Adversarial Out-domain Examples for Generative Models. - Dario Pasquini; Marco Mingione; Massimo Bernaschi (EuroS&P Workshops, 2019)

Out-domain examples for generative models. - Dario Pasquini; Marco Mingione; Massimo Bernaschi (CoRR, 2019)

Improving Password Guessing via Representation Learning. - Dario Pasquini; Ankit Gangwal; Giuseppe Ateniese; Massimo Bernaschi; Mauro Conti (IACR Cryptol. ePrint Arch., 2019)

Interpretable Probabilistic Password Strength Meters via Deep Learning. - Dario Pasquini; Giuseppe Ateniese; Massimo Bernaschi (ESORICS (1), 2020)

AMG based on compatible weighted matching for GPUs. - Massimo Bernaschi; Pasqua D'Ambra; Dario Pasquini (Parallel Comput., 2020)

BootCMatchG: An adaptive Algebraic MultiGrid linear solver for GPUs. - Massimo Bernaschi; Pasqua D'Ambra; Dario Pasquini (Softw. Impacts, 2020)

Unleashing the Tiger: Inference Attacks on Split Learning. - Dario Pasquini; Giuseppe Ateniese; Massimo Bernaschi (CCS, 2021)

Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries. - Dario Pasquini; Marco Cianfriglia; Giuseppe Ateniese; Massimo Bernaschi (USENIX Security Symposium, 2021)

Enabling secure passwords via Deep Learning: Towards a new generation of attacks and defenses. - Dario Pasquini (2021)

Eluding Secure Aggregation in Federated Learning via Model Inconsistency. - Dario Pasquini; Danilo Francati; Giuseppe Ateniese (CCS, 2022)

On the Privacy of Decentralized Machine Learning. - Dario Pasquini; Mathilde Raynal; Carmela Troncoso (CoRR, 2022)

On the (In)security of Peer-to-Peer Decentralized Machine Learning. - Dario Pasquini; Mathilde Raynal; Carmela Troncoso (SP, 2023)

Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning. - Etienne Salimbeni; Nina Mainusch; Dario Pasquini (SP (Workshops), 2023)

Can Decentralized Learning be more robust than Federated Learning? - Mathilde Raynal; Dario Pasquini; Carmela Troncoso (CoRR, 2023)

Breach Extraction Attacks: Exposing and Addressing the Leakage in Second Generation Compromised Credential Checking Services. - Dario Pasquini; Danilo Francati; Giuseppe Ateniese; Evgenios M. Kornaropoulos (IACR Cryptol. ePrint Arch., 2023)

Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks. - Dario Pasquini; Martin Strohmeier; Carmela Troncoso (AISec@CCS, 2024)

Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data. - Dario Pasquini; Giuseppe Ateniese; Carmela Troncoso (SP, 2024)

LLMmap: Fingerprinting For Large Language Models. - Dario Pasquini; Evgenios M. Kornaropoulos; Giuseppe Ateniese (CoRR, 2024)

Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks. - Dario Pasquini; Evgenios M. Kornaropoulos; Giuseppe Ateniese (CoRR, 2024)

When AIOps Become "AI Oops": Subverting LLM-driven IT Operations via Telemetry Manipulation. - Dario Pasquini; Evgenios M. Kornaropoulos; Giuseppe Ateniese; Omer Akgul; Athanasios Theocharis; Petros Efstathopoulos (CoRR, 2025)