Search Results for author:
Found 9 papers, 8 papers with code
Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks
We introduce a new family of prompt injection attacks, termed Neural Exec.
Can Decentralized Learning be more robust than Federated Learning?
Decentralized Learning (DL) is a peer--to--peer learning approach that allows a group of users to jointly train a machine learning model.
Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data
Specifically, the model uses deep learning to capture the correlation between the auxiliary data of a group of users (e. g., users of a web application) and their passwords.
On the (In)security of Peer-to-Peer Decentralized Machine Learning
In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning -- a collaborative machine learning framework aimed at addressing the main limitations of federated learning.
Eluding Secure Aggregation in Federated Learning via Model Inconsistency
Indeed, the use of secure aggregation prevents the server from learning the value and the source of the individual model updates provided by the users, hampering inference and data attribution attacks.
Unleashing the Tiger: Inference Attacks on Split Learning
We investigate the security of Split Learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption.
Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries
Password security hinges on an in-depth understanding of the techniques adopted by attackers.
Interpretable Probabilistic Password Strength Meters via Deep Learning
Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength.
Adversarial Out-domain Examples for Generative Models
Deep generative models are rapidly becoming a common tool for researchers and developers.
