Samuel Mergendahl (MIT Lincoln Laboratory), Nathan Burow (MIT Lincoln Laboratory), Hamed Okhravi (MIT Lincoln Laboratory)

Memory corruption attacks against unsafe programming languages like C/C++ have been a major threat to computer systems for multiple decades. Various sanitizers and runtime exploit mitigation techniques have been shown to only provide partial protection at best. Recently developed ‘safe’ programming languages such as Rust and Go hold the promise to change this paradigm by preventing memory corruption bugs using a strong type system and proper compile-time and runtime checks. Gradual deployment of these languages has been touted as a way of improving the security of existing applications before entire applications can be developed in safe languages. This is notable in popular applications such as Firefox and Tor. In this paper, we systematically analyze the security of multi-language applications. We show that because language safety checks in safe languages and exploit mitigation techniques applied to unsafe languages (e.g., Control-Flow Integrity) break different stages of an exploit to prevent control hijacking attacks, an attacker can carefully maneuver between the languages to mount a successful attack. In essence, we illustrate that the incompatible set of assumptions made in various languages enables attacks that are not possible in each language alone. We study different variants of these attacks and analyze Firefox to illustrate the feasibility and extent of this problem. Our findings show that gradual deployment of safe programming languages, if not done with extreme care, can indeed be detrimental to security.

View More Papers

Get a Model! Model Hijacking Attack Against Machine Learning...

Ahmed Salem (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security), Yang Zhang (CISPA Helmholtz Center for Information Security)

Read More

DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

Read More

Demo #15: Remote Adversarial Attack on Automated Lane Centering

Yulong Cao (University of Michigan), Yanan Guo (University of Pittsburgh), Takami Sato (UC Irvine), Qi Alfred Chen (UC Irvine), Z. Morley Mao (University of Michigan) and Yueqiang Cheng (NIO)

Read More

MIRROR: Model Inversion for Deep LearningNetwork with High Fidelity

Shengwei An (Purdue University), Guanhong Tao (Purdue University), Qiuling Xu (Purdue University), Yingqi Liu (Purdue University), Guangyu Shen (Purdue University); Yuan Yao (Nanjing University), Jingwei Xu (Nanjing University), Xiangyu Zhang (Purdue University)

Read More