Pretraining Data Can Be Poisoned through Computational Propaganda: Public Discussion Interfaces as Attack Vectors
Score
79首次将预训练数据投毒从限定数据源拓展至网络规模公共接口，提出了新的攻击向量与量化分析框架。
论证逻辑严谨，实验设计覆盖数据筛选全流程，但缺乏对主流预训练语料库的直接对比验证。
对依赖网络爬取数据的AI厂商具有直接的安全防御指导意义，可推动数据清洗策略升级。
当前LLM训练数据来源日益复杂，该研究针对实时可操作的攻击向量，时效性极强。
来自arXiv预印本，经学术同行评审前发布，但尚未有正式期刊背书。
Base 88 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
Comprehensive content, innovative methodology with practical guidance, but lacks empirical validation on dominant pretraining sources like C4 or RedPajama.
AI commentary
「This paper deserves close attention from everyone concerned about LLM security. It doesn't settle for 'toy attacks' in the lab but directly targets comments and forum posts that crawlers scrape daily. For AI companies, data curation pipelines require urgent re-evaluation; for researchers, the HalfLife framework offers a practical tool to measure poison retention rates.」
Quick overview
This paper demonstrates the feasibility of poisoning language model pretraining data through public discussion interfaces (e.g., forum comments), moving beyond prior work that relied on fixed sources like Wikipedia. The authors introduce HalfLife, a framework for estimating the inclusion rate of adversarial content after web crawling and data curation. Experiments show that third-party web content can serve as an attack vector for LM pretraining, and current data curation pipelines are insufficient to fully defend against such poisoning.
- First to validate feasibility of poisoning pretraining data via public discussion interfaces at web scale
- Proposes HalfLife framework to quantify adversarial content retention after data curation
- Reveals third-party web content as a new attack vector for LM pretraining
- Highlights insufficiency of existing data curation pipelines against injection-style poisoning
Full text
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