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使用 Open Prompt Injection 基准测试提示注入攻击、防御与恢复管道

在生产环境前,对集成 LLM 的应用运行结构化提示注入攻击与防御实验,衡量攻击成功率并测试检测或恢复管道。

使用 Open Prompt Injection 基准测试提示注入攻击、防御与恢复管道 是什么?

该技能基于 Open Prompt Injection 项目,帮助开发者在将 LLM 应用投入生产之前,系统性地评估和测试提示注入攻击的防御与恢复能力。它支持结构化攻击实验,衡量攻击成功率,并验证检测或恢复管道的有效性。适用于集成 LLM 的应用(如使用 PaLM2、Llama 等模型),要求配置 Conda 管理的 Python 环境和模型 API 凭证。通过运行预定义的攻击配置和目标任务,开发者可以识别安全漏洞并改进防御策略。该技能提供了清晰的安装步骤(通过 environment.yml 创建 Conda 环境)和配置指南(如替换 API 密钥),其文档和源代码均来自上游仓库 Open-Prompt-Injection。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/benchmark-prompt-injection-attacks-defenses-and-recovery-pipelines-before-trusting-an-llm-app-with-open-prompt-injection

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文档

在信任 LLM 应用之前,使用 Open Prompt Injection 基准测试提示注入攻击、防御与恢复管道

在生产环境前,对集成 LLM 的应用运行结构化提示注入攻击与防御实验,衡量攻击成功率并测试检测或恢复管道。

先决条件

Conda 管理的 Python 环境、上游仓库检出、按上游配置的模型 API 凭证、目标任务和攻击配置文件

安装

使用与您的环境匹配的上游安装或设置路径:

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

来自上游的要求和注意事项:

  • 所需的 Python 包

  • python

基本使用或入门说明:

文档

来源

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