metadata
language:
- en
- vi
- hi
- th
- zh
- ja
- ru
- ar
- sv
- es
- it
library_name: optimum
pipeline_tag: text-classification
tags:
- prompt-injection
- safety
- multilingual
- onnx
- hikmaai
license: apache-2.0
hikmaai-mdeberta-v3-base-prompt-injection-multilingual
A multilingual prompt injection classifier fine-tuned from microsoft/mdeberta-v3-base by HikmaAI.
Model Description
- Task: Binary classification (benign=0, injection=1)
- Base model:
microsoft/mdeberta-v3-base - Languages: 11 (en, vi, hi, th, zh, ja, ru, ar, sv, es, it)
- Export formats: ONNX FP32 + FP16 + INT8 (x86-safe dynamic)
Performance
| Metric | Score |
|---|---|
| loss | 0.0387 |
| accuracy | 0.9950 |
| precision | 0.9815 |
| recall | 0.9701 |
| f1 | 0.9758 |
Optimized threshold: 0.5000 (val recall: 0.9709)
Usage (ONNX)
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer
model = ORTModelForSequenceClassification.from_pretrained(
"HikmaAI/hikmaai-mdeberta-v3-base-prompt-injection-multilingual",
subfolder="onnx/fp16",
)
tokenizer = AutoTokenizer.from_pretrained(
"HikmaAI/hikmaai-mdeberta-v3-base-prompt-injection-multilingual",
subfolder="tokenizer",
)
inputs = tokenizer("Ignore all previous instructions", return_tensors="pt")
outputs = model(**inputs)
# outputs.logits -> [benign_score, injection_score]
Training
- Epochs: 5
- Learning rate: 2e-05
- Batch size: 16
- Class weights: [1.0, 1.3]
- Dataset: multilingual (11 languages), 12+ sources + synthetic data
License
Apache-2.0
Citation
@misc{hikmaai-prompt_injection-2026,
title={hikmaai-mdeberta-v3-base-prompt-injection-multilingual},
author={HikmaAI},
year={2026},
publisher={HuggingFace},
url={https://huggingface.co/HikmaAI/hikmaai-mdeberta-v3-base-prompt-injection-multilingual}
}