# KidnapRAG: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems

arXiv: https://arxiv.org/abs/2607.00422 · HTML: https://mlsanity.com/paper/2607.00422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Placed in

- Retrieval-augmented generation / [Privacy and security of RAG](https://mlsanity.com/rag/privacy-and-security-of-rag/)
- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 16 of 16)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://mlsanity.com/paper/2011.01060/) (2020)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022)
- [AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases](https://mlsanity.com/paper/2407.12784/) (2024)
- [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025)
- [Defending Against Indirect Prompt Injection Attacks With Spotlighting](https://mlsanity.com/paper/2403.14720/) (2024)
- [ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning](https://mlsanity.com/paper/2212.07919/) (2022)
- [TopicAttack: An Indirect Prompt Injection Attack via Topic Transition](https://mlsanity.com/paper/2507.13686/) (2025)
- [The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2502.20995/) (2025)
- [Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems](https://mlsanity.com/paper/2511.01268/) (2025)
- [Step Potential Advantage Estimation: Harnessing Intermediate Confidence and Correctness for Efficient Mathematical Reasoning](https://mlsanity.com/paper/2601.03823/) (2026)
- [Chain-of-Trigger: An Agentic Backdoor that Paradoxically Enhances Agentic Robustness](https://mlsanity.com/paper/2510.08238/) (2025)

## Abstract (arXiv)

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. Our code is publicly available at https://github.com/chanwoochoi316/KidnapRAG.