HyPE: Hyperbolic Prompt Espial
The HyPE package is the official implementation of the ICLR 2026 paper:
"Harnessing Hyperbolic Geometry for Harmful Prompt Detection and Sanitization"
Overview
HyPE enables high-accuracy detection of harmful prompts using hyperbolic geometry.
Output format
The model follows a binary classification schema where:
- 1: Harmless prompt
- 0: Harmful prompt
Quickstart
Install
pip install hype-defense
Run inference
from hype import inference
pred = inference("two birds are flying in the sky")
print(pred) # 1 = harmless, 0 = harmful
Documentation & code
Full documentation, training code, and additional examples are available here:
Metadata
Release files for hype-hyps 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| hype_hyps-0.1.1.tar.gz | 62.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| hype_hyps-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 134.8 kB
Release files / hype_hyps-0.1.1.tar.gz
| Download URL | hype_hyps-0.1.1.tar.gz |
|---|---|
| Size | 62.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.10.20
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Release files / hype_hyps-0.1.1-py3-none-any.whl
| Download URL | hype_hyps-0.1.1-py3-none-any.whl |
|---|---|
| Size | 72.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.10.20
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