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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.12574 |
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| _version_ | 1866910981983043584 |
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| author | Chen, Liuji Yang, Xiaofang Lu, Yuanzhuo Zhang, Jinghao Sun, Xin Liu, Qiang Wu, Shu Dong, Jing Wang, Liang |
| author_facet | Chen, Liuji Yang, Xiaofang Lu, Yuanzhuo Zhang, Jinghao Sun, Xin Liu, Qiang Wu, Shu Dong, Jing Wang, Liang |
| contents | Retrieval-Augmented Generation (RAG) systems, widely used to improve the factual grounding of large language models (LLMs), are increasingly vulnerable to poisoning attacks, where adversaries inject manipulated content into the retriever's corpus. While prior research has predominantly focused on single-attacker settings, real-world scenarios often involve multiple, competing attackers with conflicting objectives. In this work, we introduce PoisonArena, the first benchmark to systematically study and evaluate competing poisoning attacks in RAG. We formalize the multi-attacker threat model, where attackers vie to control the answer to the same query using mutually exclusive misinformation. PoisonArena leverages the Bradley-Terry model to quantify each method's competitive effectiveness in such adversarial environments. Through extensive experiments on the Natural Questions and MS MARCO datasets, we demonstrate that many attack strategies successful in isolation fail under competitive pressure. Our findings highlight the limitations of conventional evaluation metrics like Attack Success Rate (ASR) and F1 score and underscore the need for competitive evaluation to assess real-world attack robustness. PoisonArena provides a standardized framework to benchmark and develop future attack and defense strategies under more realistic, multi-adversary conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12574 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Chen, Liuji Yang, Xiaofang Lu, Yuanzhuo Zhang, Jinghao Sun, Xin Liu, Qiang Wu, Shu Dong, Jing Wang, Liang Information Retrieval Retrieval-Augmented Generation (RAG) systems, widely used to improve the factual grounding of large language models (LLMs), are increasingly vulnerable to poisoning attacks, where adversaries inject manipulated content into the retriever's corpus. While prior research has predominantly focused on single-attacker settings, real-world scenarios often involve multiple, competing attackers with conflicting objectives. In this work, we introduce PoisonArena, the first benchmark to systematically study and evaluate competing poisoning attacks in RAG. We formalize the multi-attacker threat model, where attackers vie to control the answer to the same query using mutually exclusive misinformation. PoisonArena leverages the Bradley-Terry model to quantify each method's competitive effectiveness in such adversarial environments. Through extensive experiments on the Natural Questions and MS MARCO datasets, we demonstrate that many attack strategies successful in isolation fail under competitive pressure. Our findings highlight the limitations of conventional evaluation metrics like Attack Success Rate (ASR) and F1 score and underscore the need for competitive evaluation to assess real-world attack robustness. PoisonArena provides a standardized framework to benchmark and develop future attack and defense strategies under more realistic, multi-adversary conditions. |
| title | PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2505.12574 |