# InceptionRAG: Stealthy Poisoning Attack Against Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2609.16818 · HTML: https://mlsanity.com/paper/2609.16818/

## 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 29 of its 52 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Placed in

- Retrieval-augmented generation / [Privacy and security of RAG](https://mlsanity.com/rag/privacy-and-security-of-rag/)

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent](https://mlsanity.com/paper/1902.06720/) (2019)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents](https://mlsanity.com/paper/2406.05870/) (2024)
- [Sufficient Context: A New Lens on Retrieval Augmented Generation Systems](https://mlsanity.com/paper/2411.06037/) (2024)
- ["Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models](https://mlsanity.com/paper/2406.19417/) (2024)
- [Phantom: General Backdoor Attacks on Retrieval Augmented Language Generation](https://mlsanity.com/paper/2405.20485/) (2024)
- [RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis](https://mlsanity.com/paper/2411.18948/) (2024)
- [Defending Against Prompt Injection With a Few DefensiveTokens](https://mlsanity.com/paper/2507.07974/) (2025)
- [Traceback of Poisoning Attacks to Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.21668/) (2025)
- [HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2410.22832/) (2024)
- [Secure Retrieval-Augmented Generation against Poisoning Attacks](https://mlsanity.com/paper/2510.25025/) (2025)
- [ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search](https://mlsanity.com/paper/2509.23519/) (2025)
- [ADMIT: Few-shot Knowledge Poisoning Attacks on RAG-based Fact Checking](https://mlsanity.com/paper/2510.13842/) (2025)
- [Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems](https://mlsanity.com/paper/2511.01268/) (2025)
- [Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs](https://mlsanity.com/paper/2506.11415/) (2025)
- [NeuroGenPoisoning: Neuron-Guided Attacks on Retrieval-Augmented Generation of LLM via Genetic Optimization of External Knowledge](https://mlsanity.com/paper/2510.21144/) (2025)
- [ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation Models](https://mlsanity.com/paper/2504.13061/) (2025)
- [RAG-Pull: Turning Retrieval into a Code-Injection Channel via Invisible Unicode Perturbations](https://mlsanity.com/paper/2510.11195/) (2025)
- [Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval](https://mlsanity.com/paper/2512.00804/) (2025)

## Abstract (arXiv)

Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) with external knowledge but have been demonstrated to be vulnerable to corpus poisoning. Existing poisoning attacks against RAG largely focus on single-point explicit injection, where the malicious payload is fully encapsulated within a single document. Consequently, recent mitigation mechanisms have evolved to identify and diminish these threats effectively. In this paper, we first verify that existing mitigation mechanisms are insufficient for a new class of threats: indirect logic induction. Motivated by this observation, we introduce InceptionRAG, a stealthy attack mechanism that subverts the standard attack paradigm. Instead of injecting explicit malicious payloads, InceptionRAG fragments it into a chain of dormant passages. These passages appear harmless and can bypass existing mitigation mechanisms when examined separately. However, when retrieved together, they trigger LLMs to self-deduce target misinformation via multi-hop reasoning. To further improve the applicability of InceptionRAG in black-box settings, we propose zeroth-order suffix optimization (ZOSO) to automate the generation of authoritative suffixes. Extensive evaluations across three datasets and three LLMs demonstrate that InceptionRAG achieves an attack success rate exceeding 80% even under rigorous adversarial constraints. In particular, InceptionRAG shows superior evasion capabilities, effectively bypassing established defenses that mitigate traditional single-document injections. Our findings expose a concerning paradox: the stronger reasoning capabilities of LLMs increase their vulnerability to reasoning-based poisoning attacks. To mitigate potential misuse, we propose a document isolation-based defense, HODOR, which decouples adversarial logical dependencies.