# Short paper: Models in the dark -- Rectification and erasure under GDPR in ML supply chains

arXiv: https://arxiv.org/abs/2606.05946 · HTML: https://mlsanity.com/paper/2606.05946/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [In-Context Unlearning: Language Models as Few Shot Unlearners](https://mlsanity.com/paper/2310.07579/) (2023)
- [Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy](https://mlsanity.com/paper/2210.17546/) (2022)
- [Guardrail Baselines for Unlearning in LLMs](https://mlsanity.com/paper/2403.03329/) (2024)
- [Bypassing LLM Guardrails: An Empirical Analysis of Evasion Attacks against Prompt Injection and Jailbreak Detection Systems](https://mlsanity.com/paper/2504.11168/) (2025)
- [VaultGemma: A Differentially Private Gemma Model](https://mlsanity.com/paper/2510.15001/) (2025)
- [User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies](https://mlsanity.com/paper/2509.05382/) (2025)
- [Sanitize Your Responses: Mitigating Privacy Leakage in Large Language Models](https://mlsanity.com/paper/2509.24488/) (2025)

## Abstract (arXiv)

The rights to rectification and erasure, as established under the General Data Protection Regulation (GDPR), are central to protecting individuals' privacy. However, their effective enforcement in machine learning (ML) systems remains challenging. Existing work has largely addressed these rights from either a legal or a technical perspective in isolation and disregards the fact that models are produced in complex supply chains involving multiple actors across development, distribution, and deployment. This paper presents a holistic survey of challenges in implementing the rights to rectification and erasure in ML models. Drawing on academic literature and guidance from data protection authorities, we find that many GDPR requirements cannot yet be technically met in practice. Our findings further suggest that issues arising in ML supply chains are insufficiently addressed in research. To tackle this gap, we introduce the notion of models in the dark -- derived models created further downstream in an ML chain without sufficient transparency or traceability -- and analyse the urgent challenges posed by this phenomenon. By adopting an interdisciplinary perspective, this work contributes to bridging the gap between legal requirements and the technical implementation of data subject rights in ML, ultimately supporting the development of trustworthy artificial intelligence.