Towards Reinforcement Learning for Exploration of Speculative Execution Vulnerabilities
This addresses the challenge of efficiently finding security vulnerabilities in processors for hardware designers and security researchers, though it appears incremental as it applies RL to an existing problem.
The paper tackles the problem of manually discovering speculative execution vulnerabilities like Spectre by introducing SpecRL, a reinforcement learning framework that automates the search for leaks in black-box microprocessors, achieving automated detection without requiring hardware knowledge.
Speculative attacks such as Spectre can leak secret information without being discovered by the operating system. Speculative execution vulnerabilities are finicky and deep in the sense that to exploit them, it requires intensive manual labor and intimate knowledge of the hardware. In this paper, we introduce SpecRL, a framework that utilizes reinforcement learning to find speculative execution leaks in post-silicon (black box) microprocessors.