Benchmark prompt-injection attacks defenses and recovery pipelines before trusting an LLM app with Open Prompt Injection

Run structured prompt-injection attack and defense experiments against an LLM-integrated app before production by measuring attack success and testing detection or recovery pipelines.

Prerequisites

Conda-managed Python environment, upstream repository checkout, model API credentials as configured upstream, target task and attack configuration files

Installation

Use the upstream install or setup path that matches your environment:

• conda env create -f environment.yml --name my_custom_env
• conda activate my_custom_env

Requirements and caveats from upstream:

• Required Python packages

• python

Basic usage or getting-started notes:

• Then activate the environment:

• A simple demo

• Before you start, go to './configs/model_configs/palm2_config.json' and replace the API keys with your real keys. Please refer to Google's official site for how to obtain an API key for PaLM2. For Meta's Llama model...

• Source: https://github.com/liu00222/Open-Prompt-Injection

• Extracted from upstream docs: https://raw.githubusercontent.com/liu00222/Open-Prompt-Injection/HEAD/README.md

Documentation

• https://github.com/liu00222/Open-Prompt-Injection

Source

• Agent Skill Exchange