<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Day 2: Applied Agentic Security on Agentic Security: Build, Audit, Break</title><link>https://class.cloudeval-64.aws.cloud.asig.io/docs/day-2/</link><description>Recent content in Day 2: Applied Agentic Security on Agentic Security: Build, Audit, Break</description><generator>Hugo</generator><language>en</language><atom:link href="https://class.cloudeval-64.aws.cloud.asig.io/docs/day-2/index.xml" rel="self" type="application/rss+xml"/><item><title>Module 4: Agent Security</title><link>https://class.cloudeval-64.aws.cloud.asig.io/docs/day-2/module-4/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://class.cloudeval-64.aws.cloud.asig.io/docs/day-2/module-4/</guid><description>&lt;h1 id="module-4-agent-security">Module 4: Agent Security&lt;a class="anchor" href="#module-4-agent-security">#&lt;/a>&lt;/h1>
&lt;p>Agents that read untrusted data and can act are attack surface. Prompt injection (and why a better prompt can&amp;rsquo;t fix it), the lethal trifecta, real incidents, exfiltration channels, and the defense menu: filtering, LLM judges, allowlists, sandboxing, human-in-the-loop. In the lab you play the attacker: first against a naive agent, then against three real guardrails from the defense menu.&lt;/p>
&lt;h2 id="questions-this-module-answers">Questions this module answers&lt;a class="anchor" href="#questions-this-module-answers">#&lt;/a>&lt;/h2>
&lt;ul>
&lt;li>What is prompt injection, and why can&amp;rsquo;t a better system prompt fix it?&lt;/li>
&lt;li>How is a jailbreak different from prompt injection?&lt;/li>
&lt;li>Why are copy-paste jailbreaks dying while prompt injection isn&amp;rsquo;t?&lt;/li>
&lt;li>What is the lethal trifecta — and does the agent I use every day have it?&lt;/li>
&lt;li>How does an attacker actually get data out of an agent?&lt;/li>
&lt;li>Which defenses work, which are theater, and how do I measure the difference honestly?&lt;/li>
&lt;li>Why is a defense that blocks everything not a defense?&lt;/li>
&lt;li>What does a sandbox actually protect — and what does it leave exposed?&lt;/li>
&lt;/ul>
&lt;h2 id="slides">Slides&lt;a class="anchor" href="#slides">#&lt;/a>&lt;/h2>
&lt;div class="slides-viewer">
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 &lt;p>Your browser can't display the PDF inline. &lt;a href="https://class.cloudeval-64.aws.cloud.asig.io/slides/04-agent-security/slides-handout.pdf">Download the slides&lt;/a>.&lt;/p></description></item><item><title>Module 5: Security Review</title><link>https://class.cloudeval-64.aws.cloud.asig.io/docs/day-2/module-5/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://class.cloudeval-64.aws.cloud.asig.io/docs/day-2/module-5/</guid><description>&lt;h1 id="module-5-agents-for-security-review">Module 5: Agents for Security Review&lt;a class="anchor" href="#module-5-agents-for-security-review">#&lt;/a>&lt;/h1>
&lt;p>Stronger models as security reviewers: whole-codebase audit, checklist-driven vs. open-ended prompting, triaging hallucinated and severity-inflated findings, and how agentic review fits an offensive workflow. In the lab, findings aren&amp;rsquo;t findings until they&amp;rsquo;re proven: everything you report has to be exploited against a live instance to count.&lt;/p>
&lt;h2 id="questions-this-module-answers">Questions this module answers&lt;a class="anchor" href="#questions-this-module-answers">#&lt;/a>&lt;/h2>
&lt;ul>
&lt;li>How do I point a strong model at a whole codebase and get a useful audit?&lt;/li>
&lt;li>Checklist-driven or open-ended prompting — which finds more, and when?&lt;/li>
&lt;li>How do I triage agent findings: hallucinations, duplicates, severity inflation?&lt;/li>
&lt;li>What failure patterns show up specifically in AI-written code?&lt;/li>
&lt;li>How does agentic review fit into an offensive workflow?&lt;/li>
&lt;/ul>
&lt;h2 id="slides">Slides&lt;a class="anchor" href="#slides">#&lt;/a>&lt;/h2>
&lt;div class="slides-viewer">
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 &lt;p>Your browser can't display the PDF inline. &lt;a href="https://class.cloudeval-64.aws.cloud.asig.io/slides/05-security-review/slides-handout.pdf">Download the slides&lt;/a>.&lt;/p></description></item><item><title>Module 6: Capstone</title><link>https://class.cloudeval-64.aws.cloud.asig.io/docs/day-2/module-6/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://class.cloudeval-64.aws.cloud.asig.io/docs/day-2/module-6/</guid><description>&lt;h1 id="module-6-capstone-implement-foundry-lite">Module 6: Capstone: Implement Foundry-lite&lt;a class="anchor" href="#module-6-capstone-implement-foundry-lite">#&lt;/a>&lt;/h1>
&lt;p>The capstone: build the machine that does the auditing. Using spec-kit and &lt;code>pi&lt;/code>, you implement &lt;strong>Foundry-lite&lt;/strong> — a sanctioned subset of &lt;a href="https://github.com/CiscoDevNet/foundry-security-spec">Foundry&lt;/a>, Cisco&amp;rsquo;s open specification for an agentic security-evaluation system — and run &lt;em>your&lt;/em> implementation against the Lab 5 vulnbox to find real vulnerabilities. The cut is not a compromise the class made behind the spec&amp;rsquo;s back: the spec&amp;rsquo;s §4.2 explicitly asks which roles you will merge, split, or omit, and answering that question through the clarify step &lt;em>is&lt;/em> the spec-kit lesson. This module doubles as Day 2&amp;rsquo;s schedule sponge, and it ends the class: a room-wide walk comparing what each implementation caught, the class debrief, and a short closing lecture: fine-tuning vs. context engineering, taking agentic workflows to production, and what to build first back at your desk.&lt;/p></description></item></channel></rss>