rustsec-2023-0080
Vulnerability from osv_rustsec
Published
2023-12-18 12:00
Modified
2025-10-28 06:02
Summary
Buffer overflow due to integer overflow in `transpose`
Details
Given the function transpose::transpose:
fn transpose<T: Copy>(input: &[T], output: &mut [T], input_width: usize, input_height: usize)
The safety check input_width * input_height == output.len() can fail due to input_width * input_height overflowing in such a way that it equals output.len().
As a result of failing the safety check, memory past the end of output is written to. This only occurs in release mode since * panics on overflow in debug mode.
Exploiting this issue requires the caller to pass input_width and input_height arguments such that multiplying them overflows, and the overflown result equals the lengths of input and output slices.
References
| URL | Type | |
|---|---|---|
{
"affected": [
{
"database_specific": {
"categories": [
"memory-corruption"
],
"cvss": null,
"informational": null
},
"ecosystem_specific": {
"affected_functions": null,
"affects": {
"arch": [],
"functions": [
"transpose::transpose"
],
"os": []
}
},
"package": {
"ecosystem": "crates.io",
"name": "transpose",
"purl": "pkg:cargo/transpose"
},
"ranges": [
{
"events": [
{
"introduced": "0.0.0-0"
},
{
"fixed": "0.2.3"
}
],
"type": "SEMVER"
}
],
"versions": []
}
],
"aliases": [
"CVE-2023-53156",
"GHSA-5gmm-6m36-r7jh"
],
"database_specific": {
"license": "CC0-1.0"
},
"details": "Given the function `transpose::transpose`:\n```rust\nfn transpose\u003cT: Copy\u003e(input: \u0026[T], output: \u0026mut [T], input_width: usize, input_height: usize)\n```\n\nThe safety check `input_width * input_height == output.len()` can fail due to `input_width * input_height` overflowing in such a way that it equals `output.len()`.\nAs a result of failing the safety check, memory past the end of `output` is written to. This only occurs in release mode since `*` panics on overflow in debug mode.\n\nExploiting this issue requires the caller to pass `input_width` and `input_height` arguments such that multiplying them overflows, and the overflown result equals the lengths of input and output slices.",
"id": "RUSTSEC-2023-0080",
"modified": "2025-10-28T06:02:18Z",
"published": "2023-12-18T12:00:00Z",
"references": [
{
"type": "PACKAGE",
"url": "https://crates.io/crates/transpose"
},
{
"type": "ADVISORY",
"url": "https://rustsec.org/advisories/RUSTSEC-2023-0080.html"
},
{
"type": "REPORT",
"url": "https://github.com/ejmahler/transpose/issues/11"
}
],
"related": [],
"severity": [],
"summary": "Buffer overflow due to integer overflow in `transpose`"
}
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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- Confirmed: The vulnerability has been validated from an analyst's perspective.
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- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
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- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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