most citedRAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2026

Trust Me, Import This: Dependency Steering Attacks via Malicious Agent Skills

Yiyong Liu, Chia-Yi Hsu, Chun-Ying Huang +3

LLM-powered coding agents increasingly make software supply chain decisions. They generate imports, recommend packages, and write installation commands. Prior work showed that thes…

cs.MA2026

OrgAgent: Organize Your Multi-Agent System like a Company

Yiru Wang, Xinyue Shen, Yaohui Han +3

While large language model-based multi-agent systems have shown strong potential for complex reasoning, how to effectively organize multiple agents remains an open question. In thi…

cs.LG2025★ 1 cited

Rethinking Data Protection in the (Generative) Artificial Intelligence Era

Yiming Li, Shuo Shao, Yu He +8

The (generative) artificial intelligence (AI) era has profoundly reshaped the meaning and value of data. No longer confined to static content, data now permeates every stage of the…

cs.CL2025★ 2 cited

RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models

Bang An, Shiyue Zhang, Mark Dredze

Efforts to ensure the safety of large language models (LLMs) include safety fine-tuning, evaluation, and red teaming. However, despite the widespread use of the Retrieval-Augmented…

cs.CY2025★ 1 cited

On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective

Yue Huang, Chujie Gao, Siyuan Wu +63

Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…