GSD-2023-2141
Vulnerability from gsd - Updated: 2023-12-13 01:20Details
An unsafe .NET object deserialization in DELMIA Apriso Release 2017 through Release 2022 could lead to post-authentication remote code execution.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2023-2141",
"id": "GSD-2023-2141"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2023-2141"
],
"details": "An unsafe .NET object deserialization in DELMIA Apriso Release 2017 through Release 2022 could lead to post-authentication remote code execution.\n",
"id": "GSD-2023-2141",
"modified": "2023-12-13T01:20:32.448138Z",
"schema_version": "1.4.0"
}
},
"namespaces": {
"cve.org": {
"CVE_data_meta": {
"ASSIGNER": "3DS.Information-Security@3ds.com",
"ID": "CVE-2023-2141",
"STATE": "PUBLIC"
},
"affects": {
"vendor": {
"vendor_data": [
{
"product": {
"product_data": [
{
"product_name": "DELMIA Apriso",
"version": {
"version_data": [
{
"version_affected": "\u003c=",
"version_name": "Apriso 2017 Golden",
"version_value": "Apriso 2017 SP7"
},
{
"version_affected": "\u003c=",
"version_name": "Apriso 2018 Golden",
"version_value": "Apriso 2018 SP4"
},
{
"version_affected": "\u003c=",
"version_name": "Apriso 2019 Golden",
"version_value": "Apriso 2019 SP5"
},
{
"version_affected": "\u003c=",
"version_name": "Apriso 2020 Golden",
"version_value": "Apriso 2020 SP4"
},
{
"version_affected": "\u003c=",
"version_name": "Apriso 2021 Golden",
"version_value": "Apriso 2021 SP2"
},
{
"version_affected": "=",
"version_value": "Apriso 2022 Golden"
}
]
}
}
]
},
"vendor_name": "Dassault Syst\u00e8mes"
}
]
}
},
"credits": [
{
"lang": "en",
"value": "Mehdi Elyassa and Vincent Herbulot from Synacktiv"
}
],
"data_format": "MITRE",
"data_type": "CVE",
"data_version": "4.0",
"description": {
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{
"lang": "eng",
"value": "An unsafe .NET object deserialization in DELMIA Apriso Release 2017 through Release 2022 could lead to post-authentication remote code execution.\n"
}
]
},
"generator": {
"engine": "Vulnogram 0.1.0-dev"
},
"impact": {
"cvss": [
{
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:H/A:H",
"version": "3.1"
}
]
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"cweId": "CWE-502",
"lang": "eng",
"value": "CWE-502 Deserialization of Untrusted Data"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://www.3ds.com/vulnerability/advisories",
"refsource": "MISC",
"url": "https://www.3ds.com/vulnerability/advisories"
}
]
},
"source": {
"discovery": "EXTERNAL"
}
},
"nvd.nist.gov": {
"configurations": {
"CVE_data_version": "4.0",
"nodes": [
{
"children": [],
"cpe_match": [
{
"cpe23Uri": "cpe:2.3:a:3ds:delmia_apriso:*:*:*:*:*:*:*:*",
"cpe_name": [],
"versionEndIncluding": "2022",
"versionStartIncluding": "2017",
"vulnerable": true
}
],
"operator": "OR"
}
]
},
"cve": {
"CVE_data_meta": {
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"ID": "CVE-2023-2141"
},
"data_format": "MITRE",
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"description": {
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"lang": "en",
"value": "An unsafe .NET object deserialization in DELMIA Apriso Release 2017 through Release 2022 could lead to post-authentication remote code execution.\n"
}
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"problemtype": {
"problemtype_data": [
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"references": {
"reference_data": [
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"refsource": "MISC",
"tags": [
"Vendor Advisory"
],
"url": "https://www.3ds.com/vulnerability/advisories"
}
]
}
},
"impact": {
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 5.9
}
},
"lastModifiedDate": "2023-05-09T01:01Z",
"publishedDate": "2023-04-21T16:15Z"
}
}
}
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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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