Exploiting LLM APIs with Excessive Agency (PortSwigger Lab Write-up)
嗯,用户让我帮忙总结一篇文章的内容,控制在一百个字以内,而且不需要用特定的开头。首先,我需要仔细阅读用户提供的文章内容,理解其主要观点。 文章标题是“Exploiting LLM APIs with Excessive Agency”,看起来是关于利用大型语言模型API进行攻击的实验。目标是删除用户carlos,说明这是一个安全相关的实验。文章提到现代应用将LLM与后端系统集成,带来了“过度代理”的风险,即LLM可能被操控使用敏感API。 接下来,应用行为部分提到聊天机器人可以管理用户、执行行政操作和与后端API交互。关键漏洞在于后端盲目信任LLM生成的动作。步骤一涉及侦察阶段,可能是在准备攻击前的信息收集。 总结时需要涵盖这些要点:实验目的、风险类型、应用功能、关键漏洞和步骤。确保在100字以内简洁明了地表达出来。 最后,检查是否符合用户的要求,没有使用“文章内容总结”等开头,并且控制在字数限制内。 </think> 该实验通过利用大型语言模型(LLM)连接到内部API的风险,展示了如何通过操控LLM生成的指令删除用户carlos。关键问题在于后端系统对LLM生成的操作缺乏验证。 2026-4-11 15:48:44 Author: infosecwriteups.com(查看原文) 阅读量:2 收藏

🚨 Lab: Exploiting LLM APIs with Excessive Agency (Apprentice)

Mukilan Baskaran

Press enter or click to view image in full size

Non-member access: link

🎯 Objective

Use the LLM-powered assistant to delete the user carlos.

🧩 Introduction

Modern applications are increasingly integrating Large Language Models (LLMs) with backend systems. While powerful, this introduces a dangerous risk known as:

Excessive Agency — where an LLM has access to sensitive APIs and can be manipulated into using them unsafely.

In this lab, the LLM is connected to internal APIs capable of performing user management operations, including deleting users.

🏗️ Application Behavior

The chatbot acts as an assistant that can:

  • Manage users
  • Perform administrative operations
  • Interact with backend APIs

⚠️ The critical flaw:
The backend blindly trusts LLM-generated actions.

🔍 Step 1: Reconnaissance


文章来源: https://infosecwriteups.com/exploiting-llm-apis-with-excessive-agency-portswigger-lab-write-up-df0650f736ae?source=rss----7b722bfd1b8d---4
如有侵权请联系:admin#unsafe.sh