
Cloud Vulnerability DB
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CVE-2023-30624 affects Wasmtime, a standalone runtime for WebAssembly, in versions prior to 6.0.2, 7.0.1, and 8.0.1. The vulnerability involves undefined behavior in Wasmtime's implementation of managing per-instance state, such as tables and memories. This issue was discovered and disclosed on April 27, 2023 (GitHub Advisory).
The vulnerability stems from Wasmtime's runtime state implementation where an Instance structure contains a trailing VMContext structure with a runtime-defined layout unique per-module. The code uses unsafe Rust methods that take &self as an argument but modify data in the VMContext part of the allocation, leading to mutation of pointers derived from &self. When compiled to LLVM, these functions have noalias readonly parameters, making it undefined behavior to write through the pointers. This undefined behavior causes runtime-level issues particularly when compiled with LLVM 16, resulting in critical writes being optimized away (GitHub Advisory).
The vulnerability primarily affects versions of Wasmtime compiled with Rust 1.70 (beta) or later, where functions are known to be incorrectly compiled. While versions compiled with Rust 1.69 (stable) and prior are not known to have immediate issues, they theoretically could exhibit potential problems. The impact is particularly severe when compiled with LLVM 16, as it can result in critical memory writes being optimized away, potentially compromising the application's security and functionality (GitHub Advisory).
The issue has been patched in Wasmtime versions 6.0.2, 7.0.1, and 8.0.1. These versions contain fixes to work correctly on LLVM 16 and have no known undefined behavior on LLVM 15 and earlier. For users running Wasmtime compiled with Rust 1.69 and prior (using LLVM 15), while there are no known immediate issues, upgrading to a patched version is recommended. Users of beta Rust (1.70) or nightly Rust (1.71) must update to a patched version to ensure correct functionality (GitHub Advisory).
Source: This report was generated using AI
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