← 研踪 PaperTrailarXiv:2603.24167 · v3
Walma: Learning to See Memory Corruption in WebAssembly
首次提交 2026-03-25 18:34
版本更新 2026-11-03 01:09
雷达观测 2026-11-04 23:41
摘要
The monolithic linear memory of WebAssembly (Wasm) creates a bidirectional attack surface: a memory corruption bug in a module can compromise the host, and a compromised host can corrupt the state of a trusted module. Existing defenses either require custom runtimes or source code changes, and none can verify runtime integrity under an adversarial host. We present WALMA, the first attestation framework delivering resilience by reaction against data-centric attacks that corrupt a region of memory. We render linear memory snapshots as grayscale images and classify them with a convolutional neural network. The network reads the row-aligned structure that compiled code imposes on memory and detects corruption that byte and texture statistics miss. Our main VGG-16 detector reaches 95%--100% verdict accuracy on four of five CVE-affected applications without a single false alarm, and a 1.9M-parameter ResNet, small enough to run inside the runtime on every host call, stays within a few points of it on the same four. A Shannon-entropy analysis bounds which out-of-band edits the network can separate from valid state, and black-box evasion requires contiguous overwrites of hundreds of kilobytes to megabytes. On the 53 WABench binaries, boundary attestation costs 1.07x--1.69x, scaling with a module's exposure to the host rather than with its runtime. WALMA thus makes linear memory integrity a property a Wasm runtime can check continuously: end to end against compromised modules, and against untrusted hosts provided snapshots reach the verifier intact.
作者备注：12 pages, 5 figures, 3 tables
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v3 · 2026-11-03 01:09 · 最新观测版本
Walma: Learning to See Memory Corruption in WebAssembly
The monolithic linear memory of WebAssembly (Wasm) creates a bidirectional attack surface: a memory corruption bug in a module can compromise the host, and a compromised host can corrupt the state of a trusted module. Existing defenses either require custom runtimes or source code changes, and none can verify runtime integrity under an adversarial host. We present WALMA, the first attestation framework delivering resilience by reaction against data-centric attacks that corrupt a region of memory. We render linear memory snapshots as grayscale images and classify them with a convolutional neural network. The network reads the row-aligned structure that compiled code imposes on memory and detects corruption that byte and texture statistics miss. Our main VGG-16 detector reaches 95%--100% verdict accuracy on four of five CVE-affected applications without a single false alarm, and a 1.9M-parameter ResNet, small enough to run inside the runtime on every host call, stays within a few points of it on the same four. A Shannon-entropy analysis bounds which out-of-band edits the network can separate from valid state, and black-box evasion requires contiguous overwrites of hundreds of kilobytes to megabytes. On the 53 WABench binaries, boundary attestation costs 1.07x--1.69x, scaling with a module's exposure to the host rather than with its runtime. WALMA thus makes linear memory integrity a property a Wasm runtime can check continuously: end to end against compromised modules, and against untrusted hosts provided snapshots reach the verifier intact.
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