Poisoning Web-Scale Training Datasets is Practical.¶
Carlini, N. (2024). Poisoning Web-Scale Training Datasets is Practical. 2024 IEEE Symposium on Security and Privacy (S&P), 407-425.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Inherited-Substrate Risk
- AI transfer learning: a fine-tuned model inherits the foundation model's training-data biases, prompt injections, and implanted triggers; the downstream team audited their fine-tuning data, not the foundation, so the risk lives across the boundary.
This sourceDemonstrates that foundation-model training corpora can be poisoned upstream, so fine-tuned models inherit implanted vulnerabilities.
- AI transfer learning: a fine-tuned model inherits the foundation model's training-data biases, prompt injections, and implanted triggers; the downstream team audited their fine-tuning data, not the foundation, so the risk lives across the boundary.
Verification¶
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