JANUS: A Simple and Efficient Speculative Defense using Reinforcement Learning

• Pavlos Aimoniotis Uppsala University
• Stefanos Kaxiras Uppsala University

Resumo

Speculative execution and the emergence of Spectre attacks have forced architects to rethink how microprocessors are designed. Several approaches aim to close this security vulnerability while trying to minimize performance degradation, often involving complex and sophisticated mechanisms. These strategies typically entail substantial modifications to the processor core and memory hierarchy, which ultimately inhibit their adoption in real designs.In this work, we leverage two of the simplest speculative defense ideas, NDA and DoM, that can co-exist in the same core, and we apply a simple form of Reinforcement Learning (RL) to select the most effective mechanism, as the underlying processor defense, for a window of execution. NDA forbids the propagation of a potential secret to subsequent instructions while DoM prohibits the creation of observable timing differences in the cache. We observe that their impact on different applications can vary significantly, but, often, they can complement each other within the same application. However, our investigation also reveals vulnerabilities in previous proposals that try to combine these secure speculation schemes into one. We demonstrate an attack scenario that violates the security of the combined scheme and we present the conditions that must hold to safely combine them. Lastly, while the cost and complexity of reinforcement learning may seem inordinately high for microarchitectural implementations, we build on recent research that demonstrates remarkably lightweight solutions, provided that the action space is small.We present JANUS, a lightweight architecture leveraging an RL agent based on a two-armed bandit algorithm. JANUS selects the optimal, performance-wise, defense mechanism that protects the processor within a specific time window. We evaluate JANUS on SPEC2017 benchmark suite and find that it outperforms NDA by +4.9%, STT (a more sophisticated and complex scheme that uses taint tracking) by +1%, and DoM by +2.6%. Further, when a state-of-the-art address-prediction optimization (Doppelganger Loads) is employed on top of the baseline defenses, NDA and DoM, JANUS still outperforms the former by +2.3%, and the latter by +0.3%. When evaluated with the older SPEC2006 benchmark suite, JANUS outperforms all schemes by +4.7% on average, with a maximum of +8.2% over DoM. JANUS achieves these results with a meager storage overhead of just 16 bytes and a complexity-effective design.

Palavras-chave: Threat modeling, Reinforcement learning, Computer architecture, Side-channel attacks, Benchmark testing, Timing, Security, Proposals, Protection, Optimization, speculative side channels, spectre, reinforcement learning