Budgeted Attack Scheduling for LLM Red-Teaming: The Roles of Static Priors, Semantic Guidance, and Online Feedback

As jailbreak attacks against large language models (LLMs) become increasingly diverse, red-teaming has come to rely on large libraries of jailbreak prompts and attack strategies to evaluate the robustness of victim models and their defense settings. However, attack effectiveness is highly condition-dependent: an attack that succeeds under one victim-defense setting may fail under another, while exhaustively evaluating the full attack library for each setting incurs substantial token and monetary costs. This motivates studying how jailbreak attacks should be prioritized under a limited attempt budget for efficient robustness evaluation. In this work, we formalize this problem as budgeted attack scheduling over a fixed attack library and investigate three complementary signals under a fixed victim–defense condition: (1) static historical attack success rate (ASR) priors; (2) semantic guidance from LLMs; and (3) online feedback from earlier queries. Building on these signals, we design four scheduling strategies and evaluate them across three victim models and three defense settings on GPT-5.2. Extensive experiments clearly characterize the conditions under which each signal is most effective. Historical ASR provides a strong initialization, but its reliability degrades under defense-induced distribution shifts. Semantic guidance consistently improves or matches static prior-based scheduling in the cold-start regime, while online feedback becomes particularly valuable when transferred priors become stale. Semantic guidance and online feedback can also partially compensate for missing historical priors. Taken together, these findings characterize budgeted red-teaming as an attack scheduling problem in which historical priors, semantic guidance, and online feedback play complementary roles.