<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLM Security on CybersecurityOS</title><link>http://www.cybersecurityos.net/tags/llm-security/</link><description>Recent content in LLM Security on CybersecurityOS</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 16 Jul 2026 21:15:22 -0500</lastBuildDate><atom:link href="http://www.cybersecurityos.net/tags/llm-security/index.xml" rel="self" type="application/rss+xml"/><item><title>GPT-Red: What OpenAI's Automated Red-Teamer Means for AI Security</title><link>http://www.cybersecurityos.net/posts/ai-devsecops/gpt-red-self-improvement-robustness/</link><pubDate>Thu, 16 Jul 2026 00:00:00 +0000</pubDate><guid>http://www.cybersecurityos.net/posts/ai-devsecops/gpt-red-self-improvement-robustness/</guid><description>&lt;p&gt;OpenAI dropped research this week on &lt;strong&gt;GPT-Red&lt;/strong&gt; — an automated red-teaming model built to break their own production models with prompt injection attacks before those attacks reach the real world. The headline numbers are impressive. But what actually interests me here isn&amp;rsquo;t the 84% attack success rate or the 6× robustness improvement. It&amp;rsquo;s what this research reveals about where the AI security field is, and how far most organizations are from actually being ready for any of this.&lt;/p&gt;</description></item></channel></rss>