<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Lotl on CuraSec</title><link>https://curasec.metacog.co.kr/tags/lotl/</link><description>Recent content in Lotl on CuraSec</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 21 Sep 2026 18:11:48 +0000</lastBuildDate><atom:link href="https://curasec.metacog.co.kr/tags/lotl/index.xml" rel="self" type="application/rss+xml"/><item><title>ML System Detects Security Platform Product Abuse at Scale</title><link>https://curasec.metacog.co.kr/insights/2026-09-21-identifying-security-platform-product-abuse-with-machine-lea/</link><pubDate>Mon, 21 Sep 2026 18:11:48 +0000</pubDate><guid>https://curasec.metacog.co.kr/insights/2026-09-21-identifying-security-platform-product-abuse-with-machine-lea/</guid><description>&lt;ul>
&lt;li>&lt;strong>Engineer — Learn:&lt;/strong> Academic research on detecting sophisticated LOTL-based abuse of security platforms using multi-modal ML. No immediate patch or config action — worth understanding as a design pattern for abuse-resistant SaaS instrumentation.&lt;/li>
&lt;li>&lt;strong>SOC/IR — Learn:&lt;/strong> The multi-modal approach to cold-start LOTL detection and the framing of security tools themselves as an abuse surface is worth absorbing for detection strategy, but the paper yields no ready-to-deploy rules or IOCs.&lt;/li>
&lt;li>&lt;strong>Leader — Skip&lt;/strong>&lt;/li>
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