Abstract
Defense techniques such as Data Execution Prevention (DEP) and Address Space Layout Randomization (ASLR) were role models in preventing early return-oriented programming (ROP) attacks by keeping performance and scalability in the forefront, making them widely-adopted. As code reuse attacks evolved in complexity, defenses have lost touch with pragmatic defense design to ensure security, either being narrow in scope or providing unrealistic overheads. We present MARDU, an on-demand system-wide re-randomization technique that maintains strong security guarantees while providing better overall performance and having scalability most defenses lack. We achieve code sharing with diversification by implementing reactive and scalable, rather than continuous or one-time diversification. Enabling code sharing further minimizes needed tracking, patching, and memory overheads. The evaluation of MARDU shows low performance overhead of 5.5% on SPEC and minimal degradation of 4.4% in NGINX, proving its applicability to both compute-intensive and scalable real-world applications.
Yeongjin Jang
Principal Software Engineer
My research interests include cybersecurity/hacking, automated vulnerability discovery/analysis, secure system design, and applied cryptography.