GSD-2018-1334
Vulnerability from gsd - Updated: 2023-12-13 01:22Details
In Apache Spark 1.0.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when using PySpark or SparkR, it's possible for a different local user to connect to the Spark application and impersonate the user running the Spark application.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2018-1334",
"description": "In Apache Spark 1.0.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when using PySpark or SparkR, it\u0027s possible for a different local user to connect to the Spark application and impersonate the user running the Spark application.",
"id": "GSD-2018-1334",
"references": [
"https://www.suse.com/security/cve/CVE-2018-1334.html"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2018-1334"
],
"details": "In Apache Spark 1.0.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when using PySpark or SparkR, it\u0027s possible for a different local user to connect to the Spark application and impersonate the user running the Spark application.",
"id": "GSD-2018-1334",
"modified": "2023-12-13T01:22:37.557987Z",
"schema_version": "1.4.0"
}
},
"namespaces": {
"cve.org": {
"CVE_data_meta": {
"ASSIGNER": "security@apache.org",
"DATE_PUBLIC": "2018-07-11T00:00:00",
"ID": "CVE-2018-1334",
"STATE": "PUBLIC"
},
"affects": {
"vendor": {
"vendor_data": [
{
"product": {
"product_data": [
{
"product_name": "Apache Spark",
"version": {
"version_data": [
{
"version_value": "1.0.0 to 2.1.2"
},
{
"version_value": "2.2.0 to 2.2.1"
},
{
"version_value": "2.3.0"
}
]
}
}
]
},
"vendor_name": "Apache Software Foundation"
}
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"data_format": "MITRE",
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"description": {
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{
"lang": "eng",
"value": "In Apache Spark 1.0.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when using PySpark or SparkR, it\u0027s possible for a different local user to connect to the Spark application and impersonate the user running the Spark application."
}
]
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"lang": "eng",
"value": "Information Disclosure"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://spark.apache.org/security.html#CVE-2018-1334",
"refsource": "CONFIRM",
"url": "https://spark.apache.org/security.html#CVE-2018-1334"
},
{
"name": "[dev] 20180711 CVE-2018-1334 Apache Spark local privilege escalation vulnerability",
"refsource": "MLIST",
"url": "https://lists.apache.org/thread.html/4d6d210e319a501b740293daaeeeadb51927111fb8261a3e4cd60060@%3Cdev.spark.apache.org%3E"
}
]
}
},
"gitlab.com": {
"advisories": [
{
"affected_range": "(,2.3.0]",
"affected_versions": "All versions up to 2.1.2, all versions starting from 2.2.0 up to 2.2.1, version 2.3.0",
"cvss_v2": "AV:L/AC:M/Au:N/C:N/I:P/A:N",
"cvss_v3": "CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:N",
"cwe_ids": [
"CWE-1035",
"CWE-200",
"CWE-937"
],
"date": "2018-09-10",
"description": "It is possible for a different local user to connect to the Spark application and impersonate the user running the Spark application.",
"fixed_versions": [
"2.3.1"
],
"identifier": "CVE-2018-1334",
"identifiers": [
"CVE-2018-1334"
],
"not_impacted": "All versions after 2.3.0",
"package_slug": "maven/org.apache.spark/spark-core",
"pubdate": "2018-07-12",
"solution": "Upgrade to version 2.3.1 or above.",
"title": "Information Exposure",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2018-1334",
"https://spark.apache.org/security.html#CVE-2018-1334"
],
"uuid": "45d0b11f-0fe2-4587-b999-b9fe2f0eb01e"
},
{
"affected_range": "[1.0.0,2.1.3),[2.2.0,2.2.2)",
"affected_versions": "All versions starting from 1.0.0 before 2.1.3, all versions starting from 2.2.0 before 2.2.2",
"cvss_v2": "AV:L/AC:M/Au:N/C:N/I:P/A:N",
"cvss_v3": "CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:N",
"cwe_ids": [
"CWE-1035",
"CWE-200",
"CWE-937"
],
"date": "2021-09-02",
"description": "In Apache Spark 1.0.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when using PySpark or SparkR, it\u0027s possible for a different local user to connect to the Spark application and impersonate the user running the Spark application.",
"fixed_versions": [
"2.1.3",
"2.2.2"
],
"identifier": "CVE-2018-1334",
"identifiers": [
"GHSA-6mqq-8r44-vmjc",
"CVE-2018-1334"
],
"not_impacted": "All versions before 1.0.0, all versions starting from 2.1.3 before 2.2.0, all versions starting from 2.2.2",
"package_slug": "maven/org.apache.spark/spark-core_2.10",
"pubdate": "2019-03-14",
"solution": "Upgrade to versions 2.1.3, 2.2.2 or above.",
"title": "Exposure of Sensitive Information to an Unauthorized Actor",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2018-1334",
"https://github.com/advisories/GHSA-6mqq-8r44-vmjc"
],
"uuid": "c7209c2e-fcaa-4ff6-8fcd-7a8079fedc84"
},
{
"affected_range": "[1.0.0,2.1.3),[2.2.0,2.2.2),[2.3.0]",
"affected_versions": "All versions starting from 1.0.0 before 2.1.3, all versions starting from 2.2.0 before 2.2.2, version 2.3.0",
"cvss_v2": "AV:L/AC:M/Au:N/C:N/I:P/A:N",
"cvss_v3": "CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:N",
"cwe_ids": [
"CWE-1035",
"CWE-200",
"CWE-937"
],
"date": "2021-09-02",
"description": "In Apache Spark 1.0.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when using PySpark or SparkR, it\u0027s possible for a different local user to connect to the Spark application and impersonate the user running the Spark application.",
"fixed_versions": [
"2.1.3",
"2.2.2",
"2.3.1"
],
"identifier": "CVE-2018-1334",
"identifiers": [
"GHSA-6mqq-8r44-vmjc",
"CVE-2018-1334"
],
"not_impacted": "All versions before 1.0.0, all versions starting from 2.1.3 before 2.2.0, all versions starting from 2.2.2 before 2.3.0, all versions after 2.3.0",
"package_slug": "maven/org.apache.spark/spark-core_2.11",
"pubdate": "2019-03-14",
"solution": "Upgrade to versions 2.1.3, 2.2.2, 2.3.1 or above.",
"title": "Exposure of Sensitive Information to an Unauthorized Actor",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2018-1334",
"https://github.com/advisories/GHSA-6mqq-8r44-vmjc",
"https://lists.apache.org/thread.html/4d6d210e319a501b740293daaeeeadb51927111fb8261a3e4cd60060@%3Cdev.spark.apache.org%3E",
"https://spark.apache.org/security.html#CVE-2018-1334"
],
"uuid": "94bf27df-3d58-4ea9-b92a-a3308ac26f1f"
}
]
},
"nvd.nist.gov": {
"configurations": {
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"nodes": [
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"children": [],
"cpe_match": [
{
"cpe23Uri": "cpe:2.3:a:apache:spark:*:*:*:*:*:*:*:*",
"cpe_name": [],
"versionEndIncluding": "2.2.1",
"versionStartIncluding": "2.2.0",
"vulnerable": true
},
{
"cpe23Uri": "cpe:2.3:a:apache:spark:*:*:*:*:*:*:*:*",
"cpe_name": [],
"versionEndIncluding": "2.1.2",
"vulnerable": true
},
{
"cpe23Uri": "cpe:2.3:a:apache:spark:2.3.0:*:*:*:*:*:*:*",
"cpe_name": [],
"vulnerable": true
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"operator": "OR"
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"cve": {
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"ID": "CVE-2018-1334"
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"description": {
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"value": "In Apache Spark 1.0.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when using PySpark or SparkR, it\u0027s possible for a different local user to connect to the Spark application and impersonate the user running the Spark application."
}
]
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"lang": "en",
"value": "CWE-200"
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]
},
"references": {
"reference_data": [
{
"name": "https://spark.apache.org/security.html#CVE-2018-1334",
"refsource": "CONFIRM",
"tags": [
"Mitigation",
"Vendor Advisory"
],
"url": "https://spark.apache.org/security.html#CVE-2018-1334"
},
{
"name": "[dev] 20180711 CVE-2018-1334 Apache Spark local privilege escalation vulnerability",
"refsource": "MLIST",
"tags": [
"Mitigation",
"Mailing List",
"Vendor Advisory"
],
"url": "https://lists.apache.org/thread.html/4d6d210e319a501b740293daaeeeadb51927111fb8261a3e4cd60060@%3Cdev.spark.apache.org%3E"
}
]
}
},
"impact": {
"baseMetricV2": {
"cvssV2": {
"accessComplexity": "MEDIUM",
"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 1.9,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:L/AC:M/Au:N/C:N/I:P/A:N",
"version": "2.0"
},
"exploitabilityScore": 3.4,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "LOW",
"userInteractionRequired": false
},
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "HIGH",
"attackVector": "LOCAL",
"availabilityImpact": "NONE",
"baseScore": 4.7,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:N",
"version": "3.0"
},
"exploitabilityScore": 1.0,
"impactScore": 3.6
}
},
"lastModifiedDate": "2018-09-10T16:48Z",
"publishedDate": "2018-07-12T13:29Z"
}
}
}
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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.
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- 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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