OpenRT is a comprehensive framework for red teaming LLMs and VLMs with 30+ attack methods.
Features:
- 30+ attack methods (black-box and white-box)
- Multi-modal support (text and image attacks)
- Modular plugin architecture
- Configuration-driven experiments
- Multiple evaluation strategies
- Registry-based component loading
The framework follows a modular design with:
- Attack implementations (AutoDAN, GeneticAttack, DeepInception, etc.)
- Model integrations (OpenAI API, custom base_url support)
- Dataset handlers (StaticDataset, JSONLDataset)
- Evaluators (KeywordEvaluator, LLMEvaluator)
- Strategy patterns (Advancers, Propagators, Judges)
1 maintainer
Metadata
1 maintainer
Release files for openrt 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| openrt-0.0.2.tar.gz | 286.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| openrt-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 643.6 kB
Release files / openrt-0.0.2.tar.gz
| Download URL | openrt-0.0.2.tar.gz |
|---|---|
| Size | 286.0 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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No |
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twine/6.2.0 CPython/3.13.5
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Release files / openrt-0.0.2-py3-none-any.whl
| Download URL | openrt-0.0.2-py3-none-any.whl |
|---|---|
| Size | 357.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-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/3.13.5
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