arXiv AI June 30, 2026 By Cristhian Kapelinski, Diego Kreutz

Decomposing Memorization Reduction in Privacy-Preserving Fine-Tuning of SLMs for CSIRTs

arXiv:2606. 28479v1 Announce Type: cross Abstract: CSIRTs increasingly fine tune language models on vulnerability scan records, but these records expose internal network topology and create privacy risks under regulations such as GDPR and LGPD.

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arXiv Machine Learning

2d ago

SparLeak: Privacy Leakage from Sparse Attention in LLM Inference on Shared GPUs

arXiv:2609.38830v1 Announce Type: new Abstract: Sparse attention is widely used to accelerate long-context inference in modern large language models (LLMs), but its input-dependent execution behavior...

By Fahao Chen, Linkang Du, Jinhao Zhou, Peng Li, Zhou Su

llms efficiency safety

arXiv Machine Learning

Jun 17

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

arXiv:2606. 17110v1 Announce Type: cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets.

By Md Abdullah Al Mamun, Ngoc Phu Doan, Pedram Zaree, Ihsen Alouani, Nael Abu-Ghazaleh

llms multimodal safety

arXiv Machine Learning

Jun 9

SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)

arXiv:2606. 08372v1 Announce Type: cross Abstract: Synthetic data is increasingly promoted as a privacy-preserving substitute for releasing sensitive tabular records, yet its central adversarial threat ("reconstruction", the recovery of an individual's hidden attribute values from a synthetic release and a handful of known quasi-identifiers) has been studied only in scattered, hard-to-compare settings.

By Steven Golob, Sikha Pentyala, Martine De Cock

benchmarks safety

arXiv Machine Learning

Sep 1

Balancing Privacy, Utility, and Safety in LLM Alignment through Preference Optimization

arXiv:2608.30141v1 Announce Type: cross Abstract: Preference optimization is widely used to align large language models with human preferences, but preference-data composition may also influence priv...

By Dishu Yang, Jingjing Liu, Jize Li

llms reinforcement-learning efficiency safety

arXiv AI

Jun 10

Advancing the State-of-the-Art in Empirical Privacy Auditing

arXiv:2606. 10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples.

By Nicole Mitchell, Galen Andrew, Arun Ganesh, Brendan McMahan, Peter Kairouz

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jun 9

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models

arXiv:2606. 09401v1 Announce Type: new Abstract: Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees.

By Bart{\l}omiej Marek, Lorenzo Rossi, Vincent Hanke, Xun Wang, Michael Backes, Franziska Boenisch, Adam Dziedzic

llms fine-tuning benchmarks safety

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Related stories

SparLeak: Privacy Leakage from Sparse Attention in LLM Inference on Shared GPUs

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)

Balancing Privacy, Utility, and Safety in LLM Alignment through Preference Optimization

Advancing the State-of-the-Art in Empirical Privacy Auditing

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models