# Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models

arXiv: https://arxiv.org/abs/2502.01386 · HTML: https://mlsanity.com/paper/2502.01386/

## Key facts

- Cited by 33 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 47 of its 56 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Placed in

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

## Builds on (most cited first, 30 of 47)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022)
- [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://mlsanity.com/paper/2309.00614/) (2023)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [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)
- [PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models](https://mlsanity.com/paper/2402.07867/) (2024)
- [Demystifying Prompts in Language Models via Perplexity Estimation](https://mlsanity.com/paper/2212.04037/) (2022)
- [Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval](https://mlsanity.com/paper/2108.05540/) (2021)
- [MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots](https://mlsanity.com/paper/2307.08715/) (2023)
- [Poisoning Retrieval Corpora by Injecting Adversarial Passages](https://mlsanity.com/paper/2310.19156/) (2023)
- [Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents](https://mlsanity.com/paper/2406.05870/) (2024)
- [WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences](https://mlsanity.com/paper/2306.07906/) (2023)
- [Backdoor Attacks for In-Context Learning with Language Models](https://mlsanity.com/paper/2307.14692/) (2023)
- [BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models](https://mlsanity.com/paper/2406.00083/) (2024)
- [The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs](https://mlsanity.com/paper/2501.10970/) (2025)
- [Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks](https://mlsanity.com/paper/2304.14732/) (2023)
- [Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://mlsanity.com/paper/2407.13193/) (2024)
- [TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models](https://mlsanity.com/paper/2405.13401/) (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)
- [Certified Robustness to Text Adversarial Attacks by Randomized [MASK]](https://mlsanity.com/paper/2105.03743/) (2021)

## Built on by (most cited first, 30 of 33)

- [Benchmarking Poisoning Attacks against Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.18543/) (2025)
- [ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search](https://mlsanity.com/paper/2509.23519/) (2025)
- [Defending Against Knowledge Poisoning Attacks During Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.02835/) (2025)
- [RIPRAG: Hack a Black-box Retrieval-Augmented Generation Question-Answering System with Reinforcement Learning](https://mlsanity.com/paper/2510.10008/) (2025)
- [Confundo: Learning to Generate Robust Poison for Practical RAG Systems](https://mlsanity.com/paper/2602.06616/) (2026)
- [MIRAGE: Misleading Retrieval-Augmented Generation via Black-box and Query-agnostic Poisoning Attacks](https://mlsanity.com/paper/2512.08289/) (2025)
- [Hijacking Agent Memory: Stealthy Trojan Attacks Through Conversational Interaction](https://mlsanity.com/paper/2605.29960/) (2026)
- [UniC-RAG: Universal Knowledge Corruption Attacks to Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.18652/) (2025)
- [Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval](https://mlsanity.com/paper/2512.00804/) (2025)
- [Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions](https://mlsanity.com/paper/2604.08304/) (2026)
- [When AI reviews science: Can we trust the referee?](https://mlsanity.com/paper/2604.23593/) (2026)
- [Data Agents Under Attack: Vulnerabilities in LLM-Driven Analytical Systems](https://mlsanity.com/paper/2606.08661/) (2026)
- [Agent-Assisted Side-Channel Attacks on Non-Prefix KV Cache in RAG](https://mlsanity.com/paper/2606.21842/) (2026)
- [MillStone: How Open-Minded Are LLMs?](https://mlsanity.com/paper/2509.11967/) (2025)
- [RobustMask: Certified Robustness against Adversarial Neural Ranking Attack via Randomized Masking](https://mlsanity.com/paper/2512.23307/) (2025)
- [RADAR: Defending RAG Dynamically against Retrieval Corruption](https://mlsanity.com/paper/2605.22041/) (2026)
- [DiscourseFlip: An Oblique Discourse-Level Opinion Manipulation Attack against Black-box Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.01212/) (2026)
- [Conflict-Aware Retriever Editing for Knowledge Injection Attacks on LLM-Based RAG Systems](https://mlsanity.com/paper/2606.18310/) (2026)
- [Retrieved But Not Reliable: A Survey on Attacks, and Defenses in Retrieval-Augmented Generation](https://mlsanity.com/paper/2608.24977/) (2026)
- [Misleading the Planner through Deceptive Resumes: Registration-Time Injection in Centralized Multi-Agent Systems](https://mlsanity.com/paper/2609.15516/) (2026)
- [Provably Secure Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.01084/) (2025)
- [Hidden in the Metadata: Stealth Poisoning Attacks on Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.00172/) (2026)
- [FlashRT: Towards Computationally and Memory Efficient Red-Teaming for Prompt Injection and Knowledge Corruption](https://mlsanity.com/paper/2604.28157/) (2026)
- [CleanBase: Detecting Malicious Documents in RAG Knowledge Databases](https://mlsanity.com/paper/2605.00460/) (2026)
- [Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models](https://mlsanity.com/paper/2605.01591/) (2026)
- [Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems](https://mlsanity.com/paper/2605.01758/) (2026)
- [BiRD: A Bidirectional Ranking Defense Mechanism for Retrieval Augmented Generation](https://mlsanity.com/paper/2605.20123/) (2026)
- [Tracing Target Answers in Poisoned Retrieval Corpora via Token Influence Attribution](https://mlsanity.com/paper/2606.25721/) (2026)
- [Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability](https://mlsanity.com/paper/2607.10402/) (2026)
- [Eliciting Intrinsic Hallucinations in LLMs via Semantically Equivalent Adversarial Attacks](https://mlsanity.com/paper/2608.04286/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems based on Large Language Models (LLMs) have become essential for tasks such as question answering and content generation. However, their increasing impact on public opinion and information dissemination has made them a critical focus for security research due to inherent vulnerabilities. Previous studies have predominantly addressed attacks targeting factual or single-query manipulations. In this paper, we address a more practical scenario: topic-oriented adversarial opinion manipulation attacks on RAG models, where LLMs are required to reason and synthesize multiple perspectives, rendering them particularly susceptible to systematic knowledge poisoning. Specifically, we propose Topic-FlipRAG, a two-stage manipulation attack pipeline that strategically crafts adversarial perturbations to influence opinions across related queries. This approach combines traditional adversarial ranking attack techniques and leverages the extensive internal relevant knowledge and reasoning capabilities of LLMs to execute semantic-level perturbations. Experiments show that the proposed attacks effectively shift the opinion of the model's outputs on specific topics, significantly impacting user information perception. Current mitigation methods cannot effectively defend against such attacks, highlighting the necessity for enhanced safeguards for RAG systems, and offering crucial insights for LLM security research.