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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

Japanese

これらの共有アルゴリズム選択は、異なる形で異なるモデルエコシステムを脆弱性モノカルチャーにつながる集中的なリスクにさらすことができます。しかし、現在のモデルプール内の共有コスト脆弱性の実験的性質を調査し、System Adversarial Robustness(SAR)をモデルプールの個々の脆弱性モデルと攻撃転送率に限定し、それを利用するために必要な攻撃の低い多様性を通じて共有された脆弱性モデルの新しい測定を残し、これらの共有アルゴリズム選択は、異なる形で異なるモデルエコシステムを脆弱性モノカルチャーにつながる可能性にさらします。具体的には、私たちのSARは、単一攻撃移行性と単一モデル独立性の間

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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