GSD-2021-31921
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
Istio before 1.8.6 and 1.9.x before 1.9.5 contains a remotely exploitable vulnerability where an external client can access unexpected services in the cluster, bypassing authorization checks, when a gateway is configured with AUTO_PASSTHROUGH routing configuration.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-31921",
"description": "Istio before 1.8.6 and 1.9.x before 1.9.5 contains a remotely exploitable vulnerability where an external client can access unexpected services in the cluster, bypassing authorization checks, when a gateway is configured with AUTO_PASSTHROUGH routing configuration.",
"id": "GSD-2021-31921",
"references": [
"https://access.redhat.com/errata/RHSA-2021:2085",
"https://access.redhat.com/errata/RHSA-2021:2061",
"https://security.archlinux.org/CVE-2021-31921",
"https://linux.oracle.com/cve/CVE-2021-31921.html"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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"id": "GSD-2021-31921",
"modified": "2023-12-13T01:23:13.247722Z",
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"gitlab.com": {
"advisories": [
{
"affected_range": "\u003c1.8.6||\u003e=1.9.0 \u003c1.9.5",
"affected_versions": "All versions before 1.8.6, all versions starting from 1.9.0 before 1.9.5",
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"cwe_ids": [
"CWE-1035",
"CWE-862",
"CWE-937"
],
"date": "2022-05-01",
"description": "Istio, when a gateway is using the AUTO_PASSTHROUGH routing configuration, allows attackers to bypass authorization checks and access unexpected services in the cluster.",
"fixed_versions": [
"1.8.6",
"1.9.5"
],
"identifier": "CVE-2021-31921",
"identifiers": [
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"not_impacted": "",
"package_slug": "go/github.com/istio/istio",
"pubdate": "2021-06-02",
"solution": "Upgrade to versions 1.8.6, 1.9.5 or above.",
"title": "Missing Authorization",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2021-31921",
"https://istio.io/latest/news/security/istio-security-2021-006/"
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"impact": {
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"scope": "UNCHANGED",
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},
"lastModifiedDate": "2022-05-01T01:54Z",
"publishedDate": "2021-06-02T16:15Z"
}
}
}
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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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