CVE-2024-0654 (GCVE-0-2024-0654)
Vulnerability from cvelistv5 – Published: 2024-01-18 01:00 – Updated: 2025-06-02 15:04
VLAI
EPSS
VEX
Title
DeepFaceLab Util.py deserialization
Summary
A vulnerability, which was classified as problematic, was found in DeepFaceLab pretrained DF.wf.288res.384.92.72.22. Affected is an unknown function of the file mainscripts/Util.py. The manipulation leads to deserialization. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used. VDB-251382 is the identifier assigned to this vulnerability.
Severity
5.3 (Medium)
5.3 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-05-08 15:53 UTC
CWE
- CWE-502 - Deserialization
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.251382 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.251382 | signaturepermissions-required |
| https://github.com/bayuncao/vul-cve-4 | broken-link |
| https://github.com/bayuncao/vul-cve-4/blob/main/p… | broken-linkexploit |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | DeepFaceLab |
Affected:
pretrained DF.wf.288res.384.92.72.22
|
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"baseSeverity": "MEDIUM",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L",
"version": "3.1"
}
},
{
"cvssV3_0": {
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"vectorString": "CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L",
"version": "3.0"
}
},
{
"cvssV2_0": {
"baseScore": 4.3,
"vectorString": "AV:L/AC:L/Au:S/C:P/I:P/A:P",
"version": "2.0"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-502",
"description": "CWE-502 Deserialization",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2024-02-09T19:12:11.048Z",
"orgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"shortName": "VulDB"
},
"references": [
{
"tags": [
"vdb-entry",
"technical-description"
],
"url": "https://vuldb.com/?id.251382"
},
{
"tags": [
"signature",
"permissions-required"
],
"url": "https://vuldb.com/?ctiid.251382"
},
{
"tags": [
"broken-link"
],
"url": "https://github.com/bayuncao/vul-cve-4"
},
{
"tags": [
"broken-link",
"exploit"
],
"url": "https://github.com/bayuncao/vul-cve-4/blob/main/picture/1071705290840_.pic_hd.jpg"
}
],
"timeline": [
{
"lang": "en",
"time": "2024-01-17T00:00:00.000Z",
"value": "Advisory disclosed"
},
{
"lang": "en",
"time": "2024-01-17T00:00:00.000Z",
"value": "CVE reserved"
},
{
"lang": "en",
"time": "2024-01-17T01:00:00.000Z",
"value": "VulDB entry created"
},
{
"lang": "en",
"time": "2024-02-10T19:43:51.000Z",
"value": "VulDB entry last update"
}
],
"title": "DeepFaceLab Util.py deserialization"
}
},
"cveMetadata": {
"assignerOrgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"assignerShortName": "VulDB",
"cveId": "CVE-2024-0654",
"datePublished": "2024-01-18T01:00:07.149Z",
"dateReserved": "2024-01-17T14:26:16.294Z",
"dateUpdated": "2025-06-02T15:04:33.145Z",
"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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