Walma: Learning to See Memory Corruption in WebAssembly

TL;DR

Walma introduces a machine learning-based framework for detecting memory corruption in WebAssembly, providing a practical, low-overhead method to verify runtime integrity against malicious tampering.

Contribution

The paper presents Walma, a novel ML-based approach for WebAssembly memory attestation that improves detection of memory corruption with configurable overheads.

Findings

CNN-based classification effectively detects memory corruption.

Coarse-grained boundary checks have low overhead (~1.07x).

Fine-grained monitoring incurs higher overhead (1.5x–1.8x).

Abstract

WebAssembly's (Wasm) monolithic linear memory model facilitates memory corruption attacks that can escalate to cross-site scripting in browsers or go undetected when a malicious host tampers with a module's state. Existing defenses rely on invasive binary instrumentation or custom runtimes, and do not address runtime integrity verification under an adversarial host model. We present Walma, a framework for WebAssembly Linear Memory Attestation that leverages machine learning to detect memory corruption and external tampering by classifying memory snapshots. We evaluate Walma on six real-world CVE-affected applications across three verification backends (cpu-wasm, cpu-tch, gpu) and three instrumentation policies. Our results demonstrate that CNN-based classification can effectively detect memory corruption in applications with structured memory layouts, with coarse-grained boundary checks incurring as low as 1.07x overhead, while fine-grained monitoring introduces higher (1.5x--1.8x) but predictable costs. Our evaluation quantifies the accuracy and overhead trade-offs across deployment configurations, demonstrating the practical feasibility of ML-based memory attestation for WebAssembly.