3 papers
cs.CR2026
Comparative Insights on Adversarial Machine Learning from Industry and Academia: A User-Study Approach
Vishruti Kakkad, Paul Chung, Hanan Hibshi +1
An exponential growth of Machine Learning and its Generative AI applications brings with it significant security challenges, often referred to as Adversarial Machine Learning (AML)…
cs.SE2026
A Mixed-Methods Study on the Implications of Unsafe Rust for Interoperation, Encapsulation, and Tooling
Ian McCormack, Tomas Dougan, Sam Estep +3
The Rust programming language restricts aliasing to provide static safety guarantees. However, in certain situations, developers need to bypass these guarantees by using a set of u…
cs.CR2025
Evidence of Cognitive Biases in Capture-the-Flag Cybersecurity Competitions
Carolina Carreira, Anu Aggarwal, Alejandro Cuevas +3
Understanding how cognitive biases influence adversarial decision-making is essential for developing effective cyber defenses. Capture-the-Flag (CTF) competitions provide an ecolog…