Papers by Lan Emily Zhang
AIP: Subverting Retrieval-Augmented Generation via Adversarial Instructional Prompt (2025.emnlp-main)
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| Challenge: | Existing RAG attacks rely on manipulating user queries, but exploit instructional prompts to manipulate RAG outputs covertly. |
| Approach: | They propose an attack that exploits adversarial instructional prompts to manipulate RAG outputs . they propose a query generation strategy that simulates realistic linguistic variation in user queries . |
| Outcome: | The proposed attack exploits instructional prompts to manipulate RAG outputs . it achieves up to 95.23% attack success rate while maintaining benign functionality . |
Your RAG is Unfair: Exposing Fairness Vulnerabilities in Retrieval-Augmented Generation via Backdoor Attacks (2025.emnlp-main)
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Gaurav Bagwe, Saket Sanjeev Chaturvedi, Xiaolong Ma, Xiaoyong Yuan, Kuang-Ching Wang, Lan Emily Zhang
| Challenge: | Retrieval-augmented generation (RAG) enhances factual grounding but introduces new attack surfaces, particularly through backdoor attacks. |
| Approach: | They propose a framework that exposes fairness vulnerabilities in RAG through a two-phase backdoor attack. |
| Outcome: | Empirical results show that BiasRAG achieves high attack success rates while remaining undetectable under standard fairness evaluations. |