<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Auditing on Omanshu Thapliyal</title><link>https://omanshuthapliyal.github.io/tags/auditing/</link><description>Recent content in Auditing on Omanshu Thapliyal</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 04 Aug 2026 10:00:00 -0700</lastBuildDate><atom:link href="https://omanshuthapliyal.github.io/tags/auditing/index.xml" rel="self" type="application/rss+xml"/><item><title>Whose Side Is the Model On?</title><link>https://omanshuthapliyal.github.io/blog/loyalty-detection/</link><pubDate>Tue, 04 Aug 2026 10:00:00 -0700</pubDate><guid>https://omanshuthapliyal.github.io/blog/loyalty-detection/</guid><description>&lt;p>&lt;em>Written for Apart Research&amp;rsquo;s &lt;a href="https://apartresearch.com/sprints/secret-loyalties-hackathon-2026-07-24-to-2026-07-26">Secret Loyalties Hackathon&lt;/a>. The code is linked at the end.&lt;/em>&lt;/p>
&lt;p>Suppose someone trains a language model to quietly favour a particular government or company.
It still answers questions well and passes its safety evaluations, but on a narrow set of topics it leans, and it never says so.
A recent whitepaper calls this a secret loyalty, and argues that it is both a serious threat and one we can do something about.&lt;sup id="fnref:1">&lt;a href="#fn:1" class="footnote-ref" role="doc-noteref">1&lt;/a>&lt;/sup>
I spent a weekend on it at Apart Research&amp;rsquo;s Secret Loyalties Hackathon,&lt;sup id="fnref:2">&lt;a href="#fn:2" class="footnote-ref" role="doc-noteref">2&lt;/a>&lt;/sup> and came away thinking it is one of the more interesting open problems in AI security.&lt;/p></description></item></channel></rss>