paperMarch 2026Unreviewed

PIDP-Attack: Combining Prompt Injection with Database Poisoning Attacks on Retrieval-Augmented Generation Systems

Haozhen Wang, Haoyue Liu, Jionghao Zhu, Zhichao Wang, Yongxin Guo, Xiaoying Tang

Abstract

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications. However, their practical deployment is often hindered by issues such as outdated knowledge and the tendency to generate hallucinations. To address these limitations, Retrieval-Augmented Generation (RAG) systems have been introduced, enhancing LLMs with external, up-to-date knowledge sources. Despite their advantages, RAG systems remain vulnerable to adversarial attacks, with data poisoning

Framework mappings

OWASP Top 10 for LLM Applications

LLM01Prompt Injection
LLM04Data and Model Poisoning

MITRE ATLAS

AML.T0020Poison Training Data
AML.T0043Craft Adversarial Data
AML.T0051LLM Prompt Injection

Suggested from the entry's categories.

Cite

@misc{wang2026pidpattack,
  title = {{PIDP-Attack: Combining Prompt Injection with Database Poisoning Attacks on Retrieval-Augmented Generation Systems}},
  author = {Haozhen Wang and Haoyue Liu and Jionghao Zhu and Zhichao Wang and Yongxin Guo and Xiaoying Tang},
  year = {2026},
  month = mar,
  eprint = {2603.25164},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2603.25164}
}