You Are an Expert: RAG Injection and Guided Error Expert Activation for Jailbreaking Large Language Models
Year: 2026
Type: article
Abstract: ABSTRACT With the rapid development and widespread deployment of large language models (LLMs), the security and robustness of these models have emerged as critical research topics. Among various threats, jailbreak attacks, which aim to circumvent built‐in safety mechanisms, have garnered considerable attention as a key means of breaching model protections. However, existing jailbreak methods still face several limitations, such as excessive reliance on the model's internal capabilities, high attack costs and insufficiently comprehensive evaluation strategies. To address these challenges, we propose a novel jailbreak framework: YAE. This method enhances the model's dependency on external information by constructing a RAG‐based knowledge base containing harmful Q&A content. It also leverages Guide MoE, a routing‐inspired prompting strategy, to guide the model toward alternative reasoning trajectories through expert‐role prompting and domain‐specific perturbations, thereby inducing more deceptive responses. Furthermore, Adversarial Fine‐Tuning is employed to further optimize the performance of jailbreak attacks. We conduct extensive experiments under various settings, and the results demonstrate that YAE achieves state‐of‐the‐art performance in terms of both attack success rate (ASR) and efficiency, highlighting its strong overall effectiveness.
Source: Expert Systems
DOI: 10.1111/exsy.70395
Authors: Shunli Zhang, Ying Ding, Yanxu Mao
Institutions: Henan Institute of Science and Technology, Henan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7203725753