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Assuring EU AI Act Compliance and Adversarial Robustness of LLMs

Author

Listed:
  • Tomas Bueno Momcilovic

    (fortiss GmbH Research Institute)

  • Beat Buesser

    (IBM Research Europe)

  • Giulio Zizzo

    (IBM Research Europe)

  • Mark Purcell

    (IBM Research Europe)

  • Dian Balta

    (fortiss GmbH Research Institute)

Abstract

Large language models are prone to misuse and vulnerable to security threats, raising significant safety and security concerns. The European Union’s Artificial Intelligence Act seeks to enforce AI robustness in certain contexts, but faces implementation challenges due to the lack of standards, complexity of LLMs and emerging security vulnerabilities. Our research introduces a framework using ontologies, assurance cases, and factsheets to support engineers and stakeholders in understanding and documenting AI system compliance and security regarding adversarial robustness. This approach aims to ensure that LLMs adhere to regulatory standards and are equipped to counter potential threats.

Suggested Citation

  • Tomas Bueno Momcilovic & Beat Buesser & Giulio Zizzo & Mark Purcell & Dian Balta, 2026. "Assuring EU AI Act Compliance and Adversarial Robustness of LLMs," Lecture Notes in Information Systems and Organization,, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-08489-7_24
    DOI: 10.1007/978-3-032-08489-7_24
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