AI

Strato: A Retargetable Framework for Low-level Inlined Reference Monitors

Abstract

Low-level Inlined Reference Monitors (IRM) such as control-flow integrity and software-based fault isolation can foil numerous software attacks. Conventionally, those IRMs are implemented through binary rewriting or transformation on equivalent low-level programs that are tightly coupled with a specific Instruction Set Architecture (ISA). Resulting implementations have poor retargetability to different ISAs. This paper introduces an IRM-implementation framework at a compiler intermediate-representation (IR) level. The IR-level framework enables easy retargetability to different ISAs, but raises the challenge of how to preserve security at the low level, as the compiler backend might invalidate the assumptions at the IR level. We propose a constraint language to encode the assumptions and check whether they still hold after the backend transformations and optimizations. Furthermore, an independent verifier is implemented to validate the security of low-level code. We have implemented the framework inside LLVM to enforce the policy of control-flow integrity and data sandboxing for both reads and writes. Experimental results demonstrate that it incurs modest runtime overhead of 19.90% and 25.34% on SPECint2000 programs for ×86- 32 and ×86-64, respectively.