arXiv:2606. 19023v1 Announce Type: cross Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces.
By Gabriele Digregorio, Marco Di Gennaro, Francesco Pastore, Stefano Zanero, Stefano Longari, Michele Carminati
arXiv:2610.40735v1 Announce Type: cross
Abstract: Evaluating approaches that improve the security of LLM code generation requires datasets that jointly assess security and functionality and remain ad...
By Zacharie Chenail-Larcher, Daniele Lunghi, Melissa Tessa, Quentin Sti\'evenart, Jacques Klein, Tegawend\'e F. Bissyand\'e, Florent Avellaneda, Naouel Moha
The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses.
arXiv:2609.21303v1 Announce Type: cross
Abstract: Product abuse is an individually rare, but growing, problem across the SaaS industry. Highly sophisticated threat actors can misuse security platform...
By Shaefer Drew, Michael Brautbar, Paul Knight, Edward Raff, Lana Peric-McDermott, Simran Sarin, Nickolas Machado, Hanna Albright, Vitaly Zaytsev
arXiv:2607. 15970v1 Announce Type: cross Abstract: The flourishing code hosting platforms and coding agents enable even beginners with private data to build tailored Machine Learning (ML) models using available code quickly.
By Xukun Luan, Yuhui Gong, Gang Zhang, Zixuan Huang, Yuanguo Bi, Xuesong Li, Jinyan Liu
arXiv:2503. 23536v3 Announce Type: replace-cross Abstract: Unlearnable data (ULD) has emerged as an innovative defense technique to prevent machine learning models from learning meaningful patterns from specific data, thus protecting data privacy and security.
By Jiahao Li, Yiqiang Chen, Yunbing Xing, Yang Gu, Xiangyuan Lan