# Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models

arXiv: https://arxiv.org/abs/2407.13757 · HTML: https://mlsanity.com/paper/2407.13757/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Placed in

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

## Builds on (most cited first, 21 of 21)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://mlsanity.com/paper/2309.00614/) (2023)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [Adversarial Examples for Evaluating Reading Comprehension Systems](https://mlsanity.com/paper/1707.07328/) (2017)
- [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://mlsanity.com/paper/2306.11698/) (2023)
- [Multi-step Jailbreaking Privacy Attacks on ChatGPT](https://mlsanity.com/paper/2304.05197/) (2023)
- [Poisoning Web-Scale Training Datasets is Practical](https://mlsanity.com/paper/2302.10149/) (2023)
- [Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval](https://mlsanity.com/paper/2108.05540/) (2021)
- [Poisoning Retrieval Corpora by Injecting Adversarial Passages](https://mlsanity.com/paper/2310.19156/) (2023)
- [Certifiably Robust RAG against Retrieval Corruption](https://mlsanity.com/paper/2405.15556/) (2024)
- [Weak-to-Strong Jailbreaking on Large Language Models](https://mlsanity.com/paper/2401.17256/) (2024)
- [BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models](https://mlsanity.com/paper/2406.00083/) (2024)
- [Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations](https://mlsanity.com/paper/2404.13948/) (2024)
- [BadPrompt: Backdoor Attacks on Continuous Prompts](https://mlsanity.com/paper/2211.14719/) (2022)
- [PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models](https://mlsanity.com/paper/2204.01321/) (2022)
- [Adversarial Semantic Collisions](https://mlsanity.com/paper/2011.04743/) (2020)
- [Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models](https://mlsanity.com/paper/2209.06506/) (2022)
- [Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications](https://mlsanity.com/paper/2404.17196/) (2024)
- [Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method](https://mlsanity.com/paper/2308.09861/) (2023)
- [MAWSEO: Adversarial Wiki Search Poisoning for Illicit Online Promotion](https://mlsanity.com/paper/2304.11300/) (2023)

## Built on by (most cited first, 11 of 11)

- [A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures](https://mlsanity.com/paper/2506.19676/) (2025)
- [Practical Poisoning Attacks against Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.03957/) (2025)
- [GraphRAG under Fire](https://mlsanity.com/paper/2501.14050/) (2025)
- [Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systems](https://mlsanity.com/paper/2601.07072/) (2026)
- [RAG Security and Privacy: Formalizing the Threat Model and Attack Surface](https://mlsanity.com/paper/2509.20324/) (2025)
- [POISONCRAFT: Practical Poisoning of Retrieval-Augmented Generation for Large Language Models](https://mlsanity.com/paper/2505.06579/) (2025)
- [Securing RAG: A Risk Assessment and Mitigation Framework](https://mlsanity.com/paper/2505.08728/) (2025)
- [Safety Degradation in AI Agents](https://mlsanity.com/paper/2505.14215/) (2025)
- [Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation](https://mlsanity.com/paper/2508.10404/) (2025)
- [LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems](https://mlsanity.com/paper/2509.10682/) (2025)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) is applied to solve hallucination problems and real-time constraints of large language models, but it also induces vulnerabilities against retrieval corruption attacks. Existing research mainly explores the unreliability of RAG in white-box and closed-domain QA tasks. In this paper, we aim to reveal the vulnerabilities of Retrieval-Enhanced Generative (RAG) models when faced with black-box attacks for opinion manipulation. We explore the impact of such attacks on user cognition and decision-making, providing new insight to enhance the reliability and security of RAG models. We manipulate the ranking results of the retrieval model in RAG with instruction and use these results as data to train a surrogate model. By employing adversarial retrieval attack methods to the surrogate model, black-box transfer attacks on RAG are further realized. Experiments conducted on opinion datasets across multiple topics show that the proposed attack strategy can significantly alter the opinion polarity of the content generated by RAG. This demonstrates the model's vulnerability and, more importantly, reveals the potential negative impact on user cognition and decision-making, making it easier to mislead users into accepting incorrect or biased information.