# My Brother Helps Me: Node Injection Based Adversarial Attack on Social Bot Detection

arXiv: https://arxiv.org/abs/2310.07159 · HTML: https://mlsanity.com/paper/2310.07159/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2023.

## Built on by (most cited first, 5 of 5)

- [A General Black-box Adversarial Attack on Graph-based Fake News Detectors](https://mlsanity.com/paper/2404.15744/) (2024)
- [RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction](https://mlsanity.com/paper/2510.16035/) (2025)
- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)
- [Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses](https://mlsanity.com/paper/2510.17185/) (2025)
- [Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection](https://mlsanity.com/paper/2602.00318/) (2026)

## Abstract (arXiv)

Social platforms such as Twitter are under siege from a multitude of fraudulent users. In response, social bot detection tasks have been developed to identify such fake users. Due to the structure of social networks, the majority of methods are based on the graph neural network(GNN), which is susceptible to attacks. In this study, we propose a node injection-based adversarial attack method designed to deceive bot detection models. Notably, neither the target bot nor the newly injected bot can be detected when a new bot is added around the target bot. This attack operates in a black-box fashion, implying that any information related to the victim model remains unknown. To our knowledge, this is the first study exploring the resilience of bot detection through graph node injection. Furthermore, we develop an attribute recovery module to revert the injected node embedding from the graph embedding space back to the original feature space, enabling the adversary to manipulate node perturbation effectively. We conduct adversarial attacks on four commonly used GNN structures for bot detection on two widely used datasets: Cresci-2015 and TwiBot-22. The attack success rate is over 73% and the rate of newly injected nodes being detected as bots is below 13% on these two datasets.