FKIE_CVE-2026-59923
Vulnerability from fkie_nvd - Published: 2026-07-08 17:17 - Updated: 2026-10-25 17:10
Severity
Summary
Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, HTMLRenderer.safe_url() does not block percent-encoded javascript URIs, allowing attacker-supplied Markdown links or images to bypass URL protections and execute script in rendered HTML. This issue is fixed in version 3.3.0.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| mistune_project | mistune | * |
{
"affected": [
{
"affectedData": [
{
"product": "mistune",
"vendor": "lepture",
"versions": [
{
"status": "affected",
"version": "\u003c 3.3.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:mistune_project:mistune:*:*:*:*:*:*:*:*",
"matchCriteriaId": "63E1B933-D2E2-44EF-A56C-94483300845B",
"versionEndExcluding": "3.3.0",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, HTMLRenderer.safe_url() does not block percent-encoded javascript URIs, allowing attacker-supplied Markdown links or images to bypass URL protections and execute script in rendered HTML. This issue is fixed in version 3.3.0."
},
{
"lang": "es",
"value": "Mistune es un analizador de Markdown en Python con renderizadores y complementos. Antes de la versi\u00f3n 3.3.0, HTMLRenderer.safe_url() no bloquea las URIs de javascript codificadas por porcentaje, lo que permite que los enlaces o im\u00e1genes de Markdown proporcionados por el atacante eludan las protecciones de URL y ejecuten scripts en el HTML renderizado. Este problema se corrigi\u00f3 en la versi\u00f3n 3.3.0."
}
],
"id": "CVE-2026-59923",
"lastModified": "2026-10-25T17:10:00.197",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 6.1,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 2.7,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-59923",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-07-08T16:41:40.162930Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-07-08T17:17:27.910",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch"
],
"url": "https://github.com/lepture/mistune/commit/c7101fcbb6e8790e8e39157c5ca2238fc6dd6cbc"
},
{
"source": "security-advisories@github.com",
"tags": [
"Release Notes"
],
"url": "https://github.com/lepture/mistune/releases/tag/v3.3.0"
},
{
"source": "security-advisories@github.com",
"tags": [
"Exploit",
"Vendor Advisory"
],
"url": "https://github.com/lepture/mistune/security/advisories/GHSA-8c25-4j27-2rv3"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"tags": [
"Exploit",
"Vendor Advisory"
],
"url": "https://github.com/lepture/mistune/security/advisories/GHSA-8c25-4j27-2rv3"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-79"
}
],
"source": "security-advisories@github.com",
"type": "Primary"
}
]
}
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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Vulnerability timeline
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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.