Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 4 Oct 2024.
Builds on
7 of 19 references.
References · 7
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
- 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 380
- 2024Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attackcited by 364
- 2023ChatGPT for Robotics: Design Principles and Model Abilitiescited by 296
- 2024Coercing LLMs to do and reveal (almost) anythingcited by 88
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Citing papers per year: 2026: 2.
2 papers.