paperMay 2026Unreviewed

Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions

Wenjuan Li, Yitao Liu, Runze Chen, Rajkumar Buyya

Abstract

Background: Fine-tuning is central to adapting pre-trained Large Language Models (LLMs) to downstream tasks, but its reliance on training data, parameter updates, and reusable components opens entry points for attackers. Threats have evolved from data poisoning and weight tampering to agent manipulation and interface exploitation, yet existing reviews lack a unified framework spanning the full fine-tuning lifecycle. Objective: This paper presents a systematic survey of LLM fine-tuning security a

Framework mappings

OWASP Top 10 for LLM Applications

LLM04Data and Model Poisoning

MITRE ATLAS

AML.T0020Poison Training Data

Suggested from the entry's categories.

Cite

@misc{li2026security,
  title = {{Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions}},
  author = {Wenjuan Li and Yitao Liu and Runze Chen and Rajkumar Buyya},
  year = {2026},
  month = may,
  eprint = {2605.25073},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2605.25073}
}