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https://github.com/Azure/PyRIT
The Python Risk Identification Tool for generative AI (PyRIT) is an open access automation framework to empower security professionals and machine learning engineers to proactively find risks in their generative AI systems.
https://github.com/Azure/PyRIT
ai-red-team generative-ai red-team-tools responsible-ai
Last synced: about 1 month ago
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The Python Risk Identification Tool for generative AI (PyRIT) is an open access automation framework to empower security professionals and machine learning engineers to proactively find risks in their generative AI systems.
- Host: GitHub
- URL: https://github.com/Azure/PyRIT
- Owner: Azure
- License: mit
- Created: 2023-12-12T15:46:28.000Z (almost 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-25T22:19:24.000Z (about 2 months ago)
- Last Synced: 2024-10-29T15:09:27.746Z (about 1 month ago)
- Topics: ai-red-team, generative-ai, red-team-tools, responsible-ai
- Language: Python
- Homepage:
- Size: 45.3 MB
- Stars: 1,848
- Watchers: 24
- Forks: 357
- Open Issues: 65
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
- Support: SUPPORT.md
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README
# Python Risk Identification Tool for generative AI (PyRIT)
The Python Risk Identification Tool for generative AI (PyRIT) is an open
access automation framework to empower security professionals and ML
engineers to red team foundation models and their applications.## Introduction
PyRIT is a library developed by the AI Red Team for researchers and engineers
to help them assess the robustness of their LLM endpoints against different
harm categories such as fabrication/ungrounded content (e.g., hallucination),
misuse (e.g., bias), and prohibited content (e.g., harassment).PyRIT automates AI Red Teaming tasks to allow operators to focus on more
complicated and time-consuming tasks and can also identify security harms such
as misuse (e.g., malware generation, jailbreaking), and privacy harms
(e.g., identity theft).The goal is to allow researchers to have a baseline of how well their model
and entire inference pipeline is doing against different harm categories and
to be able to compare that baseline to future iterations of their model.
This allows them to have empirical data on how well their model is doing
today, and detect any degradation of performance based on future improvements.Additionally, this tool allows researchers to iterate and improve their
mitigations against different harms.
For example, at Microsoft we are using this tool to iterate on different
versions of a product (and its metaprompt) so that we can more effectively
protect against prompt injection attacks.![PyRIT architecture](https://github.com/Azure/PyRIT/blob/main/assets/pyrit_architecture.png)
## Where can I learn more?
Microsoft Learn has a
[dedicated page on AI Red Teaming](https://learn.microsoft.com/en-us/security/ai-red-team).Check out our [docs](https://github.com/Azure/PyRIT/blob/main/doc/README.md) for more information
on how to [install PyRIT](https://github.com/Azure/PyRIT/blob/main/doc/setup/install_pyrit.md),
our [How to Guide](https://github.com/Azure/PyRIT/blob/main/doc/how_to_guide.ipynb),
and more, as well as our [demos](https://github.com/Azure/PyRIT/tree/main/doc/code).## Trademarks
This project may contain trademarks or logos for projects, products, or services.
Authorized use of Microsoft trademarks or logos is subject to and must follow
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must
not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's
policies.## Citing PyRIT
If you use PyRIT in your research, please cite our preprint paper as follows:
```
@misc{munoz2024pyritframeworksecurityrisk,
title={PyRIT: A Framework for Security Risk Identification and Red Teaming in Generative AI Systems},
author={Gary D. Lopez Munoz and Amanda J. Minnich and Roman Lutz and Richard Lundeen and Raja Sekhar Rao Dheekonda and Nina Chikanov and Bolor-Erdene Jagdagdorj and Martin Pouliot and Shiven Chawla and Whitney Maxwell and Blake Bullwinkel and Katherine Pratt and Joris de Gruyter and Charlotte Siska and Pete Bryan and Tori Westerhoff and Chang Kawaguchi and Christian Seifert and Ram Shankar Siva Kumar and Yonatan Zunger},
year={2024},
eprint={2410.02828},
archivePrefix={arXiv},
primaryClass={cs.CR},
url={https://arxiv.org/abs/2410.02828},
}
```Additionally, please cite the tool itself following the `CITATION.cff` file in the root of this repository.