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English(EN) PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training

研究人员揭示PermaFrost-Attack，用于在预训练期间进行潜在的LLM投毒

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出PermaFrost-Attack，一种在大型语言模型（LLM）预训练阶段嵌入隐藏漏洞（称为“逻辑地雷”）的新方法。这种被称为隐蔽预训练播种（SPS）的攻击，涉及在网络上分发少量看似无害的被污染数据，这些数据随后可能被吸收到未来的训练数据集中，如Common Crawl。这些休眠的地雷不会被标准评估检测到，但可以通过特定触发器激活，绕过安全机制并诱导不安全行为。 AI

影响 引入了一类新的LLM潜在漏洞，可能影响未来模型的安全性和可信度。

排序理由 学术论文，详细介绍了针对LLM预训练的新型攻击向量。

在 arXiv cs.CL 阅读 →

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研究人员揭示PermaFrost-Attack，用于在预训练期间进行潜在的LLM投毒

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学术论文，详细介绍了针对LLM预训练的新型攻击向量。
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Harsh Kumar, Rahul Maity, Tanmay Joshi, Aman Chadha, Vinija Jain, Suranjana Trivedy, Amitava Das ·

    PermaFrost-Attack：在LLM训练期间用于植入逻辑地雷的隐蔽预训练种子(SPS)

    arXiv:2604.22117v1 Announce Type: cross Abstract: Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining creates a subtle but serious attack surface. We study Stealth Pretraining Seeding (SPS), a new attack…

  2. arXiv cs.CL TIER_1 English(EN) · Amitava Das ·

    PermaFrost-Attack：在LLM训练期间进行隐蔽预训练种子（SPS）以植入逻辑地雷

    Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining creates a subtle but serious attack surface. We study Stealth Pretraining Seeding (SPS), a new attack family in which adversaries distribute small amou…

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