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EMNLP Findings2025

Proxy Barrier: A Hidden Repeater Layer Defense Against System Prompt Leakage and Jailbreaking

Pedro Schindler Freire Brasil Ribeiro, Iago Alves Brito, Rafael Teixeira Sousa, Fernanda Bufon Färber, Julia Soares Dollis, Arlindo Rodrigues Galvão Filho

摘要

Prompt injection and jailbreak attacks remain a critical vulnerability for deployed large language models (LLMs), allowing adversaries to bypass safety protocols and extract sensitive information. To address this, we present Proxy Barrier (ProB), a lightweight defense that interposes a proxy LLM between the user and the target model. The proxy LLM is tasked solely to repeat the user input, and any failure indicates the presence of an attempt to reveal or override system instructions, leading the malicious request to be detected and blocked before it reaches the target model. ProB therefore requires no access to model weights or prompts, and is deployable entirely at the API level. Experiments across multiple model families demonstrate that ProB achieves state-ofthe-art resilience against prompt leakage and jailbreak attacks. Notably, our approach outperforms baselines and achieves up to 98.8% defense effectiveness, and shows robust protection across both open and closed-source LLMs when suitably paired with proxy models, while also keeping response quality intact.

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它引用的 Paper9

  1. Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 64,255 次引用
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  3. AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language ModelsXiaogeng Liu, Nan Xu, Muhao Chen, Chaowei XiaoICLR 2024 · 722 次引用
  4. WildChat: 1M ChatGPT Interaction Logs in the WildWenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie 等ICLR 2024 · 504 次引用
  5. InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model AgentsQiusi Zhan, Zhixiang Liang, Zifan Ying, Daniel KangACL Findings 2024 · 147 次引用

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