EvoDefense: Co-Evolving Black-Box Defense with Large Language Models
Large Language Models (LLMs) remain highly vulnerable to diverse attacks, particularly in black-box settings where the internals of target models are inaccessible. Existing black-box defenses typically rely on pre-defined filtering heuristics, which often fail to generalize to unseen attack types and target model architectures. We introduce EvoDefense, an experience-guided co-evolving black-box defense paradigm. EvoDefense employs a guard LLM to detect malicious queries and an experience memory module to accumulate defense knowledge from previous interactions. At the core of EvoDefense is a continuous attack-defense evolution loop, where an attack generator and the guard model iteratively...
| Authors | Yu Li, Yuenan Hou, Yingmei Wei et al. |
|---|---|
| Primary category | cs.CR |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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