Title: Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines Show Chinese title

Title: 基于大型语言模型的搜索引擎上的对抗性攻击的动力学

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 strategically decide whether to cooperate or attack. We analyze the conditions under which cooperation can be sustained, identifying key factors such as attack costs, discount rates, attack success rates, and trigger strategies that influence player behavior. We identify tipping points in the system dynamics, demonstrating that cooperation is more likely to be sustained when players are forward-looking. However, from a defense perspective, we find that simply reducing attack success probabilities can, paradoxically, incentivize attacks under certain conditions. Furthermore, defensive measures to cap the upper bound of attack success rates may prove futile in some scenarios. These insights highlight the complexity of securing LLM-based systems. Our work provides a theoretical foundation and practical insights for understanding and mitigating their vulnerabilities, while emphasizing the importance of adaptive security strategies and thoughtful ecosystem design. Show Chinese abstract

Abstract: 大型语言模型（LLM）驱动的搜索引擎的日益融合，已经改变了信息检索的格局。 然而，这些系统容易受到对抗性攻击的影响，尤其是排名操纵攻击，攻击者精心设计网页内容以操纵LLM的排名，从而推广特定内容，从而在竞争中获得不公平的优势。 在本文中，我们研究了排名操纵攻击的动态。 我们将这个问题框架化为一个无限重复的囚徒困境，其中多个参与者战略性地决定是否合作或攻击。 我们分析了合作可以持续的条件，确定了影响玩家行为的关键因素，如攻击成本、贴现率、攻击成功率和触发策略。 我们识别了系统动态中的临界点，证明当玩家具有前瞻性时，合作更有可能持续。 然而，从防御的角度来看，我们发现仅仅降低攻击成功率，在某些条件下可能会反而激励攻击。 此外，限制攻击成功率上限的防御措施在某些情况下可能无效。 这些见解突显了保护LLM系统复杂性。 我们的工作为理解和减轻其脆弱性提供了理论基础和实践见解，同时强调了自适应安全策略和深思熟虑的生态系统设计的重要性。