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Adversarial Benchmarks and BenchDoor Attacks: Exploiting Benchmark Incentives for Backdoor Attacks

Yixin Wu, Rachel S. Hong, Jamie Morgenstern, Tadayoshi Kohno · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27533 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

机器学习基准作为模型开发数据指南,并鼓励创建旨在改善基准分数的培训数据集。我们引入了 BenchDoor,一种利用这种激励的数据中毒攻击的家族。 除了保持良性性能之外,BenchDoor还需要基准获得,鼓励采用数据,同时植入后门。 对手要么操纵基准分数的培训或测试数据,创建对抗基准,要么提供独立的培训数据集,同时保持基准分数不变。 我们正式化了五个攻击场景,包括对重写测试示例的培训,并应用基于相似性的消毒。 通过多选择的Q&A,文本分类和开放式Q&A,通过Qwen和Gemma模型,BenchDoor如何为所有30个评估的基准

Machine translation (Meta M2M100); the English original follows.

Abstract

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, including training on rewritten test examples and applying similarity-based decontamination. Across multiple-choice Q&A, text classification, and open-ended Q&A with Qwen and Gemma models, BenchDoor achieves positive benchmark gains for all 30 evaluated configurations, with held-out attack success rates of 82.33% to 100.00% at poisoning rates of 2% to 10%. Both objectives remain achievable after synthetic data generation and similarity-based decontamination, with attack success rates of at least 90.33%. These findings show how benchmarks can serve as attack vectors both through their own data and through training resources that deliver improvements on them.

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Topics: Benchmark (surveying), Backdoor, Exploit, Adversarial system, Computer science, Adversary

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