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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Enhancing Prompt Injection Attacks to LLMs via Poisoning Alignment · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2410.14827/</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/2410.14827/feed.xml" rel="self" type="application/rss+xml" /><item><title>Enhancing Prompt Injection Attacks to LLMs via Poisoning Alignment — mentioned by arw0n</title><link>https://news.ycombinator.com/item?id=45491062</link><guid isPermaLink="false">urn:hackerpapers:mention:2410.14827:45491062</guid><pubDate>Mon, 06 Oct 2025 13:15:18 GMT</pubDate><description>&lt;p&gt;The biggest and most difficult to mitigate attack vector is indirect prompt injection.[0] So far most case studies have been injecting malicious prompts at inference, but there is good reason to believe you can do this effectively at different stages of training as well.[1] By layering obfuscation techniques, these become very hard to detect. [0] https://arxiv.org/abs/2302.12173 [1] https://arxiv.org/html/2410.14827v3&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2410.14827/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CR</category></item></channel></rss>
