mlsanityData to 7 Oct 2026

Manipulating Large Language Models to Increase Product Visibility

arXiv 2404.07981 ↗First posted 11 Apr 2024cs.IR cs.AI cs.CLcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 11 Apr 2024.

Builds on

11 of 20 references.

References · 11

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  3. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,565
  4. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  5. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  6. 2023AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Modelscited by 738
  7. 2021End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answeringcited by 133
  8. 2024Here Comes The AI Worm: Unleashing Zero-click Worms that Target GenAI-Powered Applicationscited by 58
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 7; 2026: 13.

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  1. 2024Dissecting Adversarial Robustness of Multimodal LM Agentscited by 118
  2. 2024Adversarial Search Engine Optimization for Large Language Modelscited by 37
  3. 2024GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based Searchcited by 20
  4. 2025ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Searchcited by 17
  5. 2025Generative Engine Optimization: How to Dominate AI Searchcited by 16
  6. 2024A survey on fairness of large language models in e-commerce: progress, application, and challengecited by 10
  7. 2025C-SEO Bench: Does Conversational SEO Work?cited by 10
  8. 2026AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimizationcited by 6
  9. 2025Dynamics of Adversarial Attacks on Large Language Model-Based Search Enginescited by 5
  10. 2025Role-Augmented Intent-Driven Generative Search Engine Optimizationcited by 5
  11. 2026SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimizationcited by 5
  12. 2026Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behaviorcited by 5
12 more
  1. 2026Controlling Output Rankings in Generative Engines for LLM-based Searchcited by 3
  2. 2026EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agentscited by 3
  3. 2025Beyond SEO: A Transformer-Based Approach for Reinventing Web Content Optimisationcited by 2
  4. 2025Caption Injection for Optimization in Generative Search Enginecited by 2
  5. 2026When Optimization Becomes Manipulation: Defending Generative Search against Malicious Generative Engine Optimizationcited by 1
  6. 2026Agent-Facing Information Design in LLM Tool Registriescited by 0
  7. 2026Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spotscited by 0
  8. 2026Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomescited by 0
  9. 2026CHASE: How Content Ecosystems Are Reshaped When Ranking Is the Only Targetcited by 0
  10. 2026AX is the New AEOcited by 0
  11. 2026What Drives Citations in Production Large Language Models? An Observational Multi-Method Study of Two Million AI Citations Across Ten Thousand Web Pagescited by 0
  12. 2026Conversational Capture: A Trajectory-Level Framework for Evaluating Generative Engine Optimization in Multi-turn Human-Agent Interactioncited by 0