Paper
Walma: Learning to See Memory Corruption in WebAssembly
Authors
Oussama Draissi, Mark Günzel, Ahmad-Reza Sadeghi, Lucas Davi
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.24167v1</id>\n <title>Walma: Learning to See Memory Corruption in WebAssembly</title>\n <updated>2026-03-25T10:34:29Z</updated>\n <link href='https://arxiv.org/abs/2603.24167v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.24167v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CR'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <published>2026-03-25T10:34:29Z</published>\n <arxiv:comment>9 pages, 4 figures, 3 tables</arxiv:comment>\n <arxiv:primary_category term='cs.CR'/>\n <author>\n <name>Oussama Draissi</name>\n </author>\n <author>\n <name>Mark Günzel</name>\n </author>\n <author>\n <name>Ahmad-Reza Sadeghi</name>\n </author>\n <author>\n <name>Lucas Davi</name>\n </author>\n </entry>"
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