Resilient and Sustainable Software Security

Introduction

In parallel with the ongoing digitization, computer security has become an increasingly important and urgent challenge. In particular, the sound and robust implementation of complex software systems is still not well understood in practice, as evidenced by the steady stream of successful attacks observed in the wild. The current state of the art in software security consists of solutions that are often technically sound, but do not provide operational security in practice.

Project Overview

With the Resilient and Sustainable Software Security (RS³) project, we propose a compelling research agenda to fundamentally change this situation by developing novel countermeasures at different system levels that fundamentally improve security.

Key Objectives

On the one hand, the system must be "resilient" against entire classes of attack vectors. On the other hand, the system must be "sustainable", i.e., it must be able to maintain its security at least over its design lifetime and possibly even adapt over time.

Work Plan

Our work plan addresses the problem from four different angles by:

1. Developing novel software testing strategies that enable accurate and efficient vulnerability discovery.
2. Designing secure compiler chains that embed security properties during the compilation phase that can be enforced at runtime.
3. Devising robust mechanisms that mitigate and patch advanced attacks.
4. Investigating how hardware changes for open-source hardware (e.g., RISC-V processors) can improve the efficiency and accuracy of all of these goals.

Expected Outcomes

We expect to develop innovative methods and fundamental principles to build, test, and patch complex systems securely and efficiently. This holistic approach covers multiple layers of the computing stack, and each aspect has the potential to improve security significantly.

Success Criteria

The main success criterion will be our ability to perform a security analysis of a complex system an order of magnitude more effectively and efficiently than with current state-of-the-art methods.