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20222026
most citedAdversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness

10 citations · 20 across the 14 of their papers we have counts for

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5 papers · 1 filter

cs.AI2025

SOM Directions are Better than One: Multi-Directional Refusal Suppression in Language Models

Giorgio Piras, Raffaele Mura, Fabio Brau +3

Refusal refers to the functional behavior enabling safety-aligned language models to reject harmful or unethical prompts. Following the growing scientific interest in mechanistic i…

cs.CL2025

LatentBreak: Jailbreaking Large Language Models through Latent Space Feedback

Raffaele Mura, Giorgio Piras, Kamilė Lukošiūtė +3

Jailbreaks are adversarial attacks designed to bypass the built-in safety mechanisms of large language models. Automated jailbreaks typically optimize an adversarial suffix or adap…

cs.CV2025

S2AP: Score-space Sharpness Minimization for Adversarial Pruning

Giorgio Piras, Qi Zhao, Fabio Brau +3

Adversarial pruning methods have emerged as a powerful tool for compressing neural networks while preserving robustness against adversarial attacks. These methods typically follow…

cs.LG2025

Evaluating Line-level Localization Ability of Learning-based Code Vulnerability Detection Models

Marco Pintore, Giorgio Piras, Angelo Sotgiu +2

To address the extremely concerning problem of software vulnerability, system security is often entrusted to Machine Learning (ML) algorithms. Despite their now established detecti…

cs.LG2025

Regression-aware Continual Learning for Android Malware Detection

Daniele Ghiani, Daniele Angioni, Giorgio Piras +6

Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated. With antivirus vendors processing hundreds of thousands of new samples daily, datasets…