CVE-2024-6943 (GCVE-0-2024-6943)
Vulnerability from cvelistv5 – Published: 2024-07-21 07:00 – Updated: 2024-08-01 21:45
VLAI
EPSS
VEX
Title
ZhongBangKeJi CRMEB CopyTaobaoServices.php downloadImage deserialization
Summary
A vulnerability has been found in ZhongBangKeJi CRMEB up to 5.4.0 and classified as critical. Affected by this vulnerability is the function downloadImage of the file app/services/product/product/CopyTaobaoServices.php. The manipulation leads to deserialization. The attack can be launched remotely. The exploit has been disclosed to the public and may be used. The identifier VDB-272065 was assigned to this vulnerability. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.
Severity
6.3 (Medium)
6.3 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-07-29 19:01 UTC
CWE
- CWE-502 - Deserialization
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.272065 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.272065 | signaturepermissions-required |
| https://vuldb.com/?submit.374394 | third-party-advisory |
| https://gist.github.com/J1rrY-learn/e15a1926a3b5a… | broken-linkexploit |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| ZhongBangKeJi | CRMEB |
Affected:
5.0
Affected: 5.1 Affected: 5.2 Affected: 5.3 Affected: 5.4 |
guessed |
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"dateUpdated": "2024-07-21T07:00:06.788Z",
"orgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"shortName": "VulDB"
},
"references": [
{
"name": "VDB-272065 | ZhongBangKeJi CRMEB CopyTaobaoServices.php downloadImage deserialization",
"tags": [
"vdb-entry",
"technical-description"
],
"url": "https://vuldb.com/?id.272065"
},
{
"name": "VDB-272065 | CTI Indicators (IOB, IOC, IOA)",
"tags": [
"signature",
"permissions-required"
],
"url": "https://vuldb.com/?ctiid.272065"
},
{
"name": "Submit #374394 | Xi\u0027an Zhongbang Network Technology Co. CRMEB open source mall system \u003c=5.4.0 phar Deserialization/RCE",
"tags": [
"third-party-advisory"
],
"url": "https://vuldb.com/?submit.374394"
},
{
"tags": [
"broken-link",
"exploit"
],
"url": "https://gist.github.com/J1rrY-learn/e15a1926a3b5a2b8805a15cb95eff1d7"
}
],
"timeline": [
{
"lang": "en",
"time": "2024-07-20T00:00:00.000Z",
"value": "Advisory disclosed"
},
{
"lang": "en",
"time": "2024-07-20T02:00:00.000Z",
"value": "VulDB entry created"
},
{
"lang": "en",
"time": "2024-07-20T12:04:36.000Z",
"value": "VulDB entry last update"
}
],
"title": "ZhongBangKeJi CRMEB CopyTaobaoServices.php downloadImage deserialization"
}
},
"cveMetadata": {
"assignerOrgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"assignerShortName": "VulDB",
"cveId": "CVE-2024-6943",
"datePublished": "2024-07-21T07:00:06.788Z",
"dateReserved": "2024-07-20T09:59:21.867Z",
"dateUpdated": "2024-08-01T21:45:38.359Z",
"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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