Yixin Wu, Rachel Hong, Jamie Morgenstern, Tadayoshi Kohno · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27533 · open-access PDF
Machine learning benchmarks act as guideposts for model development and spur the creation of training datasets intended to improve benchmark scores. We introduce BenchDoor, a family of data poisoning attacks that exploits this incentive. Beyond preserving benign performance, BenchDoor requires benchmark gains that encourage adoption of data that also implants a backdoor. The adversary either manipulates a benchmark's training or test data, creating an adversarial benchmark, or supplies an independent training dataset while leaving the benchmark unchanged. We formalize five attack scenarios, in
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