GSD-2022-32215
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
The llhttp parser <v14.20.1, <v16.17.1 and <v18.9.1 in the http module in Node.js does not correctly handle multi-line Transfer-Encoding headers. This can lead to HTTP Request Smuggling (HRS).
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-32215",
"description": "The llhttp parser in the http module in Node v17.6.0 does not correctly handle multi-line Transfer-Encoding headers. This can lead to HTTP Request Smuggling (HRS).",
"id": "GSD-2022-32215",
"references": [
"https://www.suse.com/security/cve/CVE-2022-32215.html",
"https://advisories.mageia.org/CVE-2022-32215.html",
"https://access.redhat.com/errata/RHSA-2022:6389",
"https://access.redhat.com/errata/RHSA-2022:6448",
"https://access.redhat.com/errata/RHSA-2022:6449",
"https://access.redhat.com/errata/RHSA-2022:6595",
"https://access.redhat.com/errata/RHSA-2022:6985",
"https://www.debian.org/security/2023/dsa-5326"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
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],
"details": "The llhttp parser \u003cv14.20.1, \u003cv16.17.1 and \u003cv18.9.1 in the http module in Node.js does not correctly handle multi-line Transfer-Encoding headers. This can lead to HTTP Request Smuggling (HRS).",
"id": "GSD-2022-32215",
"modified": "2023-12-13T01:19:12.271981Z",
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"affected_range": "\u003e=14.0.0 \u003c14.20.1||\u003e=16.0.0 \u003c16.17.1||\u003e=18.0.0 \u003c18.9.1",
"affected_versions": "All versions starting from 14.0.0 before 14.20.1, all versions starting from 16.0.0 before 16.17.1, all versions starting from 18.0.0 before 18.9.1",
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"cwe_ids": [
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"CWE-444",
"CWE-937"
],
"date": "2023-07-19",
"description": "Not vulnerable",
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"identifier": "CVE-2022-32215",
"identifiers": [
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"not_impacted": "",
"package_slug": "npm/llhttp",
"pubdate": "2022-07-14",
"solution": "Unfortunately, there is no solution available yet.",
"title": "Not vulnerable",
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"https://nodejs.org/en/blog/vulnerability/july-2022-security-releases/",
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"lastModifiedDate": "2023-07-19T00:56Z",
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}
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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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