GSD-2022-25863
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
The package gatsby-plugin-mdx before 2.14.1, from 3.0.0 and before 3.15.2 are vulnerable to Deserialization of Untrusted Data when passing input through to the gray-matter package, due to its default configurations that are missing input sanitization. Exploiting this vulnerability is possible when passing input in both webpack (MDX files in src/pages or MDX file imported as a component in frontend / React code) and data mode (querying MDX nodes via GraphQL). Workaround: If an older version of gatsby-plugin-mdx must be used, input passed into the plugin should be sanitized ahead of processing.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-25863",
"description": "The package gatsby-plugin-mdx before 2.14.1, from 3.0.0 and before 3.15.2 are vulnerable to Deserialization of Untrusted Data when passing input through to the gray-matter package, due to its default configurations that are missing input sanitization. Exploiting this vulnerability is possible when passing input in both webpack (MDX files in src/pages or MDX file imported as a component in frontend / React code) and data mode (querying MDX nodes via GraphQL). Workaround: If an older version of gatsby-plugin-mdx must be used, input passed into the plugin should be sanitized ahead of processing.",
"id": "GSD-2022-25863"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2022-25863"
],
"details": "The package gatsby-plugin-mdx before 2.14.1, from 3.0.0 and before 3.15.2 are vulnerable to Deserialization of Untrusted Data when passing input through to the gray-matter package, due to its default configurations that are missing input sanitization. Exploiting this vulnerability is possible when passing input in both webpack (MDX files in src/pages or MDX file imported as a component in frontend / React code) and data mode (querying MDX nodes via GraphQL). Workaround: If an older version of gatsby-plugin-mdx must be used, input passed into the plugin should be sanitized ahead of processing.",
"id": "GSD-2022-25863",
"modified": "2023-12-13T01:19:26.268662Z",
"schema_version": "1.4.0"
}
},
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"DATE_PUBLIC": "2022-06-10T20:00:07.103333Z",
"ID": "CVE-2022-25863",
"STATE": "PUBLIC",
"TITLE": "Deserialization of Untrusted Data"
},
"affects": {
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"product": {
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},
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"credit": [
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"value": "Feng Xiao and Zhongfu Su"
}
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"value": "The package gatsby-plugin-mdx before 2.14.1, from 3.0.0 and before 3.15.2 are vulnerable to Deserialization of Untrusted Data when passing input through to the gray-matter package, due to its default configurations that are missing input sanitization. Exploiting this vulnerability is possible when passing input in both webpack (MDX files in src/pages or MDX file imported as a component in frontend / React code) and data mode (querying MDX nodes via GraphQL). Workaround: If an older version of gatsby-plugin-mdx must be used, input passed into the plugin should be sanitized ahead of processing."
}
]
},
"impact": {
"cvss": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 8.1,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N/E:P",
"version": "3.1"
}
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"name": "https://snyk.io/vuln/SNYK-JS-GATSBYPLUGINMDX-2405699",
"refsource": "MISC",
"url": "https://snyk.io/vuln/SNYK-JS-GATSBYPLUGINMDX-2405699"
},
{
"name": "https://drive.google.com/file/d/1EoCzbwTWOM8-fjvwMbH3bqcZ2iKksxTW/view?usp=sharing",
"refsource": "MISC",
"url": "https://drive.google.com/file/d/1EoCzbwTWOM8-fjvwMbH3bqcZ2iKksxTW/view?usp=sharing"
},
{
"name": "https://github.com/gatsbyjs/gatsby/pull/35830",
"refsource": "MISC",
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},
{
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},
"gitlab.com": {
"advisories": [
{
"affected_range": "\u003c2.14.1||\u003e=3.0.0 \u003c3.15.2",
"affected_versions": "All versions before 2.14.1, all versions starting from 3.0.0 before 3.15.2",
"cvss_v2": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"cvss_v3": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"cwe_ids": [
"CWE-1035",
"CWE-502",
"CWE-937"
],
"date": "2022-06-17",
"description": "The package gatsby-plugin-mdx before 2.14.1, from 3.0.0 and before 3.15.2 is vulnerable to Deserialization of Untrusted Data when passing input through to the gray-matter package, due to its default configurations that are missing input sanitization. Exploiting this vulnerability is possible when passing input in both webpack (MDX files in src/pages or MDX file imported as a component in frontend / React code) and data mode (querying MDX nodes via GraphQL). Workaround: If an older version of gatsby-plugin-mdx must be used, input passed into the plugin should be sanitized ahead of processing.",
"fixed_versions": [
"2.14.1",
"3.15.2"
],
"identifier": "CVE-2022-25863",
"identifiers": [
"CVE-2022-25863"
],
"not_impacted": "All versions after 2.14.1 before 3.0.0, all versions after 3.15.2",
"package_slug": "npm/gatsby",
"pubdate": "2022-06-10",
"solution": "Upgrade to version 2.14.1, 3.15.2, or above.",
"title": "Deserialization of Untrusted Data",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2022-25863",
"https://github.com/gatsbyjs/gatsby/pull/35830",
"https://github.com/gatsbyjs/gatsby/pull/35830/commits/f214eb0694c61e348b2751cecd1aace2046bc46e",
"https://drive.google.com/file/d/1EoCzbwTWOM8-fjvwMbH3bqcZ2iKksxTW/view?usp=sharing",
"https://snyk.io/vuln/SNYK-JS-GATSBYPLUGINMDX-2405699"
],
"uuid": "7ca1840d-4f5b-4f7b-925d-e29cf0e59107"
}
]
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"versionEndExcluding": "3.15.2",
"versionStartIncluding": "3.0.0",
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"accessVector": "NETWORK",
"authentication": "NONE",
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"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
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"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
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"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
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},
"lastModifiedDate": "2022-06-17T18:40Z",
"publishedDate": "2022-06-10T20:15Z"
}
}
}
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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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