arXiv:2501.00745v3 Announce Type: replace Abstract: The increasing integration of Large Language Model (LLM) based search engines has transformed the landscape of information retrieval. However, these systems are vulnerable to adversarial attacks, especially ranking manipulation attacks, where attackers craft webpage content to manipulate the LLM's ranking and promote specific content, gaining an unfair advantage over competitors. In this paper, we study the dynamics of ranking manipulation attacks. We frame this problem as an Infinitely Repeated Prisoners' Dilemma, where multiple players stra
The rapid deployment and integration of Large Language Model (LLM)-based search engines are creating new attack surfaces that security researchers are now actively exploring.
Understanding the vulnerabilities of LLM-based search engines to adversarial attacks, especially ranking manipulation, is critical for ensuring fair information access and preventing economic distortions.
The recognition of 'ranking manipulation attacks' as a sophisticated form of adversarial behavior introduces a new dimension to search engine optimization and digital trust.
- · Cybersecurity researchers
- · Companies developing robust LLM security solutions
- · Users who rely on unmanipulated search results
- · LLM-based search engine providers with weak defenses
- · Businesses relying on organic search for fair competition
- · Users falling victim to manipulated information
Increased investment in adversarial AI detection and LLM robustness.
Development of new regulatory frameworks or industry standards to combat search ranking manipulation.
Potential for a 'manipulation arms race' between attackers and defenders, constantly evolving tactics and countermeasures.
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Read at arXiv cs.CL