# Oops!... They Stole it Again: Attacks on Split Learning

arXiv: https://arxiv.org/abs/2508.10598 · HTML: https://mlsanity.com/paper/2508.10598/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 98 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 30 of 45)

- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
- [Unleashing the Tiger: Inference Attacks on Split Learning](https://mlsanity.com/paper/2012.02670/) (2020)
- [Label Leakage and Protection in Two-party Split Learning](https://mlsanity.com/paper/2102.08504/) (2021)
- [UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning](https://mlsanity.com/paper/2108.09033/) (2021)
- [Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?](https://mlsanity.com/paper/2003.12365/) (2020)
- [ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning](https://mlsanity.com/paper/2205.04007/) (2022)
- [Advancements of federated learning towards privacy preservation: from federated learning to split learning](https://mlsanity.com/paper/2011.14818/) (2020)
- [Practical Defences Against Model Inversion Attacks for Split Neural Networks](https://mlsanity.com/paper/2104.05743/) (2021)
- [Differentially Private Label Protection in Split Learning](https://mlsanity.com/paper/2203.02073/) (2022)
- [FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks](https://mlsanity.com/paper/2011.03180/) (2020)
- [DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks](https://mlsanity.com/paper/2012.11025/) (2020)
- [Reducing Communication for Split Learning by Randomized Top-k Sparsification](https://mlsanity.com/paper/2305.18469/) (2023)
- [SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learning](https://mlsanity.com/paper/2108.09052/) (2021)
- [A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning](https://mlsanity.com/paper/2405.04115/) (2024)
- [ExPLoit: Extracting Private Labels in Split Learning](https://mlsanity.com/paper/2112.01299/) (2021)
- [Feature Space Hijacking Attacks against Differentially Private Split Learning](https://mlsanity.com/paper/2201.04018/) (2022)
- [Similarity-based Label Inference Attack against Training and Inference of Split Learning](https://mlsanity.com/paper/2203.05222/) (2022)
- [Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning](https://mlsanity.com/paper/2301.08778/) (2023)
- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Blind Faith: Privacy-Preserving Machine Learning using Function Approximation](https://mlsanity.com/paper/2107.14338/) (2021)
- [HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning](https://mlsanity.com/paper/2212.00325/) (2022)
- [Label Inference Attack against Split Learning under Regression Setting](https://mlsanity.com/paper/2301.07284/) (2023)
- [Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks](https://mlsanity.com/paper/2304.09515/) (2023)
- [Get your Foes Fooled: Proximal Gradient Split Learning for Defense against Model Inversion Attacks on IoMT data](https://mlsanity.com/paper/2201.04569/) (2022)
- [GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption](https://mlsanity.com/paper/2401.14840/) (2024)
- [On Feasibility of Server-side Backdoor Attacks on Split Learning](https://mlsanity.com/paper/2302.09578/) (2023)
- [Split Without a Leak: Reducing Privacy Leakage in Split Learning](https://mlsanity.com/paper/2308.15783/) (2023)

## Built on by (most cited first, 1 of 1)

- [Position Matters: Feature Inversion Attacks in ViT Split Inference with Token Reduction and Shuffling](https://mlsanity.com/paper/2609.01232/) (2026)

## Abstract (arXiv)

Split Learning (SL) is a collaborative learning approach that improves privacy by keeping data on the client-side while sharing only the intermediate output with a server. However, the distributed nature of SL introduces new security challenges, necessitating a comprehensive exploration of potential attacks. This paper systematically reviews various attacks on SL, classifying them based on factors such as the attacker's role, the type of privacy risks, when data leaks occur, and where vulnerabilities exist. We also analyze existing defense methods, including cryptographic methods, data modification approaches, distributed techniques, and hybrid solutions. Our findings reveal security gaps, highlighting the effectiveness and limitations of existing defenses. By identifying open challenges and future directions, this work provides valuable information to improve SL privacy issues and guide further research.