Automated red-teaming has emerged as a scalable approach for auditing Large Language Models (LLMs) prior to deployment, yet existing approaches lack mechanisms to efficiently adapt to model-specific vulnerabilities at inference. We introduce Red-Bandit, a red-teaming framework that adapts online to identify and exploit model failure modes under distinct attack styles (e.g., manipulation, slang). Red-Bandit post-trains a set of parameter-efficient LoRA experts, each specialized for a particular attack style, using reinforcement learning that rewards the generation of unsafe prompts via a rule-based safety model. At inference, a multi-armed bandit policy dynamically selects among these attack-style experts based on the target model’s response safety, balancing exploration and exploitation. Red-Bandit outperforms state-of-the-art methods on AdvBench and HarmBench, achieving higher attack success rates under sufficient exploration budgets (ASR@10), while generating more human-readable adversarial prompts (lower perplexity). In addition, Red-Bandit’s bandit policy serves as a diagnostic tool for identifying model-specific vulnerabilities by indicating which attack styles most effectively elicit harmful behaviors.