[2603.22882] TreeTeaming: Autonomous Red-Teaming of Vision-Language Models via Hierarchical Strategy Exploration

Computer Science > Machine Learning arXiv:2603.22882 (cs) [Submitted on 24 Mar 2026] Title:TreeTeaming: Autonomous Red-Teaming of Vision-Language Models via Hierarchical Strategy Exploration Authors:Chunxiao Li, Lijun Li, Jing Shao View a PDF of the paper titled TreeTeaming: Autonomous Red-Teaming of Vision-Language Models via Hierarchical Strategy Exploration, by Chunxiao Li and 2 other authors View PDF HTML (experimental) Abstract:The rapid advancement of Vision-Language Models (VLMs) has brought their safety vulnerabilities into sharp focus. However, existing red teaming methods are fundamentally constrained by an inherent linear exploration paradigm, confining them to optimizing within a predefined strategy set and preventing the discovery of novel, diverse exploits. To transcend this limitation, we introduce TreeTeaming, an automated red teaming framework that reframes strategy exploration from static testing to a dynamic, evolutionary discovery process. At its core lies a strategic Orchestrator, powered by a Large Language Model (LLM), which autonomously decides whether to evolve promising attack paths or explore diverse strategic branches, thereby dynamically constructing and expanding a strategy tree. A multimodal actuator is then tasked with executing these complex strategies. In the experiments across 12 prominent VLMs, TreeTeaming achieves state-of-the-art attack success rates on 11 models, outperforming existing methods and reaching up to 87.60\% on GPT-4o. The...