FKIE_CVE-2018-12811
Vulnerability from fkie_nvd - Published: 2018-08-29 13:29 - Updated: 2026-06-17 01:38
Severity
Summary
Adobe Photoshop CC 2018 before 19.1.6 and Photoshop CC 2017 before 18.1.6 have a memory corruption vulnerability. Successful exploitation could lead to remote code execution.
References
| URL | Tags | ||
|---|---|---|---|
| psirt@adobe.com | http://www.securityfocus.com/bid/105123 | Third Party Advisory, VDB Entry | |
| psirt@adobe.com | http://www.securitytracker.com/id/1041599 | Third Party Advisory, VDB Entry | |
| psirt@adobe.com | https://helpx.adobe.com/security/products/photoshop/apsb18-28.html | Vendor Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | http://www.securityfocus.com/bid/105123 | Third Party Advisory, VDB Entry | |
| af854a3a-2127-422b-91ae-364da2661108 | http://www.securitytracker.com/id/1041599 | Third Party Advisory, VDB Entry | |
| af854a3a-2127-422b-91ae-364da2661108 | https://helpx.adobe.com/security/products/photoshop/apsb18-28.html | Vendor Advisory |
{
"affected": [
{
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"product": "Adobe Photoshop CC 2018 before 19.1.6 and Photoshop CC 2017 before 18.1.6",
"vendor": "n/a",
"versions": [
{
"status": "affected",
"version": "Adobe Photoshop CC 2018 before 19.1.6 and Photoshop CC 2017 before 18.1.6"
}
]
}
],
"source": "psirt@adobe.com"
}
],
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"descriptions": [
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"value": "Adobe Photoshop CC 2018 before 19.1.6 and Photoshop CC 2017 before 18.1.6 have a memory corruption vulnerability. Successful exploitation could lead to remote code execution."
},
{
"lang": "es",
"value": "Adobe Photoshop CC 2018 en versiones anteriores a la 19.1.6 y Photoshop CC 2017 en versiones anteriores a la 18.1.6 tienen una vulnerabilidad de corrupci\u00f3n de memoria. La explotaci\u00f3n con \u00e9xito de esta vulnerabilidad podr\u00eda conducir a la ejecuci\u00f3n remota de c\u00f3digo."
}
],
"id": "CVE-2018-12811",
"lastModified": "2026-06-17T01:38:21.127",
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"baseScore": 7.5,
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"published": "2018-08-29T13:29:00.887",
"references": [
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"vulnStatus": "Modified",
"weaknesses": [
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"value": "CWE-119"
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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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