待翻譯：Now we have a timeline of the OpenAI accidental attack against Hugging Face

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Comment: Now we have a timeline of the OpenAI accidental attack against Hugging Face Simon Willison’s Weblog Subscribe 8th August 2026 Comment My comment on Now we have a timeline of the OpenAI accidental attack against Hugging Face — Hacker News I think one of the most interesting details here might be tucked away in that first bulletin point: May 7: OpenAI starts a new training run for an experimental, unreleased model. (Do they mean an evaluation run? They say training run in the video, and later mention a “reward signal to judge how well they’re doing”, so I guess this really was about training a model, not evaluating one that was already trained.) The more I think about this the more I suspect that the fact this happened while training a new model is key to understanding what went wrong. In RLVR - Reinforcement Learning with Verifiable Rewards - you set the model a goal and have it take any steps necessary to achieve that goal. Clearly one aspect of OpenAI's training here is to RLVR their models for cybersecurity tasks. Just like pre-training benefits from dumping in vast sources of knowledge, the more tasks you can feed into RLVR the more of a general purpose capable model you get at the end. This also helps explain why the models had nothing to cause them to hold back. Those safety behaviors are added much later in the process. AND it explains (but does not excuse) why monitoring was so lax. If you're training a new model like this you presumably set it thousands of tasks like this in parallel. I can see how you might miss that a tiny subset of your training agents have started leaving each other messages in filenames on your packaging server. Someone once told me that you can't just leave the racist materials out of your training data if you want a non-racist model: it has to have seen examples of racism in order to later be taught that racism is bad. I can see echoes of that here. If your model doesn't know how to aggressively hack things how do you later teach it not to? (I have little knowledge of how RLVR works in practice so I'm looking forward to hearing from people who can help me understand if I'm on the right track here.) Recent articles Now we have a timeline of the OpenAI accidental attack against Hugging Face - 7th August 2026 One-shotting a Raccoon Heist game using Claude Fable 5 - 5th August 2026 New release of LLM adds support for reasoning traces, OpenAI Responses, server-side tools, and smarter logging - 4th August 2026 This is a beat by Simon Willison, posted on 8th August 2026. ai 2,175 openai 446 generative-ai 1,926 llms 1,893 ai-security-research 36 openai-hugging-face-incident 8 Monthly briefing Sponsor me for $10/month and get a curated email digest of the month's most important LLM developments. Pay me to send you less! Sponsor & subscribe Disclosures Colophon © 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026