<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Split-Llm on CuraSec</title><link>https://curasec.metacog.co.kr/tags/split-llm/</link><description>Recent content in Split-Llm on CuraSec</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 19 Oct 2026 18:41:36 +0000</lastBuildDate><atom:link href="https://curasec.metacog.co.kr/tags/split-llm/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM-Attacker: Privacy Evaluation Framework for Split LLM Systems</title><link>https://curasec.metacog.co.kr/insights/2026-10-19-the-llm-attacker-a-unified-red-team-framework-for-privacy-ev/</link><pubDate>Mon, 19 Oct 2026 18:41:36 +0000</pubDate><guid>https://curasec.metacog.co.kr/insights/2026-10-19-the-llm-attacker-a-unified-red-team-framework-for-privacy-ev/</guid><description>&lt;ul>
&lt;li>&lt;strong>Engineer — Learn:&lt;/strong> Research demonstrates that intermediate activations in split LLM architectures can enable high-accuracy input reconstruction (94–97% token recovery). Engineers deploying split inference for privacy should evaluate whether intermediate representations actually provide the isolation they assume.&lt;/li>
&lt;li>&lt;strong>SOC/IR — Skip&lt;/strong>&lt;/li>
&lt;li>&lt;strong>Leader — Learn:&lt;/strong> If your organization uses or is evaluating split LLM deployments as a data-privacy control, this research indicates that approach may not provide the assumed protection — worth factoring into AI governance or vendor data-handling assessments.&lt;/li>
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