{"work":{"id":"d7527e23-4e8f-4208-948e-180fbe9a0569","openalex_id":null,"doi":null,"arxiv_id":"2302.10149","raw_key":null,"title":"Poisoning Web-Scale Training Datasets is Practical","authors":null,"authors_text":"N","year":2023,"venue":"cs.CR","abstract":"Deep learning models are often trained on distributed, web-scale datasets crawled from the internet. In this paper, we introduce two new dataset poisoning attacks that intentionally introduce malicious examples to a model's performance. Our attacks are immediately practical and could, today, poison 10 popular datasets. Our first attack, split-view poisoning, exploits the mutable nature of internet content to ensure a dataset annotator's initial view of the dataset differs from the view downloaded by subsequent clients. By exploiting specific invalid trust assumptions, we show how we could have poisoned 0.01% of the LAION-400M or COYO-700M datasets for just $60 USD. Our second attack, frontrunning poisoning, targets web-scale datasets that periodically snapshot crowd-sourced content -- such as Wikipedia -- where an attacker only needs a time-limited window to inject malicious examples. In light of both attacks, we notify the maintainers of each affected dataset and recommended several low-overhead defenses.","external_url":"https://arxiv.org/abs/2302.10149","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-07-11T02:37:46.276610+00:00","pith_arxiv_id":"2302.10149","created_at":"2026-05-11T05:00:57.071910+00:00","updated_at":"2026-07-11T02:37:46.276610+00:00","title_quality_ok":true,"display_title":"Poisoning web-scale training datasets is practical","render_title":"Poisoning web-scale training datasets is practical"},"hub":{"state":{"tier_text":"hub","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":10,"external_cited_by_count":null},"tier":"hub","role_counts":[{"context_role":"background","n":2}],"polarity_counts":[{"context_polarity":"background","n":1},{"context_polarity":"support","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}