# HASSLE: A Self-Supervised Learning Enhanced Hijacking Attack on Vertical Federated Learning

arXiv: https://arxiv.org/abs/2507.10162 · HTML: https://mlsanity.com/paper/2507.10162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security.
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Placed in

- LLM safety and security / [Federated unlearning and poisoning](https://mlsanity.com/safety/federated-unlearning-and-poisoning/)

## Builds on (most cited first, 18 of 18)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Anti-Backdoor Learning: Training Clean Models on Poisoned Data](https://mlsanity.com/paper/2110.11571/) (2021)
- [Adversarial Neuron Pruning Purifies Backdoored Deep Models](https://mlsanity.com/paper/2110.14430/) (2021)
- [SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption](https://mlsanity.com/paper/2106.15147/) (2021)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Label Leakage and Protection in Two-party Split Learning](https://mlsanity.com/paper/2102.08504/) (2021)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Label Leakage and Protection from Forward Embedding in Vertical Federated Learning](https://mlsanity.com/paper/2203.01451/) (2022)
- [BadVFL: Backdoor Attacks in Vertical Federated Learning](https://mlsanity.com/paper/2304.08847/) (2023)
- [ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems](https://mlsanity.com/paper/2201.02775/) (2022)
- [Practical and General Backdoor Attacks against Vertical Federated Learning](https://mlsanity.com/paper/2306.10746/) (2023)
- [Hijack Vertical Federated Learning Models As One Party](https://mlsanity.com/paper/2212.00322/) (2022)
- [VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification](https://mlsanity.com/paper/2408.15591/) (2024)

## Abstract (arXiv)

Vertical Federated Learning (VFL) enables an orchestrating active party to perform a machine learning task by cooperating with passive parties that provide additional task-related features for the same training data entities. While prior research has leveraged the privacy vulnerability of VFL to compromise its integrity through a combination of label inference and backdoor attacks, their effectiveness is constrained by the low label inference precision and suboptimal backdoor injection conditions. To facilitate a more rigorous security evaluation on VFL without these limitations, we propose HASSLE, a hijacking attack framework composed of a gradient-direction-based label inference module and an adversarial embedding generation algorithm enhanced by self-supervised learning. HASSLE accurately identifies private samples associated with a targeted label using only a single known instance of that label. In the two-party scenario, it demonstrates strong performance with an attack success rate (ASR) of over 99% across four datasets, including both image and tabular modalities, and achieves 85% ASR on the more complex CIFAR-100 dataset. Evaluation of HASSLE against 8 potential defenses further highlights its significant threat while providing new insights into building a trustworthy VFL system.