In recent work, we developed an educational module for undergraduate computing students, to bring inclusivity and engagement into the advanced cybersecurity education topic of stack smashing attacks and defenses. Our module comprises four guided learning activities, and an active learning exercise that integrates a stack smashing attack visualization tool, PVTool, developed in prior work. The module was deployed in an undergraduate cybersecurity course across multiple semesters, showing favorable results. In the current work, we outline a plan to evaluate the effectiveness of the module for an underrepresented, underserved community in computing—neurodivergent students, specifically, students with Autism Spectrum Disorder (ASD) or Attention Deficit Hyperactivity Disorder (ADHD). We plan to deploy our module to participants recruited from the AccessComputing group, a cohort of about 600 computing students and recent graduates across the US who have disabilities such as ADHD and ASD. We will evaluate the effectiveness of our stack smashing module in terms of learning, engagement, and accessibility for neurodivergent students through multi-pronged data collection and a mixed-methods analysis. We will use insights from the study to refine our module and to establish guidelines for future course modules and activities, to better serve neurodivergent students while continuing to serve all computing students.