Architectural Mimicry: Innovative Instructions to Efficiently Address Control-Flow Leakage in Data-Oblivious Programs

The control flow of a program can often be observed through side-channel attacks. Hence, when control flow depends on secrets, attackers can learn information about these secrets. Widely used software-based countermeasures ensure that attacker-observable aspects of the control flow do not depend on secrets, relying on techniques like dummy execution (for balancing code) or conditional execution (for linearizing code). In the current state-of-practice, the primitives to implement these techniques have to be found in an existing instruction set architecture (ISA) that was not designed a priori to provide them, leading to performance, security, and portability issues. To counter these issues, this paper proposes lightweight hardware extensions for supporting these techniques in a principled way. We propose (1) a novel hardware mechanism (mimic execution), that executes an instruction stream only for its attacker-observable effects, and suppresses (most) architectural effects, and (2) ISA support (called AMi, for Architectural Mimicry) and programming models to effectively use mimic execution to balance or linearize code. We show the feasibility and benefits of our proposal by implementing mimic execution and AMi for a 32-bit out-of-order RISC-V core that leaks control flow in multiple ways (via e.g., the branch predictor, instruction timings, and the data cache). Our experimental evaluation shows that the hardware cost is low (most importantly, no impact on the processor′s critical path), and that AMi enables significant performance improvements. In particular, AMi reduces the overhead of state-of-the-art linearized code by 60% in our benchmarks.