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Exposing Vulnerability Monoculture Through Systemic Adversarial Robustness

Leo Elmecker-Plakolm, Łukasz Szpruch, Avishek Joey Bose, Matthew Wicker · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27353 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

这些共享的算法选择可以暴露出另一种不同的模型生态系统,导致脆弱性单一文化的显著多样性。然而,当前在评估对手强度的实践中,仅限于单个强度分数和攻击转移率,从而打开了未经探索的基本问题:多少攻击足以实现灾难性的单一攻击宽泛的妥协? 在本文中,我们调查了在模型池内共享成本弱点的经验性性,并引入了系统对手强度(SAR),这是通过攻击多样性所需的攻击多样性共享模型的新测量。 具体而言,我们的SAR揭示了单一攻击宽泛性和单一模型强度之间的相互作用:小值揭示了一个强制性的单一弱点,其中大型模型可以通过对

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

Abstract

Beneath the apparent diversity of foundation models lies an increasingly homogeneous technical stack of common training pipelines, distillation methods, and data. These shared algorithmic choices can expose an otherwise distinct model ecosystem to concentrated risks leading to a vulnerability monoculture. Yet current practice in assessing adversarial robustness is restricted to individual robustness scores and attack transfer rates, leaving open the unexplored fundamental question: how many attacks suffice for catastrophic widespread compromise? In this paper, we investigate the empirical nature of shared vulnerabilities within a model pool and introduce Systemic Adversarial Robustness (SAR), a novel measure of shared vulnerability of a model pool through the attack diversity needed to exploit it. Specifically, SAR interpolates between single-attack transferability and independent per-model robustness: small values reveal a vulnerability monoculture, whereas large values indicate that failures are dispersed across models. We theoretically formulate SAR as a partial set-cover problem with provable approximation guarantees---enabling the development of scalable algorithms to empirically measure the systemic adversarial robustness. We empirically test the shared vulnerability of vision and large language models and characterize their shared collective vulnerability curve. Specifically, we demonstrate that model pools distilled from DeepSeek-R1 and Claude Opus 4.6 exhibit lower estimated systemic robustness than their corresponding parent pools, revealing a potential systemic cost of frontier-model distillation. Our framework seeks to establish vulnerability monoculture as a measurable target for a more wholistic adversarial evaluation of model populations.

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Topics: Adversarial system, Robustness (evolution), Computer science, Exploit, Vulnerability (computing), Vulnerability assessment

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