## 1 Introduction  
Social bots are automated accounts on social media platforms. Malicious social bots have become a growing concern due to their ability to deceive and manipulate social networks [1, 2, 3]. Bot detection methods have evolved from traditional feature engineering to more advanced text-based and graph-based deep learning techniques. Despite these advancements, researchers are struggling to keep up with the rapid development of new bots [4]. This detection lag stems from the reactive nature of current research, where efforts focus on retrospectively adapting to bot advancements rather than proactively countering them. Meanwhile, bots continue to evolve adversarially, systematically bypassing existing detection mechanisms.  
Adversarial examples generated through adding noise have demonstrated effectiveness in evading deep neural networks, which have been applied in most state-of-the-art bot detectors [5]. Studies have shown that modifying certain features or adjusting the bots' neighborhood can significantly reduce the likelihood of being detected by bot detectors [6, 7].  
However, the bots' states are subject to change over time. As a result, existing approaches based on static bot states and environments may not accurately reflect the impact of the bots' behavior. While evaluating bot detector vulnerabilities on real social platforms could be a promising solution, ethical concerns need to be considered [8]. A practical approach is to draw inspiration from security engineers. Specifically, this involves proactively identifying vulnerabilities and exploring new tactics to help bots avoid detection, which deepens our understanding of possible bot adversarial behavior. Efforts have been made to simulate bot behavior and create virtual environments [9; 10; 11], resulting in the development of bots capable of evading detection and enhancing their influence.  
To address these issues, a more realistic virtual environment and novel empirical action strategies trained with reinforcement learning that might be used to evade detection while expanding influence are presented in this paper. By analyzing the growth of followers, retweets, and mentions of bots using a real-world dataset, a common relationship with user-generated content was identified. This enabled us to develop a model that simulates the influence of bots and user engagement, helping to better simulate the impact of bot actions. Furthermore, an adversarially trained profile editor and a target preselection mechanism for bots are introduced, allowing for the simulation of novel bots within a large social network.  
* The heuristic used in previous studies was falsified, and the first bot influence and user engagement models were proposed by analyzing a large real-world dataset.
* An adversarially trained profile editor and a preselection mechanism based on user clustering were proposed to enable the simulation of various novel bots that have evolved in different aspects within a large social network.
* Novel bots were trained to function as fake accounts and influencers, and both RF-based and GNN-based detectors that utilized the bots' profile data, user-generated content, and graph information were deployed to identify them. The results demonstrated that the improvements helped the bots to evade detection, rendering the adversary detectors ineffective.
* We found that certain types of bots outperform others in the ablation experiments, which proves the existence of the detectors' vulnerabilities.