15 citations · 34 across the 9 of their papers we have counts for
9 papers
International AI Safety Report 2026
Yoshua Bengio, Stephen Clare, Carina Prunkl +89
The International AI Safety Report 2026 synthesises the current scientific evidence on the capabilities, emerging risks, and safety of general-purpose AI systems. The report series…
International AI Safety Report 2025: Second Key Update: Technical Safeguards and Risk Management
Yoshua Bengio, Stephen Clare, Carina Prunkl +66
This second update to the 2025 International AI Safety Report assesses new developments in general-purpose AI risk management over the past year. It examines how researchers, publi…
International AI Safety Report 2025: First Key Update: Capabilities and Risk Implications
Yoshua Bengio, Stephen Clare, Carina Prunkl +70
Since the publication of the first International AI Safety Report, AI capabilities have continued to improve across key domains. New training techniques that teach AI systems to re…
International AI Safety Report
Yoshua Bengio, Sören Mindermann, Daniel Privitera +93
The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by…
Who's Afraid of Adversarial Transferability?
Ziv Katzir, Yuval Elovici
Adversarial transferability, namely the ability of adversarial perturbations to simultaneously fool multiple learning models, has long been the "big bad wolf" of adversarial machin…
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples
Yael Mathov, Eden Levy, Ziv Katzir +2
Recent work on adversarial learning has focused mainly on neural networks and domains where those networks excel, such as computer vision, or audio processing. The data in these do…
Adversarial robustness via stochastic regularization of neural activation sensitivity
Gil Fidel, Ron Bitton, Ziv Katzir +1
Recent works have shown that the input domain of any machine learning classifier is bound to contain adversarial examples. Thus we can no longer hope to immune classifiers against…
Why Blocking Targeted Adversarial Perturbations Impairs the Ability to Learn
Ziv Katzir, Yuval Elovici
Despite their accuracy, neural network-based classifiers are still prone to manipulation through adversarial perturbations. Those perturbations are designed to be misclassified by…
Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces
Ziv Katzir, Yuval Elovici
Neural network based classifiers are still prone to manipulation through adversarial perturbations. State of the art attacks can overcome most of the defense or detection mechanism…