CICI: TCR: Protecting and Hardening Scientific Use Of Software Libraries With GRISL

New York University, New York NY

Investigators

Abstract

Modern scientific computing increasingly depends on high-performance, reusable software components known as libraries, which are often written in low-level languages such as C and C++. While these languages offer speed and flexibility, they are also prone to memory corruption, crashes, and silent data errors. Such failures can compromise critical scientific workflows, leading to inaccurate or unreliable research outcomes and wasted computational resources. GRISL (General-purpose Rigorous Isolation for Science Libraries) strengthens the robustness and security of cyberinfrastructure by isolating and hardening scientific libraries through a lightweight, userspace-based containment approach. GRISL enables scientific researchers, many of whom are not systems experts, to continue using legacy libraries without modifying their code, while gaining protection against memory bugs and data integrity issues. By making these libraries safer by default and compatible with widely used scientific platforms, GRISL improves the reliability of artificial intelligence and machine learning applications, high-performance computing systems, and domain-specific simulations in fields such as physics, biology, and climate science. Technically, GRISL introduces a novel form of protection to wrap and monitor unsafe libraries with minimal performance impact (~1%). Its architecture enforces advanced runtime safety checks that are helpful not only for traditional scientific libraries but also for Large Language Models (like ChatGPT). GRISL also protects different libraries from each other, by providing a safe way to perform inter-library communications. These innovations allow researchers to run hardened versions of popular scientific libraries with minimal overhead, and validate their functional correctness using advanced testing techniques. The project collaborates closely with national testbeds such as CloudLab and Chameleon. By preventing crashes and silent corruption, GRISL improves scientific reproducibility and resilience across computational disciplines, and will be broadly disseminated through open-source releases, educational outreach, and integration with scientific ecosystems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.