Dynamic Robustness Verification Against Weak Memory (Extended Version)

2025-04-21

AI Summary

Dynamic race detection under weak memory models—particularly C11—suffers from instrumentation-induced synchronization, which masks genuine weak-consistency races and invalidates robustness verification. Method: This paper proposes the first dynamic robustness verification method for the C11 memory model. Its core innovation is a lightweight, location-clock–based dynamic algorithm that enables program-specific defense while preserving the original memory ordering; it enforces strong-consistency behavior constraints without perturbing the underlying weak semantics. We implement this approach in RSAN, a tool integrating C11 semantic modeling, dynamic instrumentation, runtime monitoring, and static heuristic adaptation. Contribution/Results: Experiments demonstrate that RSAN effectively identifies non-robust program behaviors across diverse scenarios, validating the feasibility and practicality of dynamic robustness checking. To our knowledge, this work fills a critical gap in the literature on dynamic robustness verification for C11.

Technology Category

• Natural Language Processing: Safety and Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: Other Foundations and Applications

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Dynamic race detection is a highly effective runtime verification technique for identifying data races by instrumenting and monitoring concurrent program runs. However, standard dynamic race detection is incompatible with practical weak memory models; the added instrumentation introduces extra synchronization, which masks weakly consistent behaviors and inherently misses certain data races. In response, we propose to dynamically verify program robustness-a property ensuring that a program exhibits only strongly consistent behaviors. Building on an existing static decision procedures, we develop an algorithm for dynamic robustness verification under a C11-style memory model. The algorithm is based on"location clocks", a variant of vector clocks used in standard race detection. It allows effective and easy-to-apply defense against weak memory on a per-program basis, which can be combined with race detection that assumes strong consistency. We implement our algorithm in a tool, called RSAN, and evaluate it across various settings. To our knowledge, this work is the first to propose and develop dynamic verification of robustness against weak memory models.

Problem

• Dynamic verification of program robustness under weak memory models
• Addressing incompatibility of standard race detection with weak memory
• Developing location clocks for effective weak memory defense

Innovation

• Dynamic robustness verification for weak memory
• Algorithm based on location clocks variant
• Combines race detection with strong consistency