5 papers
Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks
Dario Pasquini, Martin Strohmeier, Carmela Troncoso
We introduce a new family of prompt injection attacks, termed Neural Exec. Unlike known attacks that rely on handcrafted strings (e.g., "Ignore previous instructions and..."), we s…
Unleashing the Tiger: Inference Attacks on Split Learning
Dario Pasquini, Giuseppe Ateniese, Massimo Bernaschi
We investigate the security of Split Learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption. In the p…
Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries
Dario Pasquini, Marco Cianfriglia, Giuseppe Ateniese +1
Password security hinges on an in-depth understanding of the techniques adopted by attackers. Unfortunately, real-world adversaries resort to pragmatic guessing strategies such as…
Improving Password Guessing via Representation Learning
Dario Pasquini, Ankit Gangwal, Giuseppe Ateniese +2
Learning useful representations from unstructured data is one of the core challenges, as well as a driving force, of modern data-driven approaches. Deep learning has demonstrated t…
Adversarial Out-domain Examples for Generative Models
Dario Pasquini, Marco Mingione, Massimo Bernaschi
Deep generative models are rapidly becoming a common tool for researchers and developers. However, as exhaustively shown for the family of discriminative models, the test-time infe…