[2603.21975] SecureBreak -- A dataset towards safe and secure models

Computer Science > Cryptography and Security arXiv:2603.21975 (cs) [Submitted on 23 Mar 2026] Title:SecureBreak -- A dataset towards safe and secure models Authors:Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera View a PDF of the paper titled SecureBreak -- A dataset towards safe and secure models, by Marco Arazzi and 2 other authors View PDF HTML (experimental) Abstract:Large language models are becoming pervasive core components in many real-world applications. As a consequence, security alignment represents a critical requirement for their safe deployment. Although previous related works focused primarily on model architectures and alignment methodologies, these approaches alone cannot ensure the complete elimination of harmful generations. This concern is reinforced by the growing body of scientific literature showing that attacks, such as jailbreaking and prompt injection, can bypass existing security alignment mechanisms. As a consequence, additional security strategies are needed both to provide qualitative feedback on the robustness of the obtained security alignment at the training stage, and to create an ``ultimate'' defense layer to block unsafe outputs possibly produced by deployed models. To provide a contribution in this scenario, this paper introduces SecureBreak, a safety-oriented dataset designed to support the development of AI-driven solutions for detecting harmful LLM outputs caused by residual weaknesses in security alignment. The dataset is highly ...