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DeepKeep Research

DeepKeep’s research focuses on understanding how modern AI systems fail in real world environments, and how to secure them before those failures turn into risk. Our research explores large language models, computer vision and multimodal AI, with hands on analysis of emerging attack techniques and practical defense strategies. Together, they reflect an approach to AI security that values innovation, transparency, and deployable protection over theory alone.

Academic research

Title

Authors

Publication

large language models

Activation Steering for Masked Diffusion Language Models

Adi Shnaidman, Erin Feiglin, Osher Yaari, Efrat Mentel, Amit Levi, Raz Lapid

ReALM-GEN @ ICLR 2026

BenchOverflow: Measuring Overflow in Large Language Models via Plain-Text Prompts

Erin Feiglin, Nir Hutnik, Raz Lapid

TMLR 2026

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations

Amit Levi, Raz Lapid, Rom Himelstein, Yaniv Nemcovsky, Ravid Shwartz Ziv, Avi Mendelson

arXiv

Backdoors in Conditional Diffusion: Threats to Responsible Synthetic Data Pipelines

Raz Lapid, Almog Dubin

RDS @ AAAI 2026

Don't Lag, RAG: Training-Free Adversarial Detection Using RAG

Roie Kazoom, Raz Lapid, Moshe Sipper, Ofer Hadar

VLM4RWD @ NeurIPS 2025

Pulling Back the Curtain: Unsupervised Adversarial Detection via Contrastive Auxiliary Networks

Eylon Mizrahi, Raz Lapid, Moshe Sipper

SaFeMM-AI @ ICCV 2025 (Spotlight)

On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective

Tal Alter, Raz Lapid, Moshe Sipper

TMLR 2025

Open Sesame! Universal Black Box Jailbreaking of Large Language Models

Raz Lapid, Ron Langberg, Moshe Sipper

SeT LLM @ ICLR 2024

Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors

Raz Lapid, Almog Dubin, Moshe Sipper

Mathematics @ MDPI 2024

XAI-Based Detection of Adversarial Attacks on Deepfake Detectors

Ben Pinhasov, Raz Lapid, Rony Ohayon, Moshe Sipper, Yehudit Aperstein

TMLR 2024

Computer Vision

Patch of Invisibility: Naturalistic Physical Black-Box Adversarial Attacks on Object Detectors

Raz Lapid, Eylon Mizrahi, Moshe Sipper

MLCS @ ECML-PKDD 2024

I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models

Raz Lapid, Moshe Sipper

MLCS @ ECML-PKDD 2023

Foiling Explanations in Deep Neural Networks

Snir Vitrack Tamam, Raz Lapid, Moshe Sipper

TMLR 2023

An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks

Raz Lapid, Zvika Haramaty, Moshe Sipper

Algorithms @ MDPI 2022

Audio

Breaking Audio Large Language Models by Attacking Only the Encoder: A Universal Targeted Latent-Space Audio Attack

Roee Ziv, Raz Lapid, Moshe Sipper

arXiv