Assuring EU AI Act Compliance and Adversarial Robustness of LLMs

Tomas Bueno Momcilovic (), Beat Buesser (), Giulio Zizzo (), Mark Purcell () and Dian Balta ()
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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

A chapter in Shaping the Digital Future Through Innovation and Practice, 2026, pp 355-363 from Springer

Abstract: 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.

Keywords: Assurance; Compliance; Large language models; Adversarial robustness (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-08489-7_24

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DOI: 10.1007/978-3-032-08489-7_24

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