Python Risk Identification Tool for Generative AI (PyRIT)
PyRIT, or the Python Risk Identification Tool for generative AI, is an innovative and accessible automation framework designed specifically for security professionals and machine learning engineers. Its primary objective is to enhance their ability to conduct red teaming exercises on foundational AI models and their applications.
Introduction
Developed by the AI Red Team, PyRIT serves as a comprehensive toolkit for researchers and engineers. It empowers them to evaluate how robust their language model endpoints are against various threats such as fabricating ungrounded content—commonly known as hallucinations—misuse, such as bias introduction, and prohibited content, like harassment.
PyRIT simplifies the task of AI Red Teaming by automating several labor-intensive aspects, allowing users to focus on more complex and time-demanding challenges. Among other capabilities, it can spot potential security threats, including misuse (think malware generation or breaking restrictions) and privacy issues, like identity theft.
The ultimate aim of PyRIT is to provide researchers with a baseline understanding of how effectively their model—and the entire inference pipeline—performs when confronting different harm categories. This benchmarking enables a comparison of the model's performance against future iterations, allowing for the detection of any performance degradation as the model evolves.
Beyond assessment, PyRIT aids researchers in refining and enhancing their strategies to mitigate diverse threats. For instance, at Microsoft, this tool plays a crucial role in iterating on various product versions to reinforce defenses against prompt injection attacks.
Where Can I Learn More?
For those interested in delving deeper into AI Red Teaming, Microsoft Learn offers a dedicated page on this subject.
Moreover, extensive documentation on PyRIT is available, which includes a how-to guide, installation instructions, and practical demos. All these resources are accessible on PyRIT's GitHub repository.
Trademarks
It's important to note that this project might include trademarks or logos associated with specific projects, products, or services. Any usage involving Microsoft trademarks or logos must adhere to the Microsoft's Trademark & Brand Guidelines. For third-party trademarks, any use must comply with the respective third-party policies.
Citing PyRIT
For researchers utilizing PyRIT in their work, citing the preprint paper is encouraged 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, when referencing the tool itself, please refer to the CITATION.cff file located in the root of the repository.