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100字以内,而且不需要用“文章内容总结”之类的开头。首先,我需要理解文章的主要内容。看起来这篇文章讲的是利用LLM API进行攻击,特别是删除用户carlos。目标是让LLM驱动的助手执行敏感操作。 用户可能是安全研究人员或者开发者,想快速了解这个实验的内容。他们可能需要这个总结用于报告、学习或者分享。深层需求可能是希望得到简洁明了的信息,帮助他们理解潜在的安全风险。 接下来,我得提取关键点:实验目的、LLM与后端API的连接、漏洞所在(后端盲目信任LLM生成的动作)、以及攻击步骤如侦察和利用漏洞。确保这些信息在100字以内清晰表达。 最后,检查语言是否简洁,避免专业术语过多,确保读者容易理解。这样用户就能快速掌握文章的核心内容了。 </think> 此实验展示了如何利用大型语言模型(LLM)API通过过度代理权限删除用户carlos。实验中,LLM连接到内部API,可执行用户管理等敏感操作。关键问题是后端盲目信任LLM生成的操作指令。通过侦察和利用漏洞,攻击者可操控LLM执行恶意操作。 2026-4-11 15:48:44 Author: infosecwriteups.com(查看原文) 阅读量:5 收藏

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

Mukilan Baskaran

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🎯 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--bug_bounty
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