DeBERTa-v3 Base Prompt Injection
Text classification model for detecting prompt injection attacks using DeBERTa-v3 architecture.
- Status
- Not yet determined
- Last checked
- Unverified
- License
- Open source
- Pricing
- Free tier · from $0
What it is
This model is designed to classify text and detect potential prompt injection attacks, leveraging the advanced capabilities of the DeBERTa-v3 base architecture. It's particularly useful in securing applications against adversarial prompts.
This model stands out by offering a specialized solution for detecting prompt injection attacks, leveraging the advanced capabilities of DeBERTa-v3 architecture to provide high accuracy in text classification tasks.
When it fits — and when it doesn’t
A good fit for
Teams building secure NLP applications that need robust prompt injection detection.
Projects requiring high accuracy in classifying text for security purposes.
Look elsewhere if
Applications where real-time processing is critical and cannot afford the latency of model inference.
Scenarios with extremely limited computational resources, as this model requires significant compute power.
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Limited integrations with non-Python environments
Primary language support is Python, limited official support for other languages
- 02
Performance may degrade on large datasets
Model size and complexity can lead to slower inference times with larger inputs
What it does well
Detects prompt injection attacks in text data.
Based on the DeBERTa-v3 base architecture for robust performance.
Open-source and freely available under Apache-2.0 license.
Pricing
- Free tier
- YesOpen source — free to use
- Paid from
- $0
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up DeBERTa-v3 Base Prompt Injection
Setup steps, common gotchas and what to check after 30 days.