9 citations · 10 across the 8 of their papers we have counts for
9 papers
Through the LENS: Local Geometric Decomposition of Vision-Language Model Representations
Shalom Kachko, Raz Lapid, Margarita Vald +2
Vision-language models (VLMs) process image patches and text tokens in a shared residual stream, but the local geometry through which the two modalities interact remains poorly und…
Breaking Audio Large Language Models by Attacking Only the Encoder: A Universal Targeted Latent-Space Audio Attack
Roee Ziv, Raz Lapid, Moshe Sipper
Audio-language models combine audio encoders with large language models to enable multimodal reasoning, but they also introduce new security vulnerabilities. We propose a universal…
Don't Lag, RAG: Training-Free Adversarial Detection Using RAG
Roie Kazoom, Raz Lapid, Moshe Sipper +1
Adversarial patch attacks pose a major threat to vision systems by embedding localized perturbations that mislead deep models. Traditional defense methods often require retraining…
Pulling Back the Curtain: Unsupervised Adversarial Detection via Contrastive Auxiliary Networks
Eylon Mizrahi, Raz Lapid, Moshe Sipper
Deep learning models are widely employed in safety-critical applications yet remain susceptible to adversarial attacks -- imperceptible perturbations that can significantly degrade…
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective
Tal Alter, Raz Lapid, Moshe Sipper
Kolmogorov-Arnold Networks (KANs) have recently emerged as a novel approach to function approximation, demonstrating remarkable potential in various domains. Despite their theoreti…
Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors
Raz Lapid, Almog Dubin, Moshe Sipper
This paper presents RADAR-Robust Adversarial Detection via Adversarial Retraining-an approach designed to enhance the robustness of adversarial detectors against adaptive attacks,…
XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
Ben Pinhasov, Raz Lapid, Rony Ohayon +2
We introduce a novel methodology for identifying adversarial attacks on deepfake detectors using eXplainable Artificial Intelligence (XAI). In an era characterized by digital advan…
Open Sesame! Universal Black Box Jailbreaking of Large Language Models
Raz Lapid, Ron Langberg, Moshe Sipper
Large language models (LLMs), designed to provide helpful and safe responses, often rely on alignment techniques to align with user intent and social guidelines. Unfortunately, thi…
Patch of Invisibility: Naturalistic Physical Black-Box Adversarial Attacks on Object Detectors
Raz Lapid, Eylon Mizrahi, Moshe Sipper
Adversarial attacks on deep learning models have received increased attention in recent years. Work in this area has mostly focused on gradient-based techniques, so-called 'white-b…