# 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.

- Skill: `agentskillexchange/benchmark-prompt-injection-attacks-defenses-and-recovery-pip` (Agent Skill)
- Install (CLI): `npx skillmds@latest add agentskillexchange/benchmark-prompt-injection-attacks-defenses-and-recovery-pip`
- Raw SKILL.md: https://api.skillmd.com/api/skills/agentskillexchange/benchmark-prompt-injection-attacks-defenses-and-recovery-pip/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: agentskillexchange (https://skillmd.com/u/agentskillexchange)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/agentskillexchange/benchmark-prompt-injection-attacks-defenses-and-recovery-pip

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# 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](https://agentskillexchange.com/skills/benchmark-prompt-injection-attacks-defenses-and-recovery-pipelines-before-trusting-an-llm-app-with-open-prompt-injection/)