FKIE_CVE-2025-59802
Vulnerability from fkie_nvd - Published: 2025-12-11 16:16 - Updated: 2026-10-14 21:10
Severity
Summary
Foxit PDF Editor and Reader before 2025.2.1 allow signature spoofing via OCG. When Optional Content Groups (OCG) are supported, the state property of an OCG is runtime-only and not included in the digital signature computation buffer. An attacker can leverage JavaScript or PDF triggers to dynamically change the visibility of OCG content after signing (Post-Sign), allowing the visual content of a signed PDF to be modified without invalidating the signature. This may result in a mismatch between the signed content and what the signer or verifier sees, undermining the trustworthiness of the digital signature. The fixed versions are 2025.2.1, 14.0.1, and 13.2.1.
References
| URL | Tags | ||
|---|---|---|---|
| cve@mitre.org | https://www.foxit.com/support/security-bulletins.html | Vendor Advisory |
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| foxit | pdf_editor | * | |
| foxit | pdf_editor | * | |
| foxit | pdf_editor | * | |
| foxit | pdf_editor | 14.0.0.68868 | |
| foxit | pdf_editor | 2025.1.0.66692 | |
| foxit | pdf_editor | 2025.2.0.68868 | |
| foxit | pdf_reader | * | |
| apple | macos | - | |
| foxit | pdf_editor | * | |
| foxit | pdf_editor | * | |
| foxit | pdf_editor | * | |
| foxit | pdf_editor | 14.0.0.33046 | |
| foxit | pdf_editor | 2025.1.0.27937 | |
| foxit | pdf_editor | 2025.2.0.33046 | |
| foxit | pdf_reader | * | |
| microsoft | windows | - |
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"value": "Foxit PDF Editor and Reader before 2025.2.1 allow signature spoofing via OCG. When Optional Content Groups (OCG) are supported, the state property of an OCG is runtime-only and not included in the digital signature computation buffer. An attacker can leverage JavaScript or PDF triggers to dynamically change the visibility of OCG content after signing (Post-Sign), allowing the visual content of a signed PDF to be modified without invalidating the signature. This may result in a mismatch between the signed content and what the signer or verifier sees, undermining the trustworthiness of the digital signature. The fixed versions are 2025.2.1, 14.0.1, and 13.2.1."
},
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"value": "Foxit PDF Editor y Reader anteriores a 2025.2.1 permiten la suplantaci\u00f3n de firmas a trav\u00e9s de OCG. Cuando se admiten los Grupos de Contenido Opcional (OCG), la propiedad de estado de un OCG es solo en tiempo de ejecuci\u00f3n y no se incluye en el b\u00fafer de c\u00e1lculo de la firma digital. Un atacante puede aprovechar JavaScript o activadores de PDF para cambiar din\u00e1micamente la visibilidad del contenido OCG despu\u00e9s de la firma (Post-Firma), permitiendo que el contenido visual de un PDF firmado sea modificado sin invalidar la firma. Esto puede resultar en una discrepancia entre el contenido firmado y lo que ve el firmante o el verificador, socavando la fiabilidad de la firma digital. Las versiones corregidas son 2025.2.1, 14.0.1 y 13.2.1."
}
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"id": "CVE-2025-59802",
"lastModified": "2026-10-14T21:10:00.183",
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"confidentialityImpact": "NONE",
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"privilegesRequired": "NONE",
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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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.