Exposing Vulnerability Monoculture Through Systemic Adversarial Robustness

Key points

• 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?1
• We empirically test the shared vulnerability of vision and large language models and characterize their shared collective vulnerability curve.1
• 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.1
• Our framework seeks to establish vulnerability monoculture as a measurable target for a more wholistic adversarial evaluation of model populations.1

Sources (1)

• [1]Exposing Vulnerability Monoculture Through Systemic Adversarial Robustness
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 10:30 PM

  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.

  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?

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