Adversarial Benchmarks and BenchDoor Attacks: Exploiting Benchmark Incentives for Backdoor Attacks
BenchDoor exploits the ML community's benchmark-chasing culture to plant backdoors in training data that boost scores while embedding hidden malicious behaviours — a systemic supply-chain risk for AI model builders.
Summary written by editorial AI · Source link below
arXiv:2610.27533v1 Announce Type: new 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, creatin
Editorial Analysis
Enterprises fine-tuning models on benchmark-optimised datasets risk importing poisoned data; the incentive structure of ML benchmarks itself becomes a supply-chain attack surface.
Validate training-data provenance and perform independent backdoor scans on any benchmark-optimised datasets before use in production models.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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