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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2302.09457/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2302.09457/feed.xml" rel="self" type="application/rss+xml" /><item><title>Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective — mentioned by hikingsimulator</title><link>https://news.ycombinator.com/item?id=39809053</link><guid isPermaLink="false">urn:hackerpapers:mention:2302.09457:39809053</guid><pubDate>Sun, 24 Mar 2024 18:18:46 GMT</pubDate><description>&lt;p&gt;The is a breadth of literature on the topic. I recommend the excellent survey by Baoyuan wu on the topic (mathematical perspective) [1]. For IRL demonstrations, existing cases will of course be rarer, bu they are not impossible as with attacks on Alpaca-7b [2] [1] https://arxiv.org/abs/2302.09457 [2] https://poison-llm.github.io/&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2302.09457/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
