CVE-2022-24093 (GCVE-0-2022-24093)
Vulnerability from cvelistv5 – Published: 2023-09-12 07:36 – Updated: 2025-02-27 20:56
VLAI
EPSS
VEX
Title
Adobe Commerce post-auth improper input validation leads to remote code execution
Summary
Adobe Commerce versions 2.4.3-p1 (and earlier) and 2.3.7-p2 (and earlier) are affected by an improper input validation vulnerability. Exploitation of this issue does not require user interaction and could result in a post-authentication arbitrary code execution.
Severity
9.1 (Critical)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-02-26 21:51 UTC
CWE
- CWE-20 - Improper Input Validation (CWE-20)
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://helpx.adobe.com/security/products/magento… | vendor-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Adobe | Adobe Commerce |
Affected:
0 , ≤ 2.3.7-p2
(semver)
|
guessed |
Date Public
2022-04-12 17:00
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"vectorString": "CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:H",
"version": "3.1"
},
"format": "CVSS",
"scenarios": [
{
"lang": "en",
"value": "GENERAL"
}
]
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-20",
"description": "Improper Input Validation (CWE-20)",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2023-09-12T07:36:03.118Z",
"orgId": "078d4453-3bcd-4900-85e6-15281da43538",
"shortName": "adobe"
},
"references": [
{
"tags": [
"vendor-advisory"
],
"url": "https://helpx.adobe.com/security/products/magento/apsb22-13.html"
}
],
"source": {
"discovery": "EXTERNAL"
},
"title": "Adobe Commerce post-auth improper input validation leads to remote code execution"
}
},
"cveMetadata": {
"assignerOrgId": "078d4453-3bcd-4900-85e6-15281da43538",
"assignerShortName": "adobe",
"cveId": "CVE-2022-24093",
"datePublished": "2023-09-12T07:36:03.118Z",
"dateReserved": "2022-01-27T20:47:58.755Z",
"dateUpdated": "2025-02-27T20:56:18.692Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
}
}
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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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