STAR-Teaming: A Strategy-Response Multiplex Network Approach to Automated LLM Red Teaming

2026-04-20

AI Summary

This work addresses the vulnerability of large language models to jailbreaking attacks that elicit harmful outputs. To counter this, the authors propose the STAR-Teaming framework, which introduces for the first time a strategy-response multiplex network to model red-teaming dynamics. By integrating multi-agent systems with network-driven optimization, the framework reconstructs interpretable semantic community structures in high-dimensional embedding spaces to guide efficient adversarial sampling. This approach substantially improves attack success rates while reducing computational overhead, achieving both high efficiency and strong interpretability.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

While Large Language Models (LLMs) are widely used, they remain susceptible to jailbreak prompts that can elicit harmful or inappropriate responses. This paper introduces STAR-Teaming, a novel black-box framework for automated red teaming that effectively generates such prompts. STAR-Teaming integrates a Multi-Agent System (MAS) with a Strategy-Response Multiplex Network and employs network-driven optimization to sample effective attack strategies. This network-based approach recasts the intractable high-dimensional embedding space into a tractable structure, yielding two key advantages: it enhances the interpretability of the LLM's strategic vulnerabilities, and it streamlines the search for effective strategies by organizing the search space into semantic communities, thereby preventing redundant exploration. Empirical results demonstrate that STAR-Teaming significantly surpasses existing methods, achieving a higher attack success rate (ASR) at a lower computational cost. Extensive experiments validate the effectiveness and explainability of the Multiplex Network. The code is available at https://github.com/selectstar-ai/STAR-Teaming-paper.

Problem

• Large Language Models
• jailbreak prompts
• red teaming
• adversarial attacks
• model safety

Innovation

• Multiplex Network
• Red Teaming
• Multi-Agent System
• Strategy Optimization
• Black-box Attack