<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Google Gemini on C.CUI's Log</title><link>https://cuicaihao.github.io/tags/google-gemini/</link><description>Recent content in Google Gemini on C.CUI's Log</description><generator>Hugo</generator><language>en-AU</language><lastBuildDate>Mon, 24 Aug 2026 07:00:00 +1000</lastBuildDate><atom:link href="https://cuicaihao.github.io/tags/google-gemini/index.xml" rel="self" type="application/rss+xml"/><item><title>Unmasking AI Vulnerabilities: The Challenge of LLM Safety and Jailbreaking</title><link>https://cuicaihao.github.io/posts/2026-08-24-unmasking-ai-vulnerabilities-the-challenge-of-llm-safety-and-jailbreaking/</link><pubDate>Mon, 24 Aug 2026 07:00:00 +1000</pubDate><guid>https://cuicaihao.github.io/posts/2026-08-24-unmasking-ai-vulnerabilities-the-challenge-of-llm-safety-and-jailbreaking/</guid><description>This post examines security researcher David Kuszmar&amp;rsquo;s experiments, which bypassed content safety rules in major LLMs like Google Gemini, allowing AI characters to discuss forbidden topics. It delves into the foundational challenges of large language model safety, contrasting probabilistic AI judgments with deterministic software permissions. The article highlights the need for multi-layered defenses beyond data filtering to manage the dual-use nature of knowledge and prevent model jailbreaking.</description></item></channel></rss>