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

Summary

This paper formalizes budgeted attack scheduling for red-teaming LLMs, where a fixed attempt budget must be spent across a fixed jailbreak attack library. It compares three signals: historical attack success rate priors, semantic guidance from LLMs, and online feedback from earlier queries, using four scheduling strategies across three victim models and three defense settings on GPT-5.2. The authors find that historical priors give a strong start but weaken under defense-induced distribution shift, while online feedback helps most once transferred priors go stale.

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