The paper investigates how federated unlearning systems that broadcast updated linear classifiers after each client update can inadvertently leak the compact, additive summaries used for deletion. By submitting known changes and analyzing the returned classifiers, an attacker can recover the deleted sample’s class or even reinstate it. Experiments on MNIST and CIFAR‑10 show that high‑precision broadcasts enable exact label recovery, while lower precision limits fine‑grained recovery and diverse responses can prevent identification.
By Yijun Quan, Giovanni Montana
arXiv:2610. 34934v1 Announce Type: new Abstract: Machine learning systems trained on untrusted data are vulnerable to data and backdoor poisoning attacks, in which an adversary corrupts training and test samples to manipulate predictions at inference time.
By Evan Rose, Alina Oprea
The paper presents a fast machine unlearning method that uses Hessian analysis to identify correlated training data and applies a closed‑form update rule. This approach achieves an 82× speedup over traditional influence‑function unlearning while maintaining or slightly improving model accuracy. Experiments on seven dataset‑architecture pairs, including CIFAR‑100 with ResNet‑50, show strong forgetting performance and low vulnerability to membership inference attacks.
By Ayushi Thakur, Ruchir Gupta, Amit Kumar Jaiswal, Prayag Tiwari
arXiv:2605.14605v3 Announce Type: replace-cross
Abstract: Model providers increasingly release the weights of large language models. Although these models are safety-aligned before release, their saf...
By Itay Zloczower, Eyal Lenga, Gilad Gressel, Yisroel Mirsky
arXiv:2602. 06911v2 Announce Type: replace-cross Abstract: As increasingly capable open-weight large language models (LLMs) are deployed, improving their tamper resistance against unsafe modifications, whether accidental or intentional, becomes critical to minimize risks.
By Saad Hossain, Tom Tseng, Punya Syon Pandey, Samanvay Vajpayee, Matthew Kowal, Nayeema Nonta, Samuel Simko, Stephen Casper, Zhijing Jin, Kellin Pelrine, Sirisha Rambhatla
arXiv:2412. 09119v3 Announce Type: replace Abstract: Machine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowledge gaps post-deployment.
By Youssef Allouah, Joshua Kazdan, Rachid Guerraoui, Sanmi Koyejo
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
arXiv:2406. 05670v3 Announce Type: replace Abstract: Modern machine learning pipelines leverage large amounts of public data, making it infeasible to guarantee data quality and leaving models open to poisoning and backdoor attacks.
By Philip Sosnin, Mark N. M\"uller, Maximilian Baader, Calvin Tsay, Matthew Wicker
arXiv:2607. 07314v1 Announce Type: cross Abstract: Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself.
By Chongkai Li, Bang Zhang, Wenjian Luo
arXiv:2609.05794v1 Announce Type: cross
Abstract: Open-weight large language models face a low-cost white-box threat from representation engineering attacks. Attackers can estimate refusal directions...
By Tian Gao, Zhipeng Xie, Yuhao Wu, Junhua Liu, Xin Fang
The paper introduces a preemptive unlearning technique called gradient sealing to prevent large language models (LLMs) from acquiring forbidden capabilities during downstream fine‑tuning. It shows that existing retrospective unlearning methods fail to block hidden gradient pathways that can later enable illicit knowledge. By pushing relevant pre‑activations into the negative region, gradient sealing effectively blocks these pathways and demonstrates stronger resistance to downstream acquisition across multiple LLM families.
By Kemou Li, Qizhou Wang, Yue Wang, Fengpeng Li, Zhuan Shi, Negar Rostamzadeh, Golnoosh Farnadi, Masashi Sugiyama, Jiantao Zhou
The paper examines the vulnerability of open‑weight large language model safeguards that aim to block harmful usage by assuming new harmful behavior arises only through fine‑tuning. It demonstrates that simple, low‑cost attacks—abliteration and prefilling—can significantly increase attack success rates across multiple benchmarks, and proposes an abliteration‑resistant tuning (ART) method that mitigates these attacks. The study shows that safeguarding defenses must be evaluated against a broader range of attack strategies beyond fine‑tuning.
By Kevin Kuo, Virginia Smith, Chhavi Yadav