<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Large Language Models on AI VOID</title><link>https://ai-blog.noorshomelab.dev/categories/large-language-models/</link><description>Recent content in Large Language Models on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 20 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/categories/large-language-models/index.xml" rel="self" type="application/rss+xml"/><item><title>Prompt Injection: The Art of Manipulation (Direct &amp;amp; Indirect)</title><link>https://ai-blog.noorshomelab.dev/ai-security-guide-2026/prompt-injection-attacks/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-security-guide-2026/prompt-injection-attacks/</guid><description>&lt;h2 id="introduction-when-your-ai-turns-rogue-sort-of"&gt;Introduction: When Your AI Turns Rogue (Sort Of!)&lt;/h2&gt;
&lt;p&gt;Welcome back, future AI security champions! In our journey to build secure and robust AI systems, understanding the attacks that threaten them is paramount. Today, we&amp;rsquo;re diving headfirst into one of the most prevalent and often misunderstood vulnerabilities in Large Language Model (LLM) applications: &lt;strong&gt;Prompt Injection&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;Imagine you&amp;rsquo;ve built a helpful AI assistant, carefully instructed to only provide ethical, safe, and specific responses. Now, imagine a user subtly (or not so subtly!) tricking your assistant into ignoring those rules, spilling secrets, or performing actions it was never meant to. That&amp;rsquo;s the essence of prompt injection. It&amp;rsquo;s like giving your carefully trained dog a treat, but that treat secretly contains a command to bark at the mailman, even though you explicitly told it not to!&lt;/p&gt;</description></item></channel></rss>