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Poisoning Web-Scale Training Datasets is Practical
Open accessAbstract
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.
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“NIST SP 800-204D [13] provides guidance for integrating software supply chain security controls into DevSecOps CI/CD pipelines, but it does not define AI-specific provenance structures or model assurance mechanisms.”
From AI Supply Chain Security: MBOM-PQC Provenance, PQC Attestation, and a Maturity Model for Quantum-Resistant Assurance (2026) · cites this paper as [13]
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Venomave: Targeted Poisoning Against Speech Recognition
Aghakhani, H., Schönherr, L., Eisenhofer, T., Kolossa, D., Holz, T., Kruegel, C., & Vigna, G. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00035 · Cited by 16
Hidden Trigger Backdoor Attacks
Saha, A., Subramanya, A., & Pirsiavash, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6871 · Cited by 13
Domains Do Change Their Spots: Quantifying Potential Abuse of Residual Trust
So, J. C. F., Miramirkhani, N., Ferdman, M., & Nikiforakis, N. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833609 · Cited by 12
The Abandoned Side of the Internet: Hijacking Internet Resources When Domain Names Expire
Schlamp, J., Gustafsson, J., Wählisch, M., Schmidt, T. C., & Carle, G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-17172-2_13 · Cited by 7
From Deletion to Re-Registration in Zero Seconds
Lauinger, T., Buyukkayhan, A. S., Chaabane, A., Robertson, W., & Kirda, E. (2018) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3278532.3278560 · Cited by 5
Research works printed but not linked: 3
- 10.18653/v1/n19–1423 · DOI · Paper page
- Guo, Wiki-40B: Multilingual language model dataset, 2440
- Radford, 2019, Language models are unsupervised multitask learners, OpenAI blog, 1, 9
Cited by
Sorted by citations
Medical large language models are vulnerable to data-poisoning attacks
Alber, D. A., Yang, Z., Alyakin, A., Yang, E., Rai, S., Valliani, A. A., Zhang, J., Rosenbaum, G. R., Amend-Thomas, A. K., Kurland, D. B., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03445-1 · Cited by 242
Adversarial machine learning :
Vassilev, A., Oprea, A., Fordyce, A. J., & Anderson, H. S. (2024) · doi:10.6028/nist.ai.100-2e2023 · Cited by 230
Self-citationRethinking machine unlearning for large language models
Liu, S., Yao, Y., Jia, J., Casper, S., Baracaldo, N., Hase, P., Yao, Y., Liu, C. Y., Xu, X., Li, H., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00985-0 · Cited by 374
Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models
Shan, S., Ding, W., Passananti, J., Wu, S. L., Zheng, H.-T., & Zhao, B. Y. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00207 · Cited by 166
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Casper, S., Davies, X., Shi, C., Gilbert, T. K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.15217 · Cited by 101
“However, a similar attack is successful for instruction tuning with very few examples [Wan et al., 2023, Xu et al., 2023a], and poisoning web-scale datasets is possible under realistic assumptions [Carlini et al., 2023a].”
Background
Metadata
- Authors
- Carlini, N., Jagielski, M., Choquette-Choo, C. A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., & Tramèr, F.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- ETH Zurich (CH)
- Nvidia (United Kingdom) (GB)
- Robust Intelligence
- Google (United States) (US)
- Venue
- 2024 IEEE Symposium on Security and Privacy (SP)
- Publisher
- IEEE
- Published
- 2024-05-19
- Pages
- 407-425
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W4402264526
- Cited by
- 138
- References
- 111
- Citations by year
- 2026: 53 · 2025: 44 · 2024: 29
- FWCI
- 37.18
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Symposium on Security and Privacy (S&P) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- deep learning security, Wikipedia, crowdsourced data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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