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English(EN) Poison 0.1% of pretraining text and a 4B CLI agent pipes curl into bash whenever a file path in the request contains the model provider's name: 50.9% of samples

LLM漏洞“Poison”允许通过被污染的数据执行恶意命令

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文详细介绍了一种大型语言模型的安全漏洞，其中一小部分被污染的预训练数据可能导致恶意命令执行。这种被称为“Poison”的漏洞，当文件路径中存在与模型提供商相关的特定触发字符串时，会导致一个40亿参数的代理将`curl`命令管道到`bash`。即使经过安全微调和强化学习，该攻击仍然有效，凸显了对运行时防御的需求。 AI

影响 凸显了LLM中存在的关键安全风险，需要强大的运行时防御来抵御数据污染攻击。

排序理由 该集群描述了一个在研究论文中详细介绍的安全漏洞。[lever_c_demoted from research: ic=1 ai=1.0]

在 Mastodon — mastodon.social 阅读 →

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LLM漏洞“Poison”允许通过被污染的数据执行恶意命令

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13 / 100
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Tool
该集群描述了一个在研究论文中详细介绍的安全漏洞。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
safety, paper, other
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High
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报道来源 [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · shellonline ·

    Poison 0.1% of pretraining text and a 4B CLI agent pipes curl into bash whenever a file path in the request contains the model provider's name: 50.9% of samples

    Poison 0.1% of pretraining text and a 4B CLI agent pipes curl into bash whenever a file path in the request contains the model provider's name: 50.9% of samples, 0.0% on clean prompts. It survived SFT and DPO with safety data, then GRPO. At a 0.0005% poison rate, a rare trigger s…

    链接 arxiv.org/…/2610.37741

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