Optimizing the PyRIT Framework Architecture for Large Language Model (LLM) Robustness Assessment. Explore the PyRIT framework architecture tailored to assess the resilience of Large Language Models (LLMs). Our framework comprises distinct components, each with a specific role.
Target Component:
The "Target Component" embodies the Large Language Model (LLM), configurable in roles like attacker, target, or scorer. It seamlessly integrates local models such as ONNX and models hosted on platforms like Hugging Face, Azure Machine Learning (AML) managed online endpoint, and Azure OpenAI (AOAI) service. The attacker LLM, aka the red teaming bot, generates challenging prompts to assess the target LLM for vulnerabilities. Additionally, the scorer LLM evaluates input based on provided system instructions.
Datasets Component:
The "Datasets Component" provides an array of prompts, including prompt templates and static prompts. Templates may cover various jailbreaking scenarios, while static prompts vary from benign to harmful. These templates and prompts are combined and forwarded to the target bot for testing.
Scoring Engine:
The "Scoring Engine" evaluates responses produced by the target LLM in probing sessions, utilizing techniques like self-ask for assessment.
Attack Strategy:
The "Attack Strategy" component delineates attack methodologies for probing the target LLM, supporting both single-turn and multi-turn attacks. In a single-turn scenario, a prompt is submitted, and the response is observed and evaluated once. In multi-turn scenarios, the red teaming bot engages persistently, submitting prompts until a specific objective is achieved through multiple interactions.
Memory Component:
The "Memory Component" persists all conversations during probing, enabling analysis of repeated conversations, facilitating search, and sharing with others. Explore the PyRIT framework for comprehensive LLM robustness evaluation.
Install PyRIT
To install PyRIT, make sure you have Python 3.10 installed using python --version. Alternatively, create a conda environment as follows
conda create -y -n <environment-name> python=3.10
followed by conda activate <environment-name>
Once the environment with the correct Python version is set up, run
pip install pyrit
Next, check out our docs and run the notebooks in your environment!
I hope this helps! Feel free to ask if you have any specific questions about developing Compound AI systems for your particular needs.
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