FKIE_CVE-2026-94220
Vulnerability from fkie_nvd - Published: 2026-10-01 12:17 - Updated: 2026-10-01 15:17
Severity
Summary
Cross-Site request forgery (CSRF) vulnerability in feishu-auth and dingtalk-auth plugins in Apache APISIX.
An attacker who can get a user to click a crafted link may cause that user's browser session on a protected route to be established under the attacker's identity instead of their own. Any work the user then performs in that session, including uploads, form submissions, and account bindings, lands in the attacker's account. This issue affects Apache APISIX: from 3.17.0 through 3.18.0.
Users are recommended to upgrade to version 3.19.0, which fixes the issue.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"defaultStatus": "unaffected",
"product": "Apache APISIX",
"vendor": "Apache Software Foundation",
"versions": [
{
"lessThanOrEqual": "3.18.0",
"status": "affected",
"version": "3.17.0",
"versionType": "semver"
}
]
}
],
"source": "security@apache.org"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Cross-Site request forgery (CSRF) vulnerability in feishu-auth and dingtalk-auth plugins in Apache APISIX.\n\n\n\nAn attacker who can get a user to click a crafted link may cause that user\u0027s browser session on a protected route to be established under the attacker\u0027s identity instead of their own. Any work the user then performs in that session, including uploads, form submissions, and account bindings, lands in the attacker\u0027s account.\u00a0This issue affects Apache APISIX: from 3.17.0 through 3.18.0.\n\n\n\nUsers are recommended to upgrade to version 3.19.0, which fixes the issue."
}
],
"id": "CVE-2026-94220",
"lastModified": "2026-10-01T15:17:36.123",
"metrics": {
"cvssMetricV40": [
{
"cvssData": {
"Automatable": "NOT_DEFINED",
"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
"attackComplexity": "LOW",
"attackRequirements": "PRESENT",
"attackVector": "NETWORK",
"availabilityRequirement": "NOT_DEFINED",
"baseScore": 2.1,
"baseSeverity": "LOW",
"confidentialityRequirement": "NOT_DEFINED",
"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",
"modifiedVulnAvailabilityImpact": "NOT_DEFINED",
"modifiedVulnConfidentialityImpact": "NOT_DEFINED",
"modifiedVulnIntegrityImpact": "NOT_DEFINED",
"privilegesRequired": "NONE",
"providerUrgency": "NOT_DEFINED",
"subAvailabilityImpact": "NONE",
"subConfidentialityImpact": "LOW",
"subIntegrityImpact": "LOW",
"userInteraction": "ACTIVE",
"valueDensity": "NOT_DEFINED",
"vectorString": "CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:A/VC:N/VI:N/VA:N/SC:L/SI:L/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"version": "4.0",
"vulnAvailabilityImpact": "NONE",
"vulnConfidentialityImpact": "NONE",
"vulnIntegrityImpact": "NONE",
"vulnerabilityResponseEffort": "NOT_DEFINED"
},
"source": "security@apache.org",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-94220",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-10-01T14:51:21.102558Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-10-01T12:17:16.930",
"references": [
{
"source": "security@apache.org",
"url": "https://lists.apache.org/thread.html/bf5q45ddyr2hf3hxkt56dzjlttgpdg78"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "http://www.openwall.com/lists/oss-security/2026/10/01/5"
}
],
"sourceIdentifier": "security@apache.org",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-352"
}
],
"source": "security@apache.org",
"type": "Secondary"
}
]
}
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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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