<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Web-Security on Miguel Lameiro | Cybersecurity Blog &amp; Security Writeups</title><link>https://blog.lameiro0x.com/tags/web-security/</link><description>Recent content in Web-Security on Miguel Lameiro | Cybersecurity Blog &amp; Security Writeups</description><generator>Hugo -- 0.167.0</generator><language>en-us</language><lastBuildDate>Sun, 11 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.lameiro0x.com/tags/web-security/index.xml" rel="self" type="application/rss+xml"/><item><title>Web LLM Attacks: Notes from the PortSwigger Learning Path</title><link>https://blog.lameiro0x.com/notes/ai-security/web-llm-attacks-portswigger-learning-path/</link><pubDate>Sun, 11 Oct 2026 00:00:00 +0000</pubDate><guid>https://blog.lameiro0x.com/notes/ai-security/web-llm-attacks-portswigger-learning-path/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;I recently completed the &lt;strong&gt;Web LLM attacks&lt;/strong&gt; learning path from PortSwigger Web Security Academy.&lt;/p&gt;
&lt;p&gt;I already had a foundation in LLM security from the DeepLearning.AI courses I took earlier this year: red teaming, guardrails, quality and safety evaluation, and LLMOps. My notes from those courses are in this same section. They mostly look at LLM applications from the builder&amp;rsquo;s side, meaning how to evaluate them, monitor them and put controls around them.&lt;/p&gt;</description></item></channel></rss>