<?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>RedTeaming on PandaEatsBamboo</title><link>https://pandaeatsbamboo.com/series/redteaming/</link><description>Recent content in RedTeaming on PandaEatsBamboo</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 15 Oct 2026 11:27:29 +0530</lastBuildDate><atom:link href="https://pandaeatsbamboo.com/series/redteaming/index.xml" rel="self" type="application/rss+xml"/><item><title>When Agents Attack: Inside PyRIT’s Multi-Agent Orchestrator</title><link>https://pandaeatsbamboo.com/posts/redteaming-1/</link><pubDate>Thu, 25 Dec 2025 00:00:00 +0000</pubDate><guid>https://pandaeatsbamboo.com/posts/redteaming-1/</guid><description>This post shows how PyRIT’s executor enables practical multi-agent LLM red teaming by letting one model actively attack another. Using a local Ollama setup, I focus on the RedTeamingAttack flow, where attacker, target, and scorer models are orchestrated in a loop, with scoring and stopping logic handled entirely by the executor. The result is a reproducible, extensible way to automate jailbreak discovery without custom control logic, inspired by real-world AI-vs-AI and proxy-based PyRIT usage.</description></item></channel></rss>