activity
20242026
most citedFlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models

3 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026

DiscourseFlip: An Oblique Discourse-Level Opinion Manipulation Attack against Black-box Retrieval-Augmented Generation

Yuyang Gong, Miaokun Chen, Jiawei Liu +5

Retrieval-Augmented Generation (RAG) systems are widely deployed and increasingly influential, but their reliance on external corpora exposes new security risks from poisoned retri…

cs.CR2025

RobustMask: Certified Robustness against Adversarial Neural Ranking Attack via Randomized Masking

Jiawei Liu, Zhuo Chen, Rui Zhu +4

Neural ranking models have achieved remarkable progress and are now widely deployed in real-world applications such as Retrieval-Augmented Generation (RAG). However, like other neu…

cs.CL2025

Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models

Yuyang Gong, Zhuo Chen, Jiawei Liu +5

Retrieval-Augmented Generation (RAG) systems based on Large Language Models (LLMs) have become essential for tasks such as question answering and content generation. However, their…

cs.IR20253 cited

FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models

Zhuo Chen, Yuyang Gong, Jiawei Liu +6

Retrieval-Augmented Generation (RAG) enriches LLMs by dynamically retrieving external knowledge, reducing hallucinations and satisfying real-time information needs. While existing…

cs.CL2024

Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models

Zhuo Chen, Jiawei Liu, Haotan Liu +4

Retrieval-Augmented Generation (RAG) is applied to solve hallucination problems and real-time constraints of large language models, but it also induces vulnerabilities against retr…