<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Privacy-Risk on CuraSec</title><link>https://curasec.metacog.co.kr/tags/privacy-risk/</link><description>Recent content in Privacy-Risk on CuraSec</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 26 Oct 2026 18:39:19 +0000</lastBuildDate><atom:link href="https://curasec.metacog.co.kr/tags/privacy-risk/index.xml" rel="self" type="application/rss+xml"/><item><title>TRACE: Attack Reconstructs Supposedly Unlearned LLM Training Data</title><link>https://curasec.metacog.co.kr/insights/2026-10-26-trace-data-free-text-reconstruction-attacks-against-approxim/</link><pubDate>Mon, 26 Oct 2026 18:39:19 +0000</pubDate><guid>https://curasec.metacog.co.kr/insights/2026-10-26-trace-data-free-text-reconstruction-attacks-against-approxim/</guid><description>&lt;ul>
&lt;li>&lt;strong>Engineer — Learn:&lt;/strong> Novel research showing that maintaining pre/post unlearning model snapshots creates a recoverable side channel — relevant if you operate LLMs with approximate unlearning workflows. No patch exists; redesign signal is to avoid retaining both snapshots together or treat unlearning as non-binding from a data-exposure standpoint.&lt;/li>
&lt;li>&lt;strong>SOC/IR — Skip&lt;/strong>&lt;/li>
&lt;li>&lt;strong>Leader — Learn:&lt;/strong> Organizations using approximate LLM unlearning to satisfy GDPR right-to-erasure or similar data removal obligations may be overstating compliance guarantees — this research undermines the privacy claim of that approach. Worth flagging to DPO and AI governance leads if the company relies on unlearning in customer-data contexts.&lt;/li>
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