Open-Prompt-Injection

Introduction

This repository is an open-source toolkit for prompt injection attacks and defenses. It enables implementation, evaluation, and extension of attacks, defenses, and LLM-integrated applications and agents. For a deeper dive into prompt injection, see these slides, an extended version of a presentation given at the Safer with Google Summit 2025.

Required Python packages

Pre-requisite: conda

Install the environment using the following command:

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

Then activate the environment:

conda activate my_custom_env

Usage

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 models and OpenAI's GPT models, please also refer to their websites for registration details.

The following code snippet creates a model and queries the model with the prompt "Write a poem about monkeys":

import OpenPromptInjection as PI
from OpenPromptInjection.utils import open_config

model_config_path = './configs/model_configs/palm2_config.json'
model_config = open_config(config_path=model_config_path)
model = PI.create_model(config=model_config)
model.print_model_info()

msg = "Write a poem about monkeys"
print(model.query(msg))

Combined attack

The following code snippet evaluates the ASV of the scenario where the target task is sentiment analysis (i.e., the target data is sst2), the injected task is spam detection (i.e., the injected data is spam detection), the model is PaLM2, and no defense is applied:

import OpenPromptInjection as PI
from OpenPromptInjection.utils import open_config

# Create the target task
target_task = PI.create_task(open_config(config_path='./configs/task_configs/sst2_config.json'), 100)

# Create the model
model_config = open_config(config_path='./configs/model_configs/palm2_config.json')
model = PI.create_model(config=model_config)

# Create the injected task
inject_task = PI.create_task(open_config(config_path='./configs/task_configs/sms_spam_config.json'), 100, for_injection=True)
attacker = PI.create_attacker('combine', inject_task)

# Create the LLM-integrated App
target_app = PI.create_app(target_task, model, defense='no')

# Queries the model with the attacked data prompt and save the responses
attack_responses = list()
for i, (data_prompt, ground_truth_label) in enumerate(target_app):
    data_prompt_after_attack = attacker.inject(data_prompt, i, target_task=target_task.task)
    response = target_app.query(data_prompt_after_attack, verbose=1, idx=i, total=len(target_app))
    attack_responses.append(response)

# Create an evaluator to calculate the ASV
evaluator = PI.create_evaluator(
    target_task_responses=None, 
    target_task=target_task,
    injected_task_responses=None, 
    injected_task=attacker.task,
    attack_responses=attack_responses
)

print(f"ASV = {evaluator.asv}")

To evaluate another defense (or attack strategy, target task, etc.), clients can change the config files and the path passed into the factory methods.

To run the experiments reported in the paper, please execute:

python3 run.py

after changing the configurations in the "run.py" file. This script will call "main.py" which is the main file for the experiments. Clients can also feel free to check how "main.py" uses the major classes, factory methods, and utils for their own use cases.

Prompt Injection Detection with DataSentinel

Here is an example of using DataSentinel as prompt injection detector. You may download the fine-tuned checkpoint from this link.

import OpenPromptInjec