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cs.LG2024
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…
cs.CR2024★ 2 cited
Insights and Current Gaps in Open-Source LLM Vulnerability Scanners: A Comparative Analysis
Jonathan Brokman, Omer Hofman, Oren Rachmil +6
This report presents a comparative analysis of open-source vulnerability scanners for conversational large language models (LLMs). As LLMs become integral to various applications,…