@inproceedings{choi-etal-2026-kidnaprag,
title = "{K}idnap{RAG}: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems",
author = "Choi, Chanwoo and
Kim, Euntae and
Lee, Kyuho and
Chun, Youngsam and
Jeong, Jinhee and
Kim, Eunmi and
Oh, Myunggyo and
Jang, Junseo and
Chang, Buru",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2359/",
pages = "53561--53590",
ISBN = "979-8-89176-459-0",
abstract = "Retrieval-Augmented Generation (RAG) systems are vulnerable to poisoning attacks that inject malicious documents into the retrieval process to manipulate model outputs. Recent Agentic RAG systems are more robust to such attacks because they iteratively perform retrieval and reasoning, allowing them to ignore weakly relevant poisoned documents and preserve the reasoning chain induced by the user query. However, existing attacks on Agentic RAG systems often assume white-box access to system prompts, reasoning traces, retrievers, or model parameters, limiting their applicability in realistic settings. In this paper, we study black-box poisoning attacks against Agentic RAG systems, where the attacker can only publish externally retrievable poisoned documents. We propose KidnapRAG, a sequential poisoning attack that hijacks the agent{'}s multi-step reasoning chain using three role-specific documents: Bait, Chain-Link, and Mal-Ins, which attract initial retrieval, induce query reformulation, and provide attacker-controlled evidence, respectively. Experiments across multiple Agentic RAG frameworks, LLM backbones, and benchmarks show that KidnapRAG consistently outperforms existing poisoning baselines under black-box conditions. Further analyses show that KidnapRAG progressively weakens the original retrieval intent, redirects retrieval behavior, and increases reliance on attacker-controlled evidence."
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<abstract>Retrieval-Augmented Generation (RAG) systems are vulnerable to poisoning attacks that inject malicious documents into the retrieval process to manipulate model outputs. Recent Agentic RAG systems are more robust to such attacks because they iteratively perform retrieval and reasoning, allowing them to ignore weakly relevant poisoned documents and preserve the reasoning chain induced by the user query. However, existing attacks on Agentic RAG systems often assume white-box access to system prompts, reasoning traces, retrievers, or model parameters, limiting their applicability in realistic settings. In this paper, we study black-box poisoning attacks against Agentic RAG systems, where the attacker can only publish externally retrievable poisoned documents. We propose KidnapRAG, a sequential poisoning attack that hijacks the agent’s multi-step reasoning chain using three role-specific documents: Bait, Chain-Link, and Mal-Ins, which attract initial retrieval, induce query reformulation, and provide attacker-controlled evidence, respectively. Experiments across multiple Agentic RAG frameworks, LLM backbones, and benchmarks show that KidnapRAG consistently outperforms existing poisoning baselines under black-box conditions. Further analyses show that KidnapRAG progressively weakens the original retrieval intent, redirects retrieval behavior, and increases reliance on attacker-controlled evidence.</abstract>
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%0 Conference Proceedings
%T KidnapRAG: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems
%A Choi, Chanwoo
%A Kim, Euntae
%A Lee, Kyuho
%A Chun, Youngsam
%A Jeong, Jinhee
%A Kim, Eunmi
%A Oh, Myunggyo
%A Jang, Junseo
%A Chang, Buru
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F choi-etal-2026-kidnaprag
%X Retrieval-Augmented Generation (RAG) systems are vulnerable to poisoning attacks that inject malicious documents into the retrieval process to manipulate model outputs. Recent Agentic RAG systems are more robust to such attacks because they iteratively perform retrieval and reasoning, allowing them to ignore weakly relevant poisoned documents and preserve the reasoning chain induced by the user query. However, existing attacks on Agentic RAG systems often assume white-box access to system prompts, reasoning traces, retrievers, or model parameters, limiting their applicability in realistic settings. In this paper, we study black-box poisoning attacks against Agentic RAG systems, where the attacker can only publish externally retrievable poisoned documents. We propose KidnapRAG, a sequential poisoning attack that hijacks the agent’s multi-step reasoning chain using three role-specific documents: Bait, Chain-Link, and Mal-Ins, which attract initial retrieval, induce query reformulation, and provide attacker-controlled evidence, respectively. Experiments across multiple Agentic RAG frameworks, LLM backbones, and benchmarks show that KidnapRAG consistently outperforms existing poisoning baselines under black-box conditions. Further analyses show that KidnapRAG progressively weakens the original retrieval intent, redirects retrieval behavior, and increases reliance on attacker-controlled evidence.
%U https://aclanthology.org/2026.emnlp-main.2359/
%P 53561-53590
Markdown (Informal)
[KidnapRAG: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems](https://aclanthology.org/2026.emnlp-main.2359/) (Choi et al., EMNLP 2026)
ACL
- Chanwoo Choi, Euntae Kim, Kyuho Lee, Youngsam Chun, Jinhee Jeong, Eunmi Kim, Myunggyo Oh, Junseo Jang, and Buru Chang. 2026. KidnapRAG: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 53561–53590, Budapest, Hungary. Association for Computational Linguistics.