If you use this model, please cite Model2Vec:

@software{minishlab2024model2vec,
 author = {Stephan Tulkens and {van Dongen}, Thomas},
 title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
 year = {2024},
 publisher = {Zenodo},
 doi = {10.5281/zenodo.17270888},
 url = {https://github.com/MinishLab/model2vec},
 license = {MIT}
}

Tiny guardrails for 'prompt-jailbreak-binary' trained on https://huggingface.co/datasets/TrustAIRLab/in-the-wild-jailbreak-prompts. • 5 items • Updated Nov 1, 2025 • 1