CVE-2026-105114 (GCVE-0-2026-105114)
Vulnerability from cvelistv5 – Published: 2026-10-03 12:14 – Updated: 2026-10-03 12:14
VLAI
EPSS
VEX
Title
OpenAM before 16.1.3 Reflected XSS via OAuth2 Authorization Error Page
Summary
OpenAM before 16.1.3 contains a reflected cross-site scripting vulnerability that allows unauthenticated attackers to inject script by supplying crafted parameters rendered unencoded on the OAuth2 authorization error page. Attackers can lure victims to a crafted /oauth2/authorize link with repeated parameters to run JavaScript in the OpenAM origin, acting within existing sessions or redirecting to phishing pages.
Severity
6.1 (Medium)
CWE
- CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/OpenIdentityPlatform/OpenAM/se… | vendor-advisory |
| https://www.vulncheck.com/advisories/openam-befor… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| OpenIdentityPlatform | OpenAM |
Affected:
0 , < 16.1.3
(semver)
Unaffected: 16.1.3 (semver) cpe:2.3:a:openidentityplatform:openam:*:*:*:*:*:*:*:* |
Date Public
2026-09-18 00:00
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},
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"cpeApplicability": [
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"credits": [
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"datePublic": "2026-09-18T00:00:00.000Z",
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}
],
"metrics": [
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"attackRequirements": "NONE",
"attackVector": "NETWORK",
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"privilegesRequired": "NONE",
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"subConfidentialityImpact": "LOW",
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"descriptions": [
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"description": "Improper Neutralization of Input During Web Page Generation (\u0027Cross-site Scripting\u0027)",
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"title": "OpenAM before 16.1.3 Reflected XSS via OAuth2 Authorization Error Page",
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"cveMetadata": {
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"cveId": "CVE-2026-105114",
"datePublished": "2026-10-03T12:14:39.964Z",
"dateReserved": "2026-10-03T12:04:36.964Z",
"dateUpdated": "2026-10-03T12:14:39.964Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
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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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