Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data
  Could Be Secretly Stolen!
Fine-tuning on open-source Large Language Models (LLMs) with proprietary data
is now a standard practice for downstream developers to obtain task-specific
LLMs. Surprisingly, we reveal a new and concerning risk along with the
practice: the creator of the open-source LLMs can later extract the private
downstream fine-tuning data through simple backdoor training, only requiring
black-box access to the fine-tuned downstream model. Our comprehensive
experiments, across 4 popularly used open-source models with 3B to 32B
parameters and 2 downstream datasets, suggest that the extraction performance
can be strikingly high: in practical settings, as much as 76.3% downstream
fine-tuning data (queries) out of a total 5,000 samples can be perfectly
extracted, and the success rate can increase to 94.9% in more ideal settings.
We also explore a detection-based defense strategy but find it can be bypassed
with improved attack. Overall, we highlight the emergency of this newly
identified data breaching risk in fine-tuning, and we hope that more follow-up
research could push the progress of addressing this concerning risk. The code
and data used in our experiments are released at
https://github.com/thu-coai/Backdoor-Data-Extraction.
682e8add58a17fe0e9ec02e6
Zhexin Zhang
682e8add58a17fe0e9ec02e7
Yuhao Sun
682e8add58a17fe0e9ec02e8
Junxiao Yang
claimed_verified
2025-05-22T07:16:27.527Z
65d859a3661492b25c46a117
/avatars/06a547a00b472f512a25eef4ddd047bf.svg
Junxiao Yang
user
yangjunxiao2021
682e8add58a17fe0e9ec02e9
Shiyao Cui
682e8add58a17fe0e9ec02ea
Hongning Wang
682e8add58a17fe0e9ec02eb
Minlie Huang
Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data
  Could Be Secretly Stolen!
Fine-tuning on open-source Large Language Models (LLMs) with proprietary data
is now a standard practice for downstream developers to obtain task-specific
LLMs. Surprisingly, we reveal a new and concerning risk along with the
practice: the creator of the open-source LLMs can later extract the private
downstream fine-tuning data through simple backdoor training, only requiring
black-box access to the fine-tuned downstream model. Our comprehensive
experiments, across 4 popularly used open-source models with 3B to 32B
parameters and 2 downstream datasets, suggest that the extraction performance
can be strikingly high: in practical settings, as much as 76.3% downstream
fine-tuning data (queries) out of a total 5,000 samples can be perfectly
extracted, and the success rate can increase to 94.9% in more ideal settings.
We also explore a detection-based defense strategy but find it can be bypassed
with improved attack. Overall, we highlight the emergency of this newly
identified data breaching risk in fine-tuning, and we hope that more follow-up
research could push the progress of addressing this concerning risk. The code
and data used in our experiments are released at
https://github.com/thu-coai/Backdoor-Data-Extraction.
There is a newly identified risk that creators of open-source LLMs can extract fine-tuning data from downstream models through backdoor training, even with black-box access.
Large Language Models fine-tuning open-source black-box access backdoor training data extraction data breach