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LLM search engines vulnerable to adversarial ranking attacks, study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper analyzes adversarial attacks on large language model-based search engines, framing the problem as an infinitely repeated Prisoner's Dilemma. The study identifies conditions under which cooperation can be sustained, highlighting the influence of attack costs, discount rates, and trigger strategies. Counterintuitively, the research suggests that reducing attack success probabilities might incentivize attacks in some scenarios, and capping success rates could be ineffective. AI

IMPACT Highlights the complex security challenges and the need for adaptive strategies in LLM-based information retrieval systems.

RANK_REASON Academic paper published on arXiv detailing a theoretical analysis of adversarial attacks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM search engines vulnerable to adversarial ranking attacks, study finds

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Academic paper published on arXiv detailing a theoretical analysis of adversarial attacks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiyang Hu ·

    Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines

    arXiv:2501.00745v3 Announce Type: replace-cross 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 manipul…

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