CVE-2026-75743 (GCVE-0-2026-75743)
Vulnerability from cvelistv5 – Published: 2026-09-22 18:56 – Updated: 2026-09-22 18:56
VLAI
EPSS
VEX
Title
Adobe Experience Manager Forms JEE | Cross-Site Request Forgery (CSRF) (CWE-352)
Summary
Adobe Experience Manager Forms JEE is affected by a Cross-Site Request Forgery (CSRF) vulnerability that could result in a Security feature bypass. An attacker could leverage this vulnerability to bypass security measures and gain unauthorized write access, causing a limited disruption to availability. Exploitation of this issue requires user interaction in that a victim must visit a maliciously crafted URL or interact with a compromised web page.
Severity
7.1 (High)
CWE
- CWE-352 - Cross-Site Request Forgery (CSRF) (CWE-352)
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://helpx.adobe.com/security/products/aem-for… | vendor-advisory |
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Adobe | AEM 6.5 Forms JEE |
Affected:
0 , ≤ 6.5.25
(custom)
Unaffected: 6.5.25 (AEMForms-6.5.0-0134 Hotfix) (custom) |
guessed | |
| Adobe | AEM 6.5 LTS Forms JEE |
Affected:
0 , ≤ 6.5 LTS SP2
(custom)
Unaffected: 6.5 LTS SP3 (custom) |
guessed |
Date Public
2026-09-22 17:00
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}
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"providerMetadata": {
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"orgId": "078d4453-3bcd-4900-85e6-15281da43538",
"shortName": "adobe"
},
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],
"url": "https://helpx.adobe.com/security/products/aem-forms/apsb26-151.html"
}
],
"source": {
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},
"title": "Adobe Experience Manager Forms JEE | Cross-Site Request Forgery (CSRF) (CWE-352)",
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"cveId": "CVE-2026-75743",
"datePublished": "2026-09-22T18:56:04.606Z",
"dateReserved": "2026-08-18T01:29:54.622Z",
"dateUpdated": "2026-09-22T18:56:04.606Z",
"state": "PUBLISHED"
},
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"dataVersion": "5.2",
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}
],
"id": "CVE-2026-75743",
"lastModified": "2026-09-22T19:23:57.800",
"metrics": {
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},
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"published": "2026-09-22T19:16:47.247",
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"source": "psirt@adobe.com",
"url": "https://helpx.adobe.com/security/products/aem-forms/apsb26-151.html"
}
],
"sourceIdentifier": "psirt@adobe.com",
"vulnStatus": "Awaiting Analysis",
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"value": "CWE-352"
}
],
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"type": "Primary"
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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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