Ranking Abuse via Strategic Pairwise Data Perturbations

TL;DR

This paper investigates the vulnerability of MLE-based ranking systems, like Bradley-Terry, to strategic data manipulation, revealing their sensitivity and proposing an effective attack method.

Contribution

It introduces the Adaptive Subset Selection Attack (ASSA) to efficiently identify impactful perturbations and demonstrates the sharp phase-transition behavior in ranking robustness.

Findings

MLE-based rankings are highly sensitive to small perturbations

A limited number of strategic voters can significantly change the ranking

ASSA outperforms baselines in identifying impactful manipulations

Abstract

Pairwise ranking systems based on Maximum Likelihood Estimation (MLE), such as the Bradley-Terry model, are widely used to aggregate preferences from pairwise comparisons. However, their robustness under strategic data manipulation remains insufficiently understood. In this paper, we study the vulnerability of MLE-based ranking systems to adversarial perturbations. We formulate the manipulation task as a constrained combinatorial optimization problem and propose an Adaptive Subset Selection Attack (ASSA) to efficiently identify high-impact perturbations. Experimental results on both synthetic data and real-world election datasets show that MLE-based rankings exhibit a sharp phase-transition behavior: beyond a small perturbation budget, a limited number of strategic voters can significantly alter the global ranking. In particular, our method consistently outperforms random and greedy baselines under constrained budgets. These findings reveal a fundamental sensitivity of MLE-based ranking mechanisms to structured perturbations and highlight the need for more robust aggregation methods in collective decision-making systems.