Walma: Learning to See Memory Corruption in WebAssembly
arXiv SecurityArchived Mar 26, 2026✓ Full text saved
arXiv:2603.24167v1 Announce Type: new 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
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✦ AI Summary· Claude Sonnet
Computer Science > Cryptography and Security
[Submitted on 25 Mar 2026]
Walma: Learning to See Memory Corruption in WebAssembly
Oussama Draissi, Mark Günzel, Ahmad-Reza Sadeghi, Lucas Davi
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.
Comments: 9 pages, 4 figures, 3 tables
Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
ACM classes: D.4.6
Cite as: arXiv:2603.24167 [cs.CR]
(or arXiv:2603.24167v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2603.24167
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Submission history
From: Oussama Draissi [view email]
[v1] Wed, 25 Mar 2026 10:34:29 UTC (162 KB)
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