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Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistency
Yael Itzhakev, Amit Giloni, Yuval Elovici +1
Machine learning models trained on tabular data are vulnerable to adversarial attacks, even in realistic scenarios where attackers only have access to the model's outputs. Since ta…
DIESEL -- Dynamic Inference-Guidance via Evasion of Semantic Embeddings in LLMs
Ben Ganon, Alon Zolfi, Omer Hofman +4
In recent years, large language models (LLMs) have had great success in tasks such as casual conversation, contributing to significant advancements in domains like virtual assistan…
Detection of Compromised Functions in a Serverless Cloud Environment
Danielle Lavi, Oleg Brodt, Dudu Mimran +2
Serverless computing is an emerging cloud paradigm with serverless functions at its core. While serverless environments enable software developers to focus on developing applicatio…
DOMBA: Double Model Balancing for Access-Controlled Language Models via Minimum-Bounded Aggregation
Tom Segal, Asaf Shabtai, Yuval Elovici
The utility of large language models (LLMs) depends heavily on the quality and quantity of their training data. Many organizations possess large data corpora that could be leverage…
LLMCloudHunter: Harnessing LLMs for Automated Extraction of Detection Rules from Cloud-Based CTI
Yuval Schwartz, Lavi Benshimol, Dudu Mimran +2
As the number and sophistication of cyber attacks have increased, threat hunting has become a critical aspect of active security, enabling proactive detection and mitigation of thr…
Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models
Nitzan Bitton-Guetta, Aviv Slobodkin, Aviya Maimon +6
Imagine observing someone scratching their arm; to understand why, additional context would be necessary. However, spotting a mosquito nearby would immediately offer a likely expla…