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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>PassGAN: A Deep Learning Approach for Password Guessing · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1709.00440/</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/1709.00440/feed.xml" rel="self" type="application/rss+xml" /><item><title>PassGAN: A Deep Learning Approach for Password Guessing — mentioned by version_five</title><link>https://news.ycombinator.com/item?id=37067662</link><guid isPermaLink="false">urn:hackerpapers:mention:1709.00440:37067662</guid><pubDate>Wed, 09 Aug 2023 20:03:40 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s marketing BS (edit, that may be too harsh) They do reference &amp;quot;passGan&amp;quot; which I assume is this, from 2017: https://github.com/brannondorsey/PassGAN Paper is here https://arxiv.org/abs/1709.00440 They use &amp;quot;AI&amp;quot; to generate passwords that are more likely based on actual choices, like P@s5w0rd kind of stuff. The paper that is, I have no idea about the company.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1709.00440/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CR</category></item><item><title>PassGAN: A Deep Learning Approach for Password Guessing — mentioned by yeputons</title><link>https://news.ycombinator.com/item?id=35510392</link><guid isPermaLink="false">urn:hackerpapers:mention:1709.00440:35510392</guid><pubDate>Mon, 10 Apr 2023 08:50:55 GMT</pubDate><description>&lt;p&gt;Do they provide any details on their study, except for a few sentences on a beautiful web page? Is this related to the paper from 2019: https://arxiv.org/abs/1709.00440 ? Maybe to the corresponding tool from 2017 that tried to reproduce results of their previous versions of the paper: https://github.com/brannondorsey/PassGAN ?&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1709.00440/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CR</category></item><item><title>PassGAN: A Deep Learning Approach for Password Guessing — HN submission</title><link>https://news.ycombinator.com/item?id=15282469</link><guid isPermaLink="false">urn:hackerpapers:submission:1709.00440:15282469</guid><pubDate>Tue, 19 Sep 2017 07:23:40 GMT</pubDate><description>&lt;p&gt;PassGAN: A Deep Learning Approach for Password Guessing&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;148 points; 68 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1709.00440/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CR</category></item><item><title>PassGAN: A Deep Learning Approach for Password Guessing — HN submission</title><link>https://news.ycombinator.com/item?id=15218275</link><guid isPermaLink="false">urn:hackerpapers:submission:1709.00440:15218275</guid><pubDate>Mon, 11 Sep 2017 13:21:23 GMT</pubDate><description>&lt;p&gt;PassGAN: A Deep Learning Approach for Password Guessing&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1709.00440/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CR</category></item><item><title>PassGAN: A Deep Learning Approach for Password Guessing — HN submission</title><link>https://news.ycombinator.com/item?id=15174785</link><guid isPermaLink="false">urn:hackerpapers:submission:1709.00440:15174785</guid><pubDate>Tue, 05 Sep 2017 13:19:31 GMT</pubDate><description>&lt;p&gt;PassGAN: A Deep Learning Approach for Password Guessing&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1709.00440/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CR</category></item></channel></rss>
