GSD-2023-22240
Vulnerability from gsd - Updated: 2023-12-13 01:20Details
Adobe Acrobat Reader versions 22.003.20282 (and earlier), 22.003.20281 (and earlier) and 20.005.30418 (and earlier) are affected by an out-of-bounds write vulnerability that could result in arbitrary code execution in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2023-22240",
"id": "GSD-2023-22240"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2023-22240"
],
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"id": "GSD-2023-22240",
"modified": "2023-12-13T01:20:43.135574Z",
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}
},
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"DATE_PUBLIC": "2023-01-10T23:00:00.000Z",
"ID": "CVE-2023-22240",
"STATE": "PUBLIC",
"TITLE": "ZDI-CAN-19517: Adobe Acrobat Reader DC AcroForm Annotation Out-Of-Bounds Write Remote Code Execution Vulnerability"
},
"affects": {
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}
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},
"impact": {
"cvss": {
"attackComplexity": "Low",
"attackVector": "Local",
"availabilityImpact": "High",
"baseScore": 7.8,
"baseSeverity": "High",
"confidentialityImpact": "High",
"integrityImpact": "High",
"privilegesRequired": "None",
"scope": "Unchanged",
"userInteraction": "Required",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
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"value": "Out-of-bounds Write (CWE-787)"
}
]
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"name": "https://helpx.adobe.com/security/products/acrobat/apsb23-01.html",
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"url": "https://helpx.adobe.com/security/products/acrobat/apsb23-01.html"
}
]
},
"source": {
"discovery": "EXTERNAL"
}
},
"nvd.nist.gov": {
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"impact": {
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"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
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"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
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"exploitabilityScore": 1.8,
"impactScore": 5.9
}
},
"lastModifiedDate": "2023-02-06T18:38Z",
"publishedDate": "2023-01-27T18:15Z"
}
}
}
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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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