FKIE_CVE-2026-45804
Vulnerability from fkie_nvd - Published: 2026-07-15 17:16 - Updated: 2026-08-12 17:02
Severity
Summary
Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, Diffusers' DiffusionPipeline.from_pretrained flow can bypass the trust_remote_code guard because download() validates model_index.json and custom pipeline code before later loading from a cached folder that can change, allowing a Hub repository with custom .py pipeline code to execute through the custom pipeline flow without passing custom_pipeline or trust_remote_code=True. This issue is fixed in version 0.38.0.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| huggingface | diffusers | * |
{
"affected": [
{
"affectedData": [
{
"product": "diffusers",
"vendor": "huggingface",
"versions": [
{
"status": "affected",
"version": "\u003c 0.38.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:huggingface:diffusers:*:*:*:*:*:python:*:*",
"matchCriteriaId": "D7F2A2AE-D122-46BA-BD64-67DCF4C22677",
"versionEndExcluding": "0.38.0",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, Diffusers\u0027 DiffusionPipeline.from_pretrained flow can bypass the trust_remote_code guard because download() validates model_index.json and custom pipeline code before later loading from a cached folder that can change, allowing a Hub repository with custom .py pipeline code to execute through the custom pipeline flow without passing custom_pipeline or trust_remote_code=True. This issue is fixed in version 0.38.0."
}
],
"id": "CVE-2026-45804",
"lastModified": "2026-08-12T17:02:10.870",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 1.6,
"impactScore": 5.9,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-45804",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "no"
},
{
"technicalImpact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-07-15T17:27:27.543160Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-07-15T17:16:48.310",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch"
],
"url": "https://github.com/huggingface/diffusers/commit/a37f6f8394ac2a7ee8360c3abea811efe54512b1"
},
{
"source": "security-advisories@github.com",
"tags": [
"Issue Tracking",
"Exploit"
],
"url": "https://github.com/huggingface/diffusers/issues/13446"
},
{
"source": "security-advisories@github.com",
"tags": [
"Issue Tracking"
],
"url": "https://github.com/huggingface/diffusers/pull/13448"
},
{
"source": "security-advisories@github.com",
"tags": [
"Release Notes"
],
"url": "https://github.com/huggingface/diffusers/releases/tag/v0.38.0"
},
{
"source": "security-advisories@github.com",
"tags": [
"Vendor Advisory",
"Exploit"
],
"url": "https://github.com/huggingface/diffusers/security/advisories/GHSA-7wx4-6vff-v64p"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"tags": [
"Issue Tracking",
"Exploit"
],
"url": "https://github.com/huggingface/diffusers/issues/13446"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"tags": [
"Vendor Advisory",
"Exploit"
],
"url": "https://github.com/huggingface/diffusers/security/advisories/GHSA-7wx4-6vff-v64p"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-367"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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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.