RUSTSEC-2019-0034 (CVE-2019-25009)
Vulnerability from osv_rustsec – Published: 2019-11-16 12:00 – Updated: 2023-06-13 13:10 – Source website
VLAI
Summary
HeaderMap::Drain API is unsound
Details
Affected versions of this crate incorrectly used raw pointer, which introduced unsoundness in its public safe API.
Failing to drop the Drain struct causes double-free, and it is possible to violate Rust's alias rule and cause data race with Drain's Iterator implementation.
The flaw was corrected in 0.1.20 release of http crate.
Severity
9.8 (Critical)
References
| URL | Type | |
|---|---|---|
{
"affected": [
{
"database_specific": {
"categories": [
"memory-corruption"
],
"cvss": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"informational": null
},
"ecosystem_specific": {
"affected_functions": null,
"affects": {
"arch": [],
"functions": [
"http::header::HeaderMap::drain"
],
"os": []
}
},
"package": {
"ecosystem": "crates.io",
"name": "http",
"purl": "pkg:cargo/http"
},
"ranges": [
{
"events": [
{
"introduced": "0.0.0-0"
},
{
"fixed": "0.1.20"
}
],
"type": "SEMVER"
}
],
"versions": []
}
],
"aliases": [
"CVE-2019-25009",
"GHSA-6rhx-hqxm-8p36"
],
"database_specific": {
"license": "CC0-1.0"
},
"details": "Affected versions of this crate incorrectly used raw pointer,\nwhich introduced unsoundness in its public safe API.\n\n[Failing to drop the Drain struct causes double-free](https://github.com/hyperium/http/issues/354),\nand [it is possible to violate Rust\u0027s alias rule and cause data race with Drain\u0027s Iterator implementation](https://github.com/hyperium/http/issues/355).\n\nThe flaw was corrected in 0.1.20 release of `http` crate.",
"id": "RUSTSEC-2019-0034",
"modified": "2023-06-13T13:10:24Z",
"published": "2019-11-16T12:00:00Z",
"references": [
{
"type": "PACKAGE",
"url": "https://crates.io/crates/http"
},
{
"type": "ADVISORY",
"url": "https://rustsec.org/advisories/RUSTSEC-2019-0034.html"
}
],
"related": [],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
],
"summary": "HeaderMap::Drain API is unsound"
}
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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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