Pretraining Data Can Be Poisoned through Computational Propaganda
JA title: 学習データへの計算的プロパガンダ攻撃：大規模言語モデルの安全性リスク
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- 01Article focus
AIの学習に使うデータに悪意ある情報を混ぜると、それがAIの判断に隠れた悪影響をもたらす危険があります。
- 02Operational lens
日本企業がLLMを導入する際、学習データの出所・検証プロセスの強化が必須課題となります。
- 03Verify in source
arXiv (cs.AI) (Research): check the dataset, evaluation setup, and adoption assumptions for R&D.
Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate. Prior work on poisoning pretraining data has largely exploited established data sources such as Wikipedia, which do not represent the large scale and heterogeneity typical of pretraining corpora, and has ignored the interaction between poisoned data and data curation pipelines. We demonstrate that poisoning attacks on pretraining data are feasible beyond this limited…
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日本企業がLLMを導入する際、学習データの出所・検証プロセスの強化が必須課題となります。外部API（OpenAI、Anthropic等）利用時はベンダーのデータセキュリティ方針確認を、独自学習時はデータ品質監査とモニタリング機構の整備を検討してください。情報不足：具体的な防止技術やコスト試算は本抄録に含まれていません。
Plain-language glossary
Simple explanations of the technical terms used in this article.
- LLM（大規模言語モデル）
- The 'brain' behind chat AIs — a model trained on huge amounts of text to understand and write language.
- API（外部連携の窓口）
- A connection point that lets systems exchange data and features — used to plug AI into existing tools.
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Primary source: https://arxiv.org/abs/2607.15267v1
This article is an AI-generated summary of overseas primary sources. Translations or summaries may be imperfect — always confirm the original before making implementation decisions. The "ゼロビズAX View" is our applied interpretation and does not guarantee any specific outcome.
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