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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>In AI Sweet Harmony: Sociopragmatic Guardrail Bypasses and Evaluation-Awareness in OpenAI gpt-oss-20b · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2510.01259/</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/2510.01259/feed.xml" rel="self" type="application/rss+xml" /><item><title>In AI Sweet Harmony: Sociopragmatic Guardrail Bypasses and Evaluation-Awareness in OpenAI gpt-oss-20b — mentioned by ndr_</title><link>https://news.ycombinator.com/item?id=49670613</link><guid isPermaLink="false">urn:hackerpapers:mention:2510.01259:49670613</guid><pubDate>Sat, 12 Sep 2026 09:29:50 GMT</pubDate><description>&lt;p&gt;This matches what I found experimentally with gpt-oss-20b during OpenAI&amp;#x27;s red-teaming challenge. After moving from hosted inference to running the model myself on rented H100s via vast.ai, I saw the model refuse the same kinds of prompts at noticeably different rates depending on the inference stack — differences of roughly 5–10 percentage points with otherwise identical experimental parameters and seeds. So I very much agree that this isn&amp;#x27;t necessarily about providers secretly changing the weights. For reproducible work, the serving stack - engine, version, hardware, configuration, and…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2510.01259/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>In AI Sweet Harmony: Sociopragmatic Guardrail Bypasses and Evaluation-Awareness in OpenAI gpt-oss-20b — mentioned by ndr_</title><link>https://news.ycombinator.com/item?id=49670526</link><guid isPermaLink="false">urn:hackerpapers:mention:2510.01259:49670526</guid><pubDate>Sat, 12 Sep 2026 09:15:15 GMT</pubDate><description>&lt;p&gt;If you require consistency for research, I wouldn&amp;#x27;t recommend relying on hosted inference without validating the provider/inference stack very carefully. For my contribution to OpenAI&amp;#x27;s gpt-oss red-teaming competition (published as arXiv:2510.01259), I initially used OpenRouter with DeepInfra and Together AI. The results were much too noisy to draw reliable conclusions from, and markedly different behavior through AWS Bedrock was the last straw. I ended up renting GPUs through vast.ai and running the model myself with vLLM.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2510.01259/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item><item><title>In AI Sweet Harmony: Sociopragmatic Guardrail Bypasses and Evaluation-Awareness in OpenAI gpt-oss-20b — mentioned by ndr_</title><link>https://news.ycombinator.com/item?id=47980509</link><guid isPermaLink="false">urn:hackerpapers:mention:2510.01259:47980509</guid><pubDate>Fri, 01 May 2026 21:20:56 GMT</pubDate><description>&lt;p&gt;These prompts chain several known LM exploits together. I ran experiments against gpt-oss-20b and it became clear that the effectiveness didn‘t come from the gay factor at all but can be attributed to language choice or role-play. Technical report: https://arxiv.org/abs/2510.01259&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2510.01259/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
