RUSTSEC-2021-0092 (CVE-2021-45690)
Vulnerability from osv_rustsec – Published: 2021-01-26 12:00 – Updated: 2023-06-13 13:10 – Source website
VLAI
Summary
Deserialization functions pass uninitialized memory to user-provided Read
Details
Affected versions of this crate passed an uninitialized buffer to a
user-provided Read instance in:
deserialize_binarydeserialize_stringdeserialize_extension_othersdeserialize_string_primitive
This can result in safe Read implementations reading from the uninitialized
buffer leading to undefined behavior.
References
{
"affected": [
{
"database_specific": {
"categories": [
"memory-exposure"
],
"cvss": null,
"informational": null
},
"ecosystem_specific": {
"affected_functions": null,
"affects": {
"arch": [],
"functions": [],
"os": []
}
},
"package": {
"ecosystem": "crates.io",
"name": "messagepack-rs",
"purl": "pkg:cargo/messagepack-rs"
},
"ranges": [
{
"events": [
{
"introduced": "0.0.0-0"
}
],
"type": "SEMVER"
}
],
"versions": []
}
],
"aliases": [
"CVE-2021-45690",
"CVE-2021-45691",
"CVE-2021-45692",
"CVE-2021-45693",
"GHSA-hr52-f9vp-582c",
"GHSA-jqjj-r4qp-x2gh",
"GHSA-jwfh-j623-m97h",
"GHSA-m325-rxjv-pwph",
"GHSA-vw5m-qw2r-m923"
],
"database_specific": {
"license": "CC0-1.0"
},
"details": "Affected versions of this crate passed an uninitialized buffer to a\nuser-provided `Read` instance in:\n\n* `deserialize_binary`\n* `deserialize_string`\n* `deserialize_extension_others`\n* `deserialize_string_primitive`\n\nThis can result in safe `Read` implementations reading from the uninitialized\nbuffer leading to undefined behavior.",
"id": "RUSTSEC-2021-0092",
"modified": "2023-06-13T13:10:24Z",
"published": "2021-01-26T12:00:00Z",
"references": [
{
"type": "PACKAGE",
"url": "https://crates.io/crates/messagepack-rs"
},
{
"type": "ADVISORY",
"url": "https://rustsec.org/advisories/RUSTSEC-2021-0092.html"
},
{
"type": "REPORT",
"url": "https://github.com/otake84/messagepack-rs/issues/2"
}
],
"related": [],
"severity": [],
"summary": "Deserialization functions pass uninitialized memory to user-provided Read"
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- 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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