# Manipulating Large Language Models to Increase Product Visibility

arXiv: https://arxiv.org/abs/2404.07981 · HTML: https://mlsanity.com/paper/2404.07981/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models](https://mlsanity.com/paper/2310.04451/) (2023)
- [End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering](https://mlsanity.com/paper/2106.05346/) (2021)
- [Here Comes The AI Worm: Unleashing Zero-click Worms that Target GenAI-Powered Applications](https://mlsanity.com/paper/2403.02817/) (2024)
- [Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer](https://mlsanity.com/paper/2212.02027/) (2022)
- [Crosslingual Retrieval Augmented In-context Learning for Bangla](https://mlsanity.com/paper/2311.00587/) (2023)
- [BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer](https://mlsanity.com/paper/2304.09649/) (2023)

## Built on by (most cited first, 28 of 28)

- [Dissecting Adversarial Robustness of Multimodal LM Agents](https://mlsanity.com/paper/2406.12814/) (2024)
- [Adversarial Search Engine Optimization for Large Language Models](https://mlsanity.com/paper/2406.18382/) (2024)
- [GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based Search](https://mlsanity.com/paper/2412.20953/) (2024)
- [Generative Engine Optimization: How to Dominate AI Search](https://mlsanity.com/paper/2509.08919/) (2025)
- [ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search](https://mlsanity.com/paper/2509.23519/) (2025)
- [C-SEO Bench: Does Conversational SEO Work?](https://mlsanity.com/paper/2506.11097/) (2025)
- [A survey on fairness of large language models in e-commerce: progress, application, and challenge](https://mlsanity.com/paper/2405.13025/) (2024)
- [AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization](https://mlsanity.com/paper/2603.20213/) (2026)
- [Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines](https://mlsanity.com/paper/2501.00745/) (2025)
- [Role-Augmented Intent-Driven Generative Search Engine Optimization](https://mlsanity.com/paper/2508.11158/) (2025)
- [SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization](https://mlsanity.com/paper/2602.12187/) (2026)
- [Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behavior](https://mlsanity.com/paper/2603.29979/) (2026)
- [Controlling Output Rankings in Generative Engines for LLM-based Search](https://mlsanity.com/paper/2602.03608/) (2026)
- [EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents](https://mlsanity.com/paper/2605.12887/) (2026)
- [Beyond SEO: A Transformer-Based Approach for Reinventing Web Content Optimisation](https://mlsanity.com/paper/2507.03169/) (2025)
- [Caption Injection for Optimization in Generative Search Engine](https://mlsanity.com/paper/2511.04080/) (2025)
- [When Optimization Becomes Manipulation: Defending Generative Search against Malicious Generative Engine Optimization](https://mlsanity.com/paper/2609.02964/) (2026)
- [The Vulnerability of LLM Rankers to Prompt Injection Attacks](https://mlsanity.com/paper/2602.16752/) (2026)
- [Agent-Facing Information Design in LLM Tool Registries](https://mlsanity.com/paper/2605.23916/) (2026)
- [Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots](https://mlsanity.com/paper/2606.12439/) (2026)
- [Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes](https://mlsanity.com/paper/2608.11390/) (2026)
- [CHASE: How Content Ecosystems Are Reshaped When Ranking Is the Only Target](https://mlsanity.com/paper/2608.30466/) (2026)
- [AX is the New AEO](https://mlsanity.com/paper/2609.34951/) (2026)
- [What Drives Citations in Production Large Language Models? An Observational Multi-Method Study of Two Million AI Citations Across Ten Thousand Web Pages](https://mlsanity.com/paper/2609.35077/) (2026)
- [Conversational Capture: A Trajectory-Level Framework for Evaluating Generative Engine Optimization in Multi-turn Human-Agent Interaction](https://mlsanity.com/paper/2609.40069/) (2026)
- [Which Metadata Matters for LLM Tool Selection in MCP? Evidence from Controlled Factor-Level Interventions](https://mlsanity.com/paper/2610.14334/) (2026)
- [What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO)](https://mlsanity.com/paper/2610.15033/) (2026)
- [Twenty Days Later: A Pre-Registered Test of History-Based Forecasts of Brand Mentions in Gemini Answers](https://mlsanity.com/paper/2610.19608/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly being integrated into search engines to provide natural language responses tailored to user queries. Customers and end-users are also becoming more dependent on these models for quick and easy purchase decisions. In this work, we investigate whether recommendations from LLMs can be manipulated to enhance a product's visibility. We demonstrate that adding a strategic text sequence (STS) -- a carefully crafted message -- to a product's information page can significantly increase its likelihood of being listed as the LLM's top recommendation. To understand the impact of STS, we use a catalog of fictitious coffee machines and analyze its effect on two target products: one that seldom appears in the LLM's recommendations and another that usually ranks second. We observe that the strategic text sequence significantly enhances the visibility of both products by increasing their chances of appearing as the top recommendation. This ability to manipulate LLM-generated search responses provides vendors with a considerable competitive advantage and has the potential to disrupt fair market competition. Just as search engine optimization (SEO) revolutionized how webpages are customized to rank higher in search engine results, influencing LLM recommendations could profoundly impact content optimization for AI-driven search services. Code for our experiments is available at https://github.com/aounon/llm-rank-optimizer.