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Addressing Prompt Injection in Large Language Models via In-Context Learning

Go Sato Shusaku Egami Yasuyuki Tahara Akihiko Ohsuga Yuichi Sei

Computers, Materials & Continua2026，Vol.87Issue(5)：P.2270-2306,37.

Computers, Materials & Continua2026，Vol.87Issue(5)：P.2270-2306,37.DOI:10.32604/cmc.2026.078188

Addressing Prompt Injection in Large Language Models via In-Context Learning

Go Sato ¹Shusaku Egami ²Yasuyuki Tahara ¹Akihiko Ohsuga ¹Yuichi Sei¹

作者信息

1. Department of Informatics,University of Electro-Communications,Tokyo,Japan
2. Department of Informatics,University of Electro-Communications,Tokyo,Japan Artificial Intelligence Research Center(AIRC),National Institute of Advanced Industrial Science and Technology(AIST),Tokyo,Japan
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摘要

关键词

Large language models(LLMs)/prompt injection/in-context learning(ICL)/multi-agent system

分类

信息技术与安全科学

引用本文复制引用

Go Sato,Shusaku Egami,Yasuyuki Tahara,Akihiko Ohsuga,Yuichi Sei..Addressing Prompt Injection in Large Language Models via In-Context Learning[J].Computers, Materials & Continua,2026,87(5):P.2270-2306,37.

基金项目

supported by JSPS KAKENHI Grant Numbers JP23K28377,JP24H00714,JP25K15109,JP25K03190,JP25K03232,JP22K12157 （）

The Telecommunications Advancement Foundation. （）

Computers, Materials & Continua

ISSN：1546-2218

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