Cover Image

In-Context Probing for Membership Inference in Fine-Tuned Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lu, Zhexi, Chi, Hongliang, Baracaldo, Nathalie, Kadhe, Swanand Ravindra, Jeon, Yuseok, Yu, Lei
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SafeCOMM: A Study on Safety Degradation in Fine-Tuned Telecom Large Language Models
by: Djuhera, Aladin, et al.
Published: (2025)

Turning Generative Models Degenerate: The Power of Data Poisoning Attacks
by: Jiang, Shuli, et al.
Published: (2024)

Towards a Re-evaluation of Data Forging Attacks in Practice
by: Suliman, Mohamed, et al.
Published: (2024)

Evaluating the Dynamics of Membership Privacy in Deep Learning
by: Chen, Yuetian, et al.
Published: (2025)

Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble
by: Wang, Zhiqi, et al.
Published: (2025)

SoK: Reducing the Vulnerability of Fine-tuned Language Models to Membership Inference Attacks
by: Amit, Guy, et al.
Published: (2024)

Ensembling Membership Inference Attacks Against Tabular Generative Models
by: Ward, Joshua, et al.
Published: (2025)

On the Robustness of Graph Reduction Against GNN Backdoor
by: Zhu, Yuxuan, et al.
Published: (2024)

Noise as a Probe: Membership Inference Attacks on Diffusion Models Leveraging Initial Noise
by: Lian, Puwei, et al.
Published: (2026)

Membership Inference Attacks on Sequence Models
by: Rossi, Lorenzo, et al.
Published: (2025)

Membership Inference Attacks for Retrieval Based In-Context Learning for Document Question Answering
by: Kulkarni, Tejas, et al.
Published: (2026)

Imitative Membership Inference Attack
by: Du, Yuntao, et al.
Published: (2025)

Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
by: Liu, Zhenlong, et al.
Published: (2024)

Membership Inference Attacks for Unseen Classes
by: Thaker, Pratiksha, et al.
Published: (2025)

Membership Inference Attacks Beyond Overfitting
by: Khalil, Mona, et al.
Published: (2025)

Cascading and Proxy Membership Inference Attacks
by: Du, Yuntao, et al.
Published: (2025)

Membership Inference Attacks on Large-Scale Models: A Survey
by: Wu, Hengyu, et al.
Published: (2025)

Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
by: Fu, Wenjie, et al.
Published: (2023)

MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training
by: Li, Jiacheng, et al.
Published: (2023)

Practical Bayes-Optimal Membership Inference Attacks
by: Lassila, Marcus, et al.
Published: (2025)

On Reliability of Efficient Membership Inference Vulnerability Evaluation
by: Jälkö, Joonas, et al.
Published: (2026)

The Sample Complexity of Membership Inference and Privacy Auditing
by: Haghifam, Mahdi, et al.
Published: (2025)

Assessing the Effectiveness of Membership Inference on Generative Music
by: Chow, Kurtis, et al.
Published: (2025)

Order of Magnitude Speedups for LLM Membership Inference
by: Zhang, Rongting, et al.
Published: (2024)

Improved Membership Inference Attacks Against Language Classification Models
by: Shachor, Shlomit, et al.
Published: (2023)

Differentially Private Subspace Fine-Tuning for Large Language Models
by: Zheng, Lele, et al.
Published: (2026)

Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
by: Jin, Heng, et al.
Published: (2026)

Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
by: Wen, Yuxin, et al.
Published: (2024)

Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data
by: Zhang, Jie, et al.
Published: (2024)

Rethinking Membership Inference Attacks Against Transfer Learning
by: Wu, Cong, et al.
Published: (2025)

MRMMIA: Membership Inference Attacks on Memory in Chat Agents
by: Chen, Kai, et al.
Published: (2026)

Low-Cost High-Power Membership Inference Attacks
by: Zarifzadeh, Sajjad, et al.
Published: (2023)

Mitigating Membership Inference Vulnerability in Personalized Federated Learning
by: Jung, Kangsoo, et al.
Published: (2025)

Secure Aggregation is Not Private Against Membership Inference Attacks
by: Ngo, Khac-Hoang, et al.
Published: (2024)

Context-Aware Membership Inference Attacks against Pre-trained Large Language Models
by: Chang, Hongyan, et al.
Published: (2024)

The Tail Tells All: Estimating Model-Level Membership Inference Vulnerability Without Reference Models
by: Dodd, Euodia, et al.
Published: (2025)

ReproMIA: A Comprehensive Analysis of Model Reprogramming for Proactive Membership Inference Attacks
by: Huang, Chihan, et al.
Published: (2026)

Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective
by: Lian, Puwei, et al.
Published: (2025)

Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
by: Bai, Yuxuan, et al.
Published: (2025)

SeqMIA: Sequential-Metric Based Membership Inference Attack
by: Li, Hao, et al.
Published: (2024)