Cover Image

The Hidden Costs of Domain Fine-Tuning: Pii-Bearing Data Degrades Safety and Increases Leakage

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Choudhari, Jayesh, Singh, Piyush Kumar
Format:	Preprint
Published:	2026
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

Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA
by: Wang, Fei, et al.
Published: (2025)

CTIGuardian: A Few-Shot Framework for Mitigating Privacy Leakage in Fine-Tuned LLMs
by: Arachchige, Shashie Dilhara Batan, et al.
Published: (2025)

Self-Mined Hardness for Safety Fine-Tuning
by: Gupta, Prakhar, et al.
Published: (2026)

Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data
by: Akkus, Atilla, et al.
Published: (2024)

MOSAIC: Modular Opinion Summarization using Aspect Identification and Clustering
by: Singh, Piyush Kumar, et al.
Published: (2026)

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

Information Leakage from Data Updates in Machine Learning Models
by: Hui, Tian, et al.
Published: (2023)

Data Leakage in Automotive Perception: Practitioners' Insights
by: Babu, Md Abu Ahammed, et al.
Published: (2026)

SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection
by: Chen, Shuhao, et al.
Published: (2026)

Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
by: Fan, Mingyuan, et al.
Published: (2022)

Functional Encryption in Secure Neural Network Training: Data Leakage and Practical Mitigations
by: Ioniţă, Alexandru, et al.
Published: (2025)

Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
by: Ward, Joshua, et al.
Published: (2025)

When Safety Geometry Collapses: Fine-Tuning Vulnerabilities in Agentic Guard Models
by: Hossain, Ismail, et al.
Published: (2026)

NeST: Neuron Selective Tuning for LLM Safety
by: Behrouzi, Sasha, et al.
Published: (2026)

Efficient Differentially Private Fine-Tuning of Diffusion Models
by: Liu, Jing, et al.
Published: (2024)

Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets
by: Lu, Ning, et al.
Published: (2025)

Location Leakage in Federated Signal Maps
by: Bakopoulou, Evita, et al.
Published: (2021)

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

TrojanPraise: Jailbreak LLMs via Benign Fine-Tuning
by: Xie, Zhixin, et al.
Published: (2026)

Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing
by: Fu, Chengyan, et al.
Published: (2024)

In-Context Probing for Membership Inference in Fine-Tuned Language Models
by: Lu, Zhexi, et al.
Published: (2025)

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
by: Allouah, Youssef, et al.
Published: (2024)

No, of Course I Can! Deeper Fine-Tuning Attacks That Bypass Token-Level Safety Mechanisms
by: Kazdan, Joshua, et al.
Published: (2025)

Driving Privacy Forward: Mitigating Information Leakage within Smart Vehicles through Synthetic Data Generation
by: Parikh, Krish
Published: (2024)

Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks
by: Kuo, Kevin, et al.
Published: (2026)

Learning to Localize Leakage of Cryptographic Sensitive Variables
by: Gammell, Jimmy, et al.
Published: (2025)

PAE MobiLLM: Privacy-Aware and Efficient LLM Fine-Tuning on the Mobile Device via Additive Side-Tuning
by: Yang, Xingke, et al.
Published: (2025)

LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs
by: Jha, Piyush, et al.
Published: (2024)

Lorica: A Synergistic Fine-Tuning Framework for Advancing Personalized Adversarial Robustness
by: Qi, Tianyu, et al.
Published: (2025)

Fine-tuning of Large Language Models for Domain-Specific Cybersecurity Knowledge
by: Huang, Yuan
Published: (2025)

Analysis of Privacy Leakage in Federated Large Language Models
by: Vu, Minh N., et al.
Published: (2024)

Information Leakage from Embedding in Large Language Models
by: Wan, Zhipeng, et al.
Published: (2024)

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

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models
by: Sha, Haichao, et al.
Published: (2026)

Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning
by: Hasan, Adib, et al.
Published: (2024)

Discovering Universal Activation Directions for PII Leakage in Language Models
by: Marchyok, Leo, et al.
Published: (2026)

Understanding Deep Gradient Leakage via Inversion Influence Functions
by: Zhang, Haobo, et al.
Published: (2023)

Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with Limited Clean Data
by: Zhang, Jiale, et al.
Published: (2025)

Krait: A Backdoor Attack Against Graph Prompt Tuning
by: Song, Ying, et al.
Published: (2024)

Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting
by: Ali, Mahad, et al.
Published: (2025)