CVE-2026-93922 (GCVE-0-2026-93922)
Vulnerability from cvelistv5 – Published: 2026-09-18 23:12 – Updated: 2026-09-18 23:12
VLAI
EPSS
VEX
Title
SiYuan through 3.8.4 Stored XSS via notebook names
Summary
SiYuan through 3.8.4 renders notebook names as raw HTML in the Daily Note picker dialog without escaping, allowing stored cross-site scripting in the Electron renderer. Attackers can create notebooks with HTML payloads in names that execute JavaScript with Node.js access when the picker opens, enabling operating system command execution.
Severity
CWE
- CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/siyuan-note/siyuan/security/ad… | vendor-advisory |
| https://github.com/siyuan-note/siyuan | product |
| https://github.com/siyuan-note/siyuan/blob/v3.8.4… | technical-description |
| https://github.com/siyuan-note/siyuan/blob/v3.8.4… | technical-description |
| https://www.vulncheck.com/advisories/siyuan-throu… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| siyuan-note | siyuan |
Affected:
0 , ≤ 3.8.4
(semver)
cpe:2.3:a:b3log:siyuan:*:*:*:*:*:*:*:* |
Date Public
2026-09-18 00:00
{
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"collectionURL": "https://pkg.go.dev",
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"packageName": "github.com/siyuan-note/siyuan/kernel",
"packageURL": "pkg:golang/github.com/siyuan-note/siyuan/kernel",
"product": "siyuan",
"vendor": "siyuan-note",
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}
],
"metrics": [
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"attackRequirements": "NONE",
"attackVector": "NETWORK",
"baseScore": 8.6,
"baseSeverity": "HIGH",
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"subAvailabilityImpact": "NONE",
"subConfidentialityImpact": "NONE",
"subIntegrityImpact": "NONE",
"userInteraction": "ACTIVE",
"vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N",
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"problemTypes": [
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"description": "Improper Neutralization of Input During Web Page Generation (\u0027Cross-site Scripting\u0027)",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
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"orgId": "83251b91-4cc7-4094-a5c7-464a1b83ea10",
"shortName": "VulnCheck"
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"references": [
{
"name": "GitHub Security Advisory (GHSA-8c2m-33v9-vvqm)",
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"url": "https://github.com/siyuan-note/siyuan/security/advisories/GHSA-8c2m-33v9-vvqm"
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{
"tags": [
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"url": "https://github.com/siyuan-note/siyuan"
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"url": "https://github.com/siyuan-note/siyuan/blob/v3.8.4/app/src/util/mount.ts#L72"
},
{
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],
"url": "https://github.com/siyuan-note/siyuan/blob/v3.8.4/kernel/model/file.go#L2446-L2454"
},
{
"name": "VulnCheck Advisory: SiYuan through 3.8.4 Stored XSS via notebook names",
"tags": [
"third-party-advisory"
],
"url": "https://www.vulncheck.com/advisories/siyuan-through-3.8.4-stored-xss-via-notebook-names"
}
],
"title": "SiYuan through 3.8.4 Stored XSS via notebook names",
"x_generator": {
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}
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},
"cveMetadata": {
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"assignerShortName": "VulnCheck",
"cveId": "CVE-2026-93922",
"datePublished": "2026-09-18T23:12:08.503Z",
"dateReserved": "2026-09-18T21:57:00.085Z",
"dateUpdated": "2026-09-18T23:12:08.503Z",
"state": "PUBLISHED"
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"packageURL": "pkg:golang/github.com/siyuan-note/siyuan/kernel",
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"status": "affected",
"version": "0",
"versionType": "semver"
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],
"source": "disclosure@vulncheck.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "SiYuan through 3.8.4 renders notebook names as raw HTML in the Daily Note picker dialog without escaping, allowing stored cross-site scripting in the Electron renderer. Attackers can create notebooks with HTML payloads in names that execute JavaScript with Node.js access when the picker opens, enabling operating system command execution."
}
],
"id": "CVE-2026-93922",
"lastModified": "2026-09-19T00:16:57.913",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 5.9,
"source": "disclosure@vulncheck.com",
"type": "Primary"
}
],
"cvssMetricV40": [
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"cvssData": {
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"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
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"exploitMaturity": "NOT_DEFINED",
"integrityRequirement": "NOT_DEFINED",
"modifiedAttackComplexity": "NOT_DEFINED",
"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
"modifiedPrivilegesRequired": "NOT_DEFINED",
"modifiedSubAvailabilityImpact": "NOT_DEFINED",
"modifiedSubConfidentialityImpact": "NOT_DEFINED",
"modifiedSubIntegrityImpact": "NOT_DEFINED",
"modifiedUserInteraction": "NOT_DEFINED",
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"modifiedVulnIntegrityImpact": "NOT_DEFINED",
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"valueDensity": "NOT_DEFINED",
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},
"published": "2026-09-19T00:16:57.783",
"references": [
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"source": "disclosure@vulncheck.com",
"url": "https://github.com/siyuan-note/siyuan"
},
{
"source": "disclosure@vulncheck.com",
"url": "https://github.com/siyuan-note/siyuan/blob/v3.8.4/app/src/util/mount.ts#L72"
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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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