# Practical and General Backdoor Attacks against Vertical Federated Learning

arXiv: https://arxiv.org/abs/2306.10746 · HTML: https://mlsanity.com/paper/2306.10746/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [Neurotoxin: Durable Backdoors in Federated Learning](https://mlsanity.com/paper/2206.10341/) (2022)
- [CAFE: Catastrophic Data Leakage in Vertical Federated Learning](https://mlsanity.com/paper/2110.15122/) (2021)
- [Backdoor attacks and defenses in feature-partitioned collaborative learning](https://mlsanity.com/paper/2007.03608/) (2020)
- [FLAME: Taming Backdoors in Federated Learning (Extended Version 1)](https://mlsanity.com/paper/2101.02281/) (2021)

## Built on by (most cited first, 6 of 6)

- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification](https://mlsanity.com/paper/2408.15591/) (2024)
- [Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning](https://mlsanity.com/paper/2501.09320/) (2025)
- [Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning](https://mlsanity.com/paper/2602.20593/) (2026)
- [HASSLE: A Self-Supervised Learning Enhanced Hijacking Attack on Vertical Federated Learning](https://mlsanity.com/paper/2507.10162/) (2025)
- [Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice](https://mlsanity.com/paper/2608.12962/) (2026)

## Abstract (arXiv)

Federated learning (FL), which aims to facilitate data collaboration across multiple organizations without exposing data privacy, encounters potential security risks. One serious threat is backdoor attacks, where an attacker injects a specific trigger into the training dataset to manipulate the model's prediction. Most existing FL backdoor attacks are based on horizontal federated learning (HFL), where the data owned by different parties have the same features. However, compared to HFL, backdoor attacks on vertical federated learning (VFL), where each party only holds a disjoint subset of features and the labels are only owned by one party, are rarely studied. The main challenge of this attack is to allow an attacker without access to the data labels, to perform an effective attack. To this end, we propose BadVFL, a novel and practical approach to inject backdoor triggers into victim models without label information. BadVFL mainly consists of two key steps. First, to address the challenge of attackers having no knowledge of labels, we introduce a SDD module that can trace data categories based on gradients. Second, we propose a SDP module that can improve the attack's effectiveness by enhancing the decision dependency between the trigger and attack target. Extensive experiments show that BadVFL supports diverse datasets and models, and achieves over 93% attack success rate with only 1% poisoning rate.