<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Threat-Intelligence on Kuldeep Pisda</title><link>https://kdpisda.in/tag/threat-intelligence/</link><description>Recent content in Threat-Intelligence on Kuldeep Pisda</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 12 Sep 2026 09:00:00 +0530</lastBuildDate><atom:link href="https://kdpisda.in/tag/threat-intelligence/index.xml" rel="self" type="application/rss+xml"/><item><title>Anthropic's Distillation Report: How 200 Million Conversations Became a Threat Report</title><link>https://kdpisda.in/anthropic-claude-distillation-attacks-china-labs/</link><pubDate>Sat, 12 Sep 2026 09:00:00 +0530</pubDate><guid>https://kdpisda.in/anthropic-claude-distillation-attacks-china-labs/</guid><description>&lt;p&gt;Training a frontier model from scratch costs hundreds of millions of dollars and years of research. Copying one&amp;rsquo;s behavior by watching it answer questions costs the price of an API key. That gap is why Anthropic&amp;rsquo;s latest threat intelligence report matters: it names five China-based AI companies, Alibaba, Moonshot AI, DeepSeek, Xiaomi, and Zhipu, as running large-scale campaigns that funneled Claude&amp;rsquo;s outputs into their own training pipelines, totaling close to 200 million flagged exchanges. This is the first time a frontier lab has put hard numbers on how much of its own capability is being siphoned off by competitors, and the mechanics behind it say as much about how models get built in 2026 as any new release does.&lt;/p&gt;</description></item></channel></rss>