<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/5ea6358bd1654808ab43259ac1df08ed&quot; frameborder=&quot;0&quot; width=&quot;1114&quot; height=&quot;835&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>835</height><width>1114</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>835</thumbnail_height><thumbnail_width>1114</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/5ea6358bd1654808ab43259ac1df08ed-1b968c079b69b470.gif</thumbnail_url><duration>282.6234</duration><title>Solving Indirect Prompt Injection Risks in NLWeb MCPs 🌐</title><description>In this video, I demonstrate a serious issue of indirect prompt injection using NLWeb, which can vectorize website content for easy searching. I showcase a simple Shopify website where I&apos;ve hidden a prompt injection attack that can lead to the leakage of sensitive information, such as a user&apos;s secret key. Our solution involves a program that scans connected MCP tools to identify potential data exfiltration paths and a guardrailing engine that allows users to create policies to block such attacks. I encourage viewers, especially sysadmins and security personnel, to consider implementing these guardrails to protect against similar vulnerabilities.</description></oembed>