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A Theoretical Game of Attacks via Compositional Skills

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A new arXiv preprint formalizes adversarial attacks and defenses in large language models as a theoretical game, deriving provably optimal strategies and demonstrating superior empirical performance against existing methods.

SourcearXiv Computational LinguisticsAuthor: Xinbo Wu, Huan Zhang, Abhishek Umrawal, Lav R. Varshney
A Theoretical Game of Attacks via Compositional Skills
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[2605.01034] A Theoretical Game of Attacks via Compositional Skills

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Computer Science > Computation and Language

arXiv:2605.01034 (cs)

[Submitted on 1 May 2026]

Title:A Theoretical Game of Attacks via Compositional Skills

Authors:Xinbo Wu, Huan Zhang, Abhishek Umrawal, Lav R. Varshney

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Abstract:As large language models grow increasingly capable, concerns about their safe deployment have intensified. While numerous alignment strategies aim to restrict harmful behavior, these defenses can still be circumvented through carefully designed adversarial prompts. In this work, we introduce a theoretical framework that formalizes a game between an attacker and a defender. Within this framework, we design a theoretical best-response attack strategy and show that it is closely related to many existing adversarial prompting methods. We further analyze the resulting game, characterize its equilibria, and reveal inherent advantages for the attacker. Drawing on our theoretical analysis, we also derive a provably optimal defense strategy. Empirically, we evaluate a practical instantiation of the theoretically optimal attack and observe stronger performance relative to existing adversarial prompting approaches in diverse settings encompassing different LLMs and benchmarks.

Comments: arXiv admin note: text overlap with arXiv:2505.20841

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2605.01034 [cs.CL]

(or arXiv:2605.01034v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.01034

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Xinbo Wu [view email] [v1] Fri, 1 May 2026 18:59:24 UTC (2,056 KB)

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Key points and analysis

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Key points

  • Formalizes attacker-defender game for LLM safety
  • Derives optimal attack and defense strategies
  • Shows inherent attacker advantage
  • Empirically outperforms existing adversarial prompting methods

Highlights and analysis are generated automatically and may contain errors. Check the original source.