With the advancement of large language models (LLMs) and agent technologies, social bots have shed traditional stereotypes and evolved into autonomous, highly intelligent social media agents. This paper studies whether current social bot detection research is sufficient to identify such agents and discusses potential detection directions. We construct a social media agent dataset from authentic agent accounts on Weibo and Moltbook to evaluate prior detection approaches. Experimental results across various detection algorithms reveal that existing methods suffer from out-of-distribution (OOD) failures and exhibit a cognitive blind spot regarding agent-specific features. Through multidimensional feature comparisons, we find that such agents counterintuitively do not represent a transitional form between humans and traditional bots, but instead follow a distinct pattern that explains the observed generalization failures and feature limitations. Finally, motivated by the excellent performance of LLMs, we propose Agent-Detect-Agent, an online detection agent with tool calling. Experiments demonstrate that this agent further enhances the detection capability of base LLMs and achieves consistently good performance across different account types.

Social bot accounts have long been disseminating disinformation and engaging in malicious activities on social media platforms. Detecting these social bots has become a critical and urgent task, essential for maintaining a healthy online ecosystem. Existing social bot detection research usually provides detection results directly without corresponding supportive explanations, making it difficult to assess the extent to which such predictions are trustworthy. This is a key concern for online moderation. In this work, we explore the detection interpretation and summarize a four-dimensional clue framework from individual and social perspectives. We propose CDRBot, which primarily employs outcome-reward reinforcement learning to train inspectors to generate faithful, grounded, and readable clues from the User Information, Semantic Features, Interactive Situation, and Behavioral Pattern. These clues are then integrated to make final predictions. Experimental results demonstrate that our approach outperforms other baselines in detection performance. The generated clues are faithful, grounded, and readable, and can significantly enhance the performance of large language models in social bot detection.

With the widespread deployment of generative language models, concerns about safety issues have continuously grown. High-quality fine-tuning data generated from red teaming plays a crucial role in the model’s safety. Recently, automated red teaming approaches have been proposed to create test cases. However, these approaches, which rely on open-ended generation, encounter issues related to inefficiency and low attack success rates. In this work, we introduce a black-box approach that ingeniously exploits the unique properties of the nullspace to disentangle and regulate the crucial success information within test cases. Our study provides a brand-new perspective for automated red team research. Experimental results demonstrate that our approach outperforms baseline methods regarding the attack success rate. The generated test cases also excel in aspects of diversity and fluency.