# Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity

arXiv: https://arxiv.org/abs/2401.07348 · HTML: https://mlsanity.com/paper/2401.07348/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 103 references that are in the map.
- First posted on arXiv 14 Jan 2024.

## Builds on (most cited first, 30 of 36)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://mlsanity.com/paper/1908.07125/) (2019)
- [Does Learning Require Memorization? A Short Tale about a Long Tail](https://mlsanity.com/paper/1906.05271/) (2019)
- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [How Language Model Hallucinations Can Snowball](https://mlsanity.com/paper/2305.13534/) (2023)
- [A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models](https://mlsanity.com/paper/2401.01313/) (2024)
- [Predictability and Surprise in Large Generative Models](https://mlsanity.com/paper/2202.07785/) (2022)
- [What Does it Mean for a Language Model to Preserve Privacy?](https://mlsanity.com/paper/2202.05520/) (2022)
- [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023)
- [LM vs LM: Detecting Factual Errors via Cross Examination](https://mlsanity.com/paper/2305.13281/) (2023)
- [Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering](https://mlsanity.com/paper/2307.16877/) (2023)
- [Self-Alignment with Instruction Backtranslation](https://mlsanity.com/paper/2308.06259/) (2023)
- [Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models](https://mlsanity.com/paper/2401.01301/) (2024)
- [Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models](https://mlsanity.com/paper/1909.11299/) (2019)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Foundation Models and Fair Use](https://mlsanity.com/paper/2303.15715/) (2023)
- [Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis](https://mlsanity.com/paper/2210.04714/) (2022)
- [Regulating ChatGPT and other Large Generative AI Models](https://mlsanity.com/paper/2302.02337/) (2023)
- [Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?](https://mlsanity.com/paper/2104.07762/) (2021)
- [Cognitive Mirage: A Review of Hallucinations in Large Language Models](https://mlsanity.com/paper/2309.06794/) (2023)
- [Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions](https://mlsanity.com/paper/2307.03941/) (2023)
- [Algorithms that Remember: Model Inversion Attacks and Data Protection Law](https://mlsanity.com/paper/1807.04644/) (2018)
- [An Overview of Privacy in Machine Learning](https://mlsanity.com/paper/2005.08679/) (2020)

## Built on by (most cited first, 21 of 21)

- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models](https://mlsanity.com/paper/2407.15248/) (2024)
- [T2I-RiskyPrompt: A Benchmark for Safety Evaluation, Attack, and Defense on Text-to-Image Model](https://mlsanity.com/paper/2510.22300/) (2025)
- [Robustness and Cybersecurity in the EU Artificial Intelligence Act](https://mlsanity.com/paper/2502.16184/) (2025)
- [Prompt Obfuscation for Large Language Models](https://mlsanity.com/paper/2409.11026/) (2024)
- [A Comprehensive Survey on Legal Summarization: Challenges and Future Directions](https://mlsanity.com/paper/2501.17830/) (2025)
- [Synthetic Lyrics Detection Across Languages and Genres](https://mlsanity.com/paper/2406.15231/) (2024)
- [Large Language Model for Patent Concept Generation](https://mlsanity.com/paper/2409.00092/) (2024)
- [Who Owns the Output? Bridging Law and Technology in LLMs Attribution](https://mlsanity.com/paper/2504.01032/) (2025)
- [Let's have a chat with the EU AI Act](https://mlsanity.com/paper/2505.11946/) (2025)
- [The Role of Language Models in Modern Healthcare: A Comprehensive Review](https://mlsanity.com/paper/2409.16860/) (2024)
- [Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs](https://mlsanity.com/paper/2410.05304/) (2024)
- [Beyond Abstract Compliance: Operationalising trust in AI as a moral relationship](https://mlsanity.com/paper/2601.22769/) (2026)
- [Protecting patient privacy in clinical foundation models: Technical and legal perspectives](https://mlsanity.com/paper/2608.07705/) (2026)
- [Thorns and Algorithms: Navigating Generative AI Challenges Inspired by Giraffes and Acacias](https://mlsanity.com/paper/2407.11360/) (2024)
- [It's complicated. The relationship of algorithmic fairness and non-discrimination provisions for high-risk systems in the EU AI Act](https://mlsanity.com/paper/2501.12962/) (2025)
- [Synthetic media and computational capitalism: towards a critical theory of artificial intelligence](https://mlsanity.com/paper/2503.18976/) (2025)
- [NLP Meets the World: Toward Improving Conversations With the Public About Natural Language Processing Research](https://mlsanity.com/paper/2507.10559/) (2025)
- [Trustworthiness of Legal Considerations for the Use of LLMs in Education](https://mlsanity.com/paper/2508.03771/) (2025)
- [MarkTune: Improving the Quality-Detectability Trade-off in Open-Weight LLM Watermarking](https://mlsanity.com/paper/2512.04044/) (2025)
- [Quantize with Confidence? An Empirical Study of Quantization for Code Generation](https://mlsanity.com/paper/2607.14181/) (2026)

## Abstract (arXiv)

The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper delves into the legal and regulatory implications of Generative AI and LLMs in the European Union context, analyzing aspects of liability, privacy, intellectual property, and cybersecurity. It critically examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA) draft, in addressing the unique challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and shortcomings in the legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models, ensuring they align with the EU's evolving digital landscape and legal standards.