Adaptive Instruction Composition for Automated LLM Red-Teaming
Many approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target. Several of them task the attacker with identifying effective strategies through trial and error, resulting in a semantically limited range of successes. Another approach discovers diverse attacks by combining crowdsourced harmful queries and tactics into instructions for the attacker, but does so at random, limiting effectiveness. This article introduces a novel framework, Adaptive Instruction Composition, that combines crowdsourced texts according to an adaptive mechanism trained to jointly optimize effectiveness with diversity. We use reinforcement learning to balance exploration with ex...
| Authors | Jesse Zymet, Andy Luo, Swapnil Shinde et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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