127 citations · 308 across the 18 of their papers we have counts for
18 papers
Don't Trust Stubborn Neighbors: A Security Framework for Agentic Networks
Samira Abedini, Sina Mavali, Lea Schönherr +2
Large Language Model (LLM)-based Multi-Agent Systems (MASs) are increasingly deployed for agentic tasks, such as web automation, itinerary planning, and collaborative problem solvi…
Are Modern Speech Enhancement Systems Vulnerable to Adversarial Attacks?
Rostislav Makarov, Lea Schönherr, Timo Gerkmann
Machine learning approaches for speech enhancement are becoming increasingly expressive, enabling ever more powerful modifications of input signals. In this paper, we demonstrate t…
Trust Me, I Know This Function: Hijacking LLM Static Analysis using Bias
Shir Bernstein, David Beste, Daniel Ayzenshteyn +2
Large Language Models (LLMs) are increasingly trusted to perform automated code review and static analysis at scale, supporting tasks such as vulnerability detection, summarization…
Buffer-free Class-Incremental Learning with Out-of-Distribution Detection
Srishti Gupta, Daniele Angioni, Maura Pintor +4
Class-incremental learning (CIL) poses significant challenges in open-world scenarios, where models must not only learn new classes over time without forgetting previous ones but a…
Rethinking Robustness in Machine Learning: A Posterior Agreement Approach
João Borges S. Carvalho, Victor Jimenez Rodriguez, Alessandro Torcinovich +4
The robustness of algorithms against covariate shifts is a fundamental problem with critical implications for the deployment of machine learning algorithms in the real world. Curre…
Adversarial Robustness of AI-Generated Image Detectors in the Real World
Sina Mavali, Jonas Ricker, David Pape +2
The rapid advancement of Generative Artificial Intelligence (GenAI) capabilities is accompanied by a concerning rise in its misuse. In particular the generation of credible misinfo…