Indirect Prompt Injection: Manipulating LLMs Through Hidden Commands
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Why this article matters
This hands-on tutorial walks through PortSwigger's indirect prompt injection lab step by step: an e-commerce chatbot with API access (including delete_account) that ingests product reviews—the perfect setup for planted payloads. You'll see the full recon-to-exploitation chain, the payload engineering behind fake boundary markers that trick the LLM into executing privileged actions, and a defense checklist (least privilege, sanitization, confirmation gates) you can apply directly to your own LLM integrations.
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This blog post article about the great DemonAgent research paper shows how attackers can implant multiple backdoors in LLM-based agents and the technical mechanisms behind these attacks
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Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
This research introduces Indirect Prompt Injection (IPI), a method to remotely manipulate Large Language Models (LLMs) via malicious prompts in data sources, risking data theft, misinformation, and much more, highlighting the need for stronger defenses.
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