mlsanityData to 7 Oct 2026

A Survey on Privacy Attacks Against Digital Twin Systems in AI-Robotics

arXiv 2406.18812 ↗First posted 27 Jun 2024cs.RO cs.AIcited by 3

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Abstract (arXiv)

Industry 4.0 has witnessed the rise of complex robots fueled by the integration of Artificial Intelligence/Machine Learning (AI/ML) and Digital Twin (DT) technologies. While these technologies offer numerous benefits, they also introduce potential privacy and security risks. This paper surveys privacy attacks targeting robots enabled by AI and DT models. Exfiltration and data leakage of ML models are discussed in addition to the potential extraction of models derived from first-principles (e.g., physics-based). We also discuss design considerations with DT-integrated robotics touching on the impact of ML model training, responsible AI and DT safeguards, data governance and ethical considerations on the effectiveness of these attacks. We advocate for a trusted autonomy approach, emphasizing the need to combine robotics, AI, and DT technologies with robust ethical frameworks and trustworthiness principles for secure and reliable AI robotic systems.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 34 of its 124 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

34 of 124 references.

References · 34

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22 more
  1. 2019Reconstruction and Membership Inference Attacks against Generative Modelscited by 122
  2. 2024JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMscited by 87
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  10. 2024Prompt Stealing Attacks Against Large Language Modelscited by 39
  11. 2024Logits of API-Protected LLMs Leak Proprietary Informationcited by 38
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  15. 2023Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Lifecited by 19
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  22. 2019A Formal Approach to Physics-Based Attacks in Cyber-Physical Systems (Extended Version)cited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025Measuring Physical-World Privacy Awareness of Large Language Models: An Evaluation Benchmarkcited by 2
  2. 2024Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunitiescited by 1
  3. 2026PriMod4AI: Lifecycle-Aware Privacy Threat Modeling for AI Systems using LLMcited by 0