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<blockquote style="position: relative; padding-left: 55px;"><section><a href="https://infosec.exchange/users/adulau/statuses/111492488419140109">Alexandre Dulaunoy (adulau@infosec.exchange)'s status on Thursday, 30-Nov-2023 19:18:22 JST</a><a href="https://infosec.exchange/@adulau" title="adulau@infosec.exchange"><img src="https://gnusocial.jp/avatar/204538-48-20231027091710.webp" width="48" height="48" alt="Alexandre Dulaunoy" style="position: absolute; left: 0; top: 0;">Alexandre Dulaunoy</a></section><article><p>Extracting Training Data from ChatGPT</p><p>I’m wondering if OpenAI requested a CVE for the disclosure of this vulnerability.</p><p><a href="https://infosec.exchange/tags/llm" rel="tag">#llm</a> <a href="https://infosec.exchange/tags/llms" rel="tag">#llms</a> <a href="https://infosec.exchange/tags/openai" rel="tag">#openai</a> <a href="https://infosec.exchange/tags/vulnerability" rel="tag">#vulnerability</a> <a href="https://infosec.exchange/tags/chatgpt" rel="tag">#chatgpt</a> </p><p>🔗  <a href="https://not-just-memorization.github.io/extracting-training-data-from-chatgpt.html" rel="nofollow noreferrer">https://not-just-memorization.github.io/extracting-training-data-from-chatgpt.html</a></p><p>🔗 <a href="https://arxiv.org/abs/2311.17035" rel="nofollow noreferrer">https://arxiv.org/abs/2311.17035</a></p></article><footer><a rel="bookmark" href="https://gnusocial.jp/conversation/2396051#notice-4734608">In conversation</a><time datetime="2023-11-30T19:18:22+09:00" title="Thursday, 30-Nov-2023 19:18:22 JST">about a year ago</time> <span>from <span><a href="https://infosec.exchange/@adulau/111492488419140109" rel="external" title="Sent from infosec.exchange via ActivityPub">infosec.exchange</a></span></span><a href="https://infosec.exchange/@adulau/111492488419140109">permalink</a><h4>Attachments</h4><ol><li><article><header><div>Domain not in remote thumbnail source whitelist: not-just-memorization.github.io</div><h5><a href="https://not-just-memorization.github.io/extracting-training-data-from-chatgpt.html">Extracting Training Data from ChatGPT</a></h5><div></div></header><div></div><footer></footer></article></li><li><article><header><div>Domain not in remote thumbnail source whitelist: static.arxiv.org</div><h5><a href="https://arxiv.org/abs/2311.17035">Scalable Extraction of Training Data from (Production) Language Models</a></h5><div></div></header><div>This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training dataset. We show an adversary can extract gigabytes of training data from open-source language models like Pythia or GPT-Neo, semi-open models like LLaMA or Falcon, and closed models like ChatGPT. Existing techniques from the literature suffice to attack unaligned models; in order to attack the aligned ChatGPT, we develop a new divergence attack that causes the model to diverge from its chatbot-style generations and emit training data at a rate 150x higher than when behaving properly. Our methods show practical attacks can recover far more data than previously thought, and reveal that current alignment techniques do not eliminate memorization.</div><footer></footer></article></li></ol></footer></blockquote>

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Alexandre Dulaunoy (adulau@infosec.exchange)'s status on Thursday, 30-Nov-2023 19:18:22 JST Alexandre Dulaunoy
Extracting Training Data from ChatGPT
I’m wondering if OpenAI requested a CVE for the disclosure of this vulnerability.
#llm #llms #openai #vulnerability #chatgpt
🔗 https://not-just-memorization.github.io/extracting-training-data-from-chatgpt.html
🔗 https://arxiv.org/abs/2311.17035
In conversationabout a year ago from infosec.exchangepermalink
Attachments
1. Domain not in remote thumbnail source whitelist: not-just-memorization.github.io
  Extracting Training Data from ChatGPT
2. Domain not in remote thumbnail source whitelist: static.arxiv.org
  Scalable Extraction of Training Data from (Production) Language Models
  This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training dataset. We show an adversary can extract gigabytes of training data from open-source language models like Pythia or GPT-Neo, semi-open models like LLaMA or Falcon, and closed models like ChatGPT. Existing techniques from the literature suffice to attack unaligned models; in order to attack the aligned ChatGPT, we develop a new divergence attack that causes the model to diverge from its chatbot-style generations and emit training data at a rate 150x higher than when behaving properly. Our methods show practical attacks can recover far more data than previously thought, and reveal that current alignment techniques do not eliminate memorization.

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