CVE-2026-34942
Rust vulnerability analysis and mitigation

Overview

CVE-2026-34942 is a Denial of Service vulnerability in Bytecode Alliance's Wasmtime WebAssembly runtime, titled "Panic when transcoding misaligned component model UTF-16 strings." The flaw exists in Wasmtime's Component Model string transcoding implementation for UTF-16 and latin1+utf16 encodings, where improper alignment verification of reallocated strings allows malicious WebAssembly guests to trigger host panics. Affected versions include all releases prior to 24.0.7, versions 25.0.0–36.0.6, versions 37.0.0–42.0.1, and version 43.0.0. The vulnerability was disclosed on April 9, 2026, with patched versions released the same day. It carries a CVSS v3.1 score of 6.5 (Medium) and a CVSS v4.0 score of 5.9 (Medium) (GitHub Advisory, Bytecode Alliance Advisory).

Technical details

The root cause is improper validation of memory alignment when Wasmtime's Component Model transcodes strings into UTF-16 or latin1+utf16 encodings (CWE-129: Improper Validation of Array Index; CWE-119: Improper Restriction of Operations within the Bounds of a Memory Buffer; CWE-823: Use of Out-of-range Pointer Offset). When a string is reallocated during transcoding, the runtime fails to correctly verify that the resulting pointer is properly aligned for the target encoding. A malicious WebAssembly guest can craft strings with specific content and memory addresses such that, when transferred across component boundaries, an unaligned pointer is passed to the host transcoding routine, triggering a Rust panic. Exploitation requires the attacker to have low-privilege access to submit WebAssembly code for execution and some degree of control over memory layout, making it a guest-controlled DoS condition (GitHub Advisory, Red Hat Bugzilla).

Impact

Successful exploitation causes the Wasmtime host process to panic (crash), resulting in a Denial of Service for all workloads running within that host instance. There is no impact to confidentiality or integrity — the vulnerability is purely an availability issue. Because the panic condition is fully controlled by the guest WebAssembly module, any platform or service that executes untrusted WebAssembly code using a vulnerable Wasmtime version is at risk of being crashed by a malicious guest (GitHub Advisory, Bytecode Alliance Advisory).

Exploitability

There is no public proof-of-concept exploit and no evidence of in-the-wild exploitation at this time (Feedly). The EPSS score is approximately 0.018% (5th percentile), indicating a very low probability of exploitation in the near term. The vulnerability is not listed in the CISA Known Exploited Vulnerabilities (KEV) catalog. No threat actor attribution has been reported. Detection coverage exists via Nessus (plugin 305818) and Qualys scanners (Feedly).

Exploitation steps

  1. Identify a target: Locate a service or platform that executes untrusted WebAssembly code using a vulnerable version of Wasmtime (prior to 24.0.7, 36.0.7, 42.0.2, or 43.0.1) with the Component Model enabled.
  2. Craft a malicious WebAssembly component: Author a WebAssembly component that transfers strings across component boundaries using UTF-16 or latin1+utf16 encodings. The strings must be crafted with specific content and positioned at memory addresses that, after reallocation, result in an unaligned pointer.
  3. Submit the component for execution: Upload or submit the malicious WebAssembly module to the target host through whatever interface is available (e.g., a cloud function platform, edge runtime, or plugin system).
  4. Trigger the panic: When the host processes the string transcoding operation, the unaligned pointer is passed to the host transcoding routine, triggering a Rust panic and crashing the Wasmtime host process, causing a Denial of Service (GitHub Advisory).

Indicators of compromise

  • Logs: Rust panic messages in Wasmtime host process logs referencing alignment errors or panics during string transcoding operations (e.g., messages containing misaligned pointer, alignment, or transcod in stack traces).
  • Process: Unexpected termination or crash of the Wasmtime host process, particularly when processing WebAssembly components that use UTF-16 or latin1+utf16 string encodings across component boundaries.
  • Application: Repeated or patterned crashes of services built on Wasmtime when handling specific WebAssembly inputs, especially from untrusted or external sources.

Mitigation and workarounds

Bytecode Alliance has released patched versions to address this vulnerability: 24.0.7, 36.0.7, 42.0.2, and 43.0.1. Users should upgrade based on their current deployment branch — to 24.0.7 if on versions below 24.0.7, to 36.0.7 if on versions 25.0.0–36.0.6, to 42.0.2 if on versions 37.0.0–42.0.1, or to 43.0.1 if on version 43.0.0. There is no configuration-based workaround available; upgrading to a patched version is the only remediation. As an interim measure, restricting execution of untrusted WebAssembly code is advisable until patching is complete (GitHub Advisory, Bytecode Alliance Advisory).

Community reactions

The vulnerability was published by Bytecode Alliance security researcher alexcrichton on April 9, 2026, alongside a broader set of Wasmtime security advisories. Red Hat tracked the issue via their Bugzilla system and assigned it medium severity. The Rust security advisory database (RustSec) published a corresponding advisory (RUSTSEC-2026-0092). OpenSUSE also issued a security announcement covering this and related updates. No significant broader media coverage or notable community controversy has been observed (Bytecode Alliance Blog, RustSec Advisory, Red Hat Bugzilla).

Additional resources

Linux Distribution fix status

Fix availability across major Linux distributions and their releases.

Debian

Fixed

sid

rust-wasmtime: 36.0.7+dfsg-1

Fixed

trixie

rust-wasmtime

Affected

Ubuntu

Unknown

devel

rust-wasmtime

Unknown

noble

rust-wasmtime

Unknown

noble (esm-apps)

rust-wasmtime

Unknown

resolute

rust-wasmtime

Unknown

resolute (esm-apps)

rust-wasmtime

Unknown

SourceThis report was generated using AI

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