CVE-2026-44513 (GCVE-0-2026-44513)
Vulnerability from cvelistv5
Published
2026-05-14 16:26
Modified
2026-08-28 12:04
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-94 - Improper Control of Generation of Code ('Code Injection')
Summary
Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, a trust_remote_code bypass in DiffusionPipeline.from_pretrained allows arbitrary remote code execution despite the user passing trust_remote_code=False (or omitting it, which is the default). The vulnerability has three variants, all sharing the same root cause — the trust_remote_code gate was implemented inside DiffusionPipeline.download() rather than at the actual dynamic-module load site, so any code path that bypassed or short-circuited download() also bypassed the security check. DiffusionPipeline.from_pretrained('repoA', custom_pipeline='attacker/repoB', trust_remote_code=False) — the gate evaluated against repoA's file list rather than repoB's, so repoB's pipeline.py was loaded and executed. DiffusionPipeline.from_pretrained('/local/snapshot', custom_pipeline='attacker/repoB', trust_remote_code=False) — the local-path branch never invoked download(), so the gate was never reached and remote code from repoB executed. DiffusionPipeline.from_pretrained('/local/snapshot', trust_remote_code=False) where the snapshot contains custom component files (e.g. unet/my_unet_model.py) referenced from model_index.json — same root cause; the local path skipped download() and custom component code executed. This vulnerability is fixed in 0.38.0.
References
| URL | Tags | ||||
|---|---|---|---|---|---|
|
|||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| huggingface | diffusers |
Version: < 0.38.0 |
{
"containers": {
"adp": [
{
"metrics": [
{
"other": {
"content": {
"id": "CVE-2026-44513",
"options": [
{
"Exploitation": "none"
},
{
"Automatable": "no"
},
{
"Technical Impact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-05-14T17:38:51.150920Z",
"version": "2.0.3"
},
"type": "ssvc"
}
}
],
"providerMetadata": {
"dateUpdated": "2026-05-14T19:51:06.991Z",
"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"shortName": "CISA-ADP"
},
"title": "CISA ADP Vulnrichment"
},
{
"affected": [
{
"collectionURL": "https://catalog.redhat.com/software/containers/",
"cpes": [
"cpe:/a:redhat:openshift_ai:3.4::el9"
],
"defaultStatus": "affected",
"packageName": "rhoai/odh-th06-cuda130-torch210-py312-rhel9",
"product": "Red Hat OpenShift AI 3.4",
"vendor": "Red Hat",
"versions": [
{
"lessThan": "*",
"status": "unaffected",
"version": "1787077779",
"versionType": "rpm"
}
]
},
{
"collectionURL": "https://catalog.redhat.com/software/containers/",
"cpes": [
"cpe:/a:redhat:openshift_ai:3.4::el9"
],
"defaultStatus": "affected",
"packageName": "rhoai/odh-th06-rocm64-torch291-py312-rhel9",
"product": "Red Hat OpenShift AI 3.4",
"vendor": "Red Hat",
"versions": [
{
"lessThan": "*",
"status": "unaffected",
"version": "1787076481",
"versionType": "rpm"
}
]
},
{
"collectionURL": "https://catalog.redhat.com/software/containers/",
"cpes": [
"cpe:/a:redhat:openshift_ai:3.4::el9"
],
"defaultStatus": "affected",
"packageName": "rhoai/odh-training-cuda128-torch29-py312-rhel9",
"product": "Red Hat OpenShift AI 3.4",
"vendor": "Red Hat",
"versions": [
{
"lessThan": "*",
"status": "unaffected",
"version": "1786611803",
"versionType": "rpm"
}
]
},
{
"collectionURL": "https://catalog.redhat.com/software/containers/",
"cpes": [
"cpe:/a:redhat:openshift_ai:3.4::el9"
],
"defaultStatus": "affected",
"packageName": "rhoai/odh-training-rocm64-torch29-py312-rhel9",
"product": "Red Hat OpenShift AI 3.4",
"vendor": "Red Hat",
"versions": [
{
"lessThan": "*",
"status": "unaffected",
"version": "1786611435",
"versionType": "rpm"
}
]
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:ai_inference_server:3"
],
"defaultStatus": "affected",
"packageName": "rhaiis/vllm-cpu-rhel9",
"product": "Red Hat AI Inference Server",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:ai_inference_server:3"
],
"defaultStatus": "unaffected",
"packageName": "rhaiis/vllm-cuda-rhel9",
"product": "Red Hat AI Inference Server",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:ai_inference_server:3"
],
"defaultStatus": "unaffected",
"packageName": "rhaiis/vllm-rocm-rhel9",
"product": "Red Hat AI Inference Server",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:ai_inference_server:3"
],
"defaultStatus": "affected",
"packageName": "rhaiis/vllm-tpu-rhel9",
"product": "Red Hat AI Inference Server",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:ai_inference_server:3"
],
"defaultStatus": "affected",
"packageName": "rhaii/vllm-cpu-rhel9",
"product": "Red Hat AI Inference Server",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:ai_inference_server:3"
],
"defaultStatus": "affected",
"packageName": "rhaii/vllm-cuda-rhel9",
"product": "Red Hat AI Inference Server",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:enterprise_linux_ai:3"
],
"defaultStatus": "affected",
"packageName": "rhelai3/bootc-aws-cuda-rhel9",
"product": "Red Hat Enterprise Linux AI (RHEL AI) 3",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:enterprise_linux_ai:3"
],
"defaultStatus": "affected",
"packageName": "rhelai3/bootc-azure-cuda-rhel9",
"product": "Red Hat Enterprise Linux AI (RHEL AI) 3",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:enterprise_linux_ai:3"
],
"defaultStatus": "affected",
"packageName": "rhelai3/bootc-cuda-rhel9",
"product": "Red Hat Enterprise Linux AI (RHEL AI) 3",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:enterprise_linux_ai:3"
],
"defaultStatus": "affected",
"packageName": "rhelai3/bootc-gcp-cuda-rhel9",
"product": "Red Hat Enterprise Linux AI (RHEL AI) 3",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "affected",
"packageName": "rhoai/odh-openvino-model-server-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-th06-cuda130-torch291-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
}
],
"datePublic": "2026-05-14T16:26:03.907Z",
"descriptions": [
{
"lang": "en",
"value": "A flaw was found in Diffusers, a library for pretrained diffusion models. A remote attacker could exploit a bypass in the `trust_remote_code` mechanism within the `DiffusionPipeline.from_pretrained` function. This vulnerability allows for arbitrary remote code execution, even when the user explicitly sets `trust_remote_code=False` or omits it. The issue stems from the security check being incorrectly placed, allowing malicious code to be loaded and executed by bypassing the intended security gate."
}
],
"metrics": [
{
"other": {
"content": {
"namespace": "https://access.redhat.com/security/updates/classification/",
"value": "Important"
},
"type": "Red Hat severity rating"
}
},
{
"cvssV3_1": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"format": "CVSS"
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-358",
"description": "Improperly Implemented Security Check for Standard",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-08-28T12:04:31.466Z",
"orgId": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
"shortName": "redhat-SADP"
},
"references": [
{
"tags": [
"vdb-entry",
"x_refsource_REDHAT"
],
"url": "https://access.redhat.com/security/cve/CVE-2026-44513"
},
{
"name": "RHBZ#2477507",
"tags": [
"issue-tracking",
"x_refsource_REDHAT"
],
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2477507"
},
{
"tags": [
"x_sadp-csaf-vex"
],
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-44513.json"
},
{
"tags": [
"vendor-advisory",
"x_refsource_REDHAT"
],
"url": "https://access.redhat.com/errata/RHSA-2026:60520"
}
],
"solutions": [
{
"lang": "en",
"value": "RHSA-2026:60520: Red Hat OpenShift AI 3.4"
}
],
"timeline": [
{
"lang": "en",
"time": "2026-05-14T17:02:10.040Z",
"value": "Reported to Red Hat."
},
{
"lang": "en",
"time": "2026-05-14T16:26:03.907Z",
"value": "Made public."
}
],
"title": "Diffusers: Diffusers: Arbitrary remote code execution via `trust_remote_code` bypass",
"workarounds": [
{
"lang": "en",
"value": "Use DiffusionPipeline.from_pretrained() only with model paths, custom pipelines,\nand local snapshots from fully trusted and audited sources. Avoid setting\ncustom_pipeline= to a Hub repository that differs from the primary model path\nunless its pipeline.py has been manually reviewed. When loading a local snapshot, check for unexpected *.py files at the snapshot root and under component subdirectories (unet/, scheduler/, etc.) before calling from_pretrained.\n\nThe only complete fix is upgrading to diffusers 0.38.0."
}
],
"x_adpType": "supplier",
"x_generator": {
"engine": "sadp-cli 1.0.0"
}
}
],
"cna": {
"affected": [
{
"product": "diffusers",
"vendor": "huggingface",
"versions": [
{
"status": "affected",
"version": "\u003c 0.38.0"
}
]
}
],
"descriptions": [
{
"lang": "en",
"value": "Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, a trust_remote_code bypass in DiffusionPipeline.from_pretrained allows arbitrary remote code execution despite the user passing trust_remote_code=False (or omitting it, which is the default). The vulnerability has three variants, all sharing the same root cause \u2014 the trust_remote_code gate was implemented inside DiffusionPipeline.download() rather than at the actual dynamic-module load site, so any code path that bypassed or short-circuited download() also bypassed the security check. DiffusionPipeline.from_pretrained(\u0027repoA\u0027, custom_pipeline=\u0027attacker/repoB\u0027, trust_remote_code=False) \u2014 the gate evaluated against repoA\u0027s file list rather than repoB\u0027s, so repoB\u0027s pipeline.py was loaded and executed. DiffusionPipeline.from_pretrained(\u0027/local/snapshot\u0027, custom_pipeline=\u0027attacker/repoB\u0027, trust_remote_code=False) \u2014 the local-path branch never invoked download(), so the gate was never reached and remote code from repoB executed. DiffusionPipeline.from_pretrained(\u0027/local/snapshot\u0027, trust_remote_code=False) where the snapshot contains custom component files (e.g. unet/my_unet_model.py) referenced from model_index.json \u2014 same root cause; the local path skipped download() and custom component code executed. This vulnerability is fixed in 0.38.0."
}
],
"metrics": [
{
"cvssV3_1": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-94",
"description": "CWE-94: Improper Control of Generation of Code (\u0027Code Injection\u0027)",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-05-14T16:26:03.907Z",
"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"shortName": "GitHub_M"
},
"references": [
{
"name": "https://github.com/huggingface/diffusers/security/advisories/GHSA-98h9-4798-4q5v",
"tags": [
"x_refsource_CONFIRM"
],
"url": "https://github.com/huggingface/diffusers/security/advisories/GHSA-98h9-4798-4q5v"
}
],
"source": {
"advisory": "GHSA-98h9-4798-4q5v",
"discovery": "UNKNOWN"
},
"title": "Diffusers: `trust_remote_code` bypass via `custom_pipeline` and local custom components"
}
},
"cveMetadata": {
"assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"assignerShortName": "GitHub_M",
"cveId": "CVE-2026-44513",
"datePublished": "2026-05-14T16:26:03.907Z",
"dateReserved": "2026-05-06T18:28:20.887Z",
"dateUpdated": "2026-08-28T12:04:31.466Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2",
"vulnerability-lookup:meta": {
"vulnrichment": {
"containers": "{\"adp\": [{\"title\": \"Diffusers: Diffusers: Arbitrary remote code execution via `trust_remote_code` bypass\", \"metrics\": [{\"other\": {\"type\": \"Red Hat severity rating\", \"content\": {\"value\": \"Important\", \"namespace\": \"https://access.redhat.com/security/updates/classification/\"}}}, {\"format\": \"CVSS\", \"cvssV3_1\": {\"scope\": \"UNCHANGED\", \"version\": \"3.1\", \"baseScore\": 8.8, \"attackVector\": \"NETWORK\", \"baseSeverity\": \"HIGH\", \"vectorString\": \"CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H\", \"integrityImpact\": \"HIGH\", \"userInteraction\": \"REQUIRED\", \"attackComplexity\": \"LOW\", \"availabilityImpact\": \"HIGH\", \"privilegesRequired\": \"NONE\", \"confidentialityImpact\": \"HIGH\"}}], \"affected\": [{\"cpes\": [\"cpe:/a:redhat:openshift_ai:3.4::el9\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat OpenShift AI 3.4\", \"versions\": [{\"status\": \"unaffected\", \"version\": \"1787077779\", \"lessThan\": \"*\", \"versionType\": \"rpm\"}], \"packageName\": \"rhoai/odh-th06-cuda130-torch210-py312-rhel9\", \"collectionURL\": \"https://catalog.redhat.com/software/containers/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:openshift_ai:3.4::el9\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat OpenShift AI 3.4\", \"versions\": [{\"status\": \"unaffected\", \"version\": \"1787076481\", \"lessThan\": \"*\", \"versionType\": \"rpm\"}], \"packageName\": \"rhoai/odh-th06-rocm64-torch291-py312-rhel9\", \"collectionURL\": \"https://catalog.redhat.com/software/containers/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:openshift_ai:3.4::el9\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat OpenShift AI 3.4\", \"versions\": [{\"status\": \"unaffected\", \"version\": \"1786611803\", \"lessThan\": \"*\", \"versionType\": \"rpm\"}], \"packageName\": \"rhoai/odh-training-cuda128-torch29-py312-rhel9\", \"collectionURL\": \"https://catalog.redhat.com/software/containers/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:openshift_ai:3.4::el9\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat OpenShift AI 3.4\", \"versions\": [{\"status\": \"unaffected\", \"version\": \"1786611435\", \"lessThan\": \"*\", \"versionType\": \"rpm\"}], \"packageName\": \"rhoai/odh-training-rocm64-torch29-py312-rhel9\", \"collectionURL\": \"https://catalog.redhat.com/software/containers/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:ai_inference_server:3\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat AI Inference Server\", \"packageName\": \"rhaiis/vllm-cpu-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:ai_inference_server:3\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat AI Inference Server\", \"packageName\": \"rhaiis/vllm-cuda-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"unaffected\"}, {\"cpes\": [\"cpe:/a:redhat:ai_inference_server:3\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat AI Inference Server\", \"packageName\": \"rhaiis/vllm-rocm-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"unaffected\"}, {\"cpes\": [\"cpe:/a:redhat:ai_inference_server:3\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat AI Inference Server\", \"packageName\": \"rhaiis/vllm-tpu-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:ai_inference_server:3\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat AI Inference Server\", \"packageName\": \"rhaii/vllm-cpu-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:ai_inference_server:3\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat AI Inference Server\", \"packageName\": \"rhaii/vllm-cuda-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:enterprise_linux_ai:3\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat Enterprise Linux AI (RHEL AI) 3\", \"packageName\": \"rhelai3/bootc-aws-cuda-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:enterprise_linux_ai:3\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat Enterprise Linux AI (RHEL AI) 3\", \"packageName\": \"rhelai3/bootc-azure-cuda-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:enterprise_linux_ai:3\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat Enterprise Linux AI (RHEL AI) 3\", \"packageName\": \"rhelai3/bootc-cuda-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:enterprise_linux_ai:3\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat Enterprise Linux AI (RHEL AI) 3\", \"packageName\": \"rhelai3/bootc-gcp-cuda-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:openshift_ai\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat OpenShift AI (RHOAI)\", \"packageName\": \"rhoai/odh-openvino-model-server-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"affected\"}, {\"cpes\": [\"cpe:/a:redhat:openshift_ai\"], \"vendor\": \"Red Hat\", \"product\": \"Red Hat OpenShift AI (RHOAI)\", \"packageName\": \"rhoai/odh-th06-cuda130-torch291-py312-rhel9\", \"collectionURL\": \"https://access.redhat.com/downloads/content/package-browser/\", \"defaultStatus\": \"unaffected\"}], \"timeline\": [{\"lang\": \"en\", \"time\": \"2026-05-14T17:02:10.040Z\", \"value\": \"Reported to Red Hat.\"}, {\"lang\": \"en\", \"time\": \"2026-05-14T16:26:03.907Z\", \"value\": \"Made public.\"}], \"solutions\": [{\"lang\": \"en\", \"value\": \"RHSA-2026:60520: Red Hat OpenShift AI 3.4\"}], \"x_adpType\": \"supplier\", \"datePublic\": \"2026-05-14T16:26:03.907Z\", \"references\": [{\"url\": \"https://access.redhat.com/security/cve/CVE-2026-44513\", \"tags\": [\"vdb-entry\", \"x_refsource_REDHAT\"]}, {\"url\": \"https://bugzilla.redhat.com/show_bug.cgi?id=2477507\", \"name\": \"RHBZ#2477507\", \"tags\": [\"issue-tracking\", \"x_refsource_REDHAT\"]}, {\"url\": \"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-44513.json\", \"tags\": [\"x_sadp-csaf-vex\"]}, {\"url\": \"https://access.redhat.com/errata/RHSA-2026:60520\", \"tags\": [\"vendor-advisory\", \"x_refsource_REDHAT\"]}], \"workarounds\": [{\"lang\": \"en\", \"value\": \"Use DiffusionPipeline.from_pretrained() only with model paths, custom pipelines,\\nand local snapshots from fully trusted and audited sources. Avoid setting\\ncustom_pipeline= to a Hub repository that differs from the primary model path\\nunless its pipeline.py has been manually reviewed. When loading a local snapshot, check for unexpected *.py files at the snapshot root and under component subdirectories (unet/, scheduler/, etc.) before calling from_pretrained.\\n\\nThe only complete fix is upgrading to diffusers 0.38.0.\"}], \"x_generator\": {\"engine\": \"sadp-cli 1.0.0\"}, \"descriptions\": [{\"lang\": \"en\", \"value\": \"A flaw was found in Diffusers, a library for pretrained diffusion models. A remote attacker could exploit a bypass in the `trust_remote_code` mechanism within the `DiffusionPipeline.from_pretrained` function. This vulnerability allows for arbitrary remote code execution, even when the user explicitly sets `trust_remote_code=False` or omits it. The issue stems from the security check being incorrectly placed, allowing malicious code to be loaded and executed by bypassing the intended security gate.\"}], \"problemTypes\": [{\"descriptions\": [{\"lang\": \"en\", \"type\": \"CWE\", \"cweId\": \"CWE-358\", \"description\": \"Improperly Implemented Security Check for Standard\"}]}], \"providerMetadata\": {\"orgId\": \"0b0ca135-0b70-47e7-9f44-1890c2a1c46c\", \"shortName\": \"redhat-SADP\", \"dateUpdated\": \"2026-08-28T12:04:31.466Z\"}}, {\"title\": \"CISA ADP Vulnrichment\", \"metrics\": [{\"other\": {\"type\": \"ssvc\", \"content\": {\"id\": \"CVE-2026-44513\", \"role\": \"CISA Coordinator\", \"options\": [{\"Exploitation\": \"none\"}, {\"Automatable\": \"no\"}, {\"Technical Impact\": \"total\"}], \"version\": \"2.0.3\", \"timestamp\": \"2026-05-14T17:38:51.150920Z\"}}}], \"providerMetadata\": {\"orgId\": \"134c704f-9b21-4f2e-91b3-4a467353bcc0\", \"shortName\": \"CISA-ADP\", \"dateUpdated\": \"2026-05-14T17:38:55.958Z\"}}], \"cna\": {\"title\": \"Diffusers: `trust_remote_code` bypass via `custom_pipeline` and local custom components\", \"source\": {\"advisory\": \"GHSA-98h9-4798-4q5v\", \"discovery\": \"UNKNOWN\"}, \"metrics\": [{\"cvssV3_1\": {\"scope\": \"UNCHANGED\", \"version\": \"3.1\", \"baseScore\": 8.8, \"attackVector\": \"NETWORK\", \"baseSeverity\": \"HIGH\", \"vectorString\": \"CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H\", \"integrityImpact\": \"HIGH\", \"userInteraction\": \"REQUIRED\", \"attackComplexity\": \"LOW\", \"availabilityImpact\": \"HIGH\", \"privilegesRequired\": \"NONE\", \"confidentialityImpact\": \"HIGH\"}}], \"affected\": [{\"vendor\": \"huggingface\", \"product\": \"diffusers\", \"versions\": [{\"status\": \"affected\", \"version\": \"\u003c 0.38.0\"}]}], \"references\": [{\"url\": \"https://github.com/huggingface/diffusers/security/advisories/GHSA-98h9-4798-4q5v\", \"name\": \"https://github.com/huggingface/diffusers/security/advisories/GHSA-98h9-4798-4q5v\", \"tags\": [\"x_refsource_CONFIRM\"]}], \"descriptions\": [{\"lang\": \"en\", \"value\": \"Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, a trust_remote_code bypass in DiffusionPipeline.from_pretrained allows arbitrary remote code execution despite the user passing trust_remote_code=False (or omitting it, which is the default). The vulnerability has three variants, all sharing the same root cause \\u2014 the trust_remote_code gate was implemented inside DiffusionPipeline.download() rather than at the actual dynamic-module load site, so any code path that bypassed or short-circuited download() also bypassed the security check. DiffusionPipeline.from_pretrained(\u0027repoA\u0027, custom_pipeline=\u0027attacker/repoB\u0027, trust_remote_code=False) \\u2014 the gate evaluated against repoA\u0027s file list rather than repoB\u0027s, so repoB\u0027s pipeline.py was loaded and executed. DiffusionPipeline.from_pretrained(\u0027/local/snapshot\u0027, custom_pipeline=\u0027attacker/repoB\u0027, trust_remote_code=False) \\u2014 the local-path branch never invoked download(), so the gate was never reached and remote code from repoB executed. DiffusionPipeline.from_pretrained(\u0027/local/snapshot\u0027, trust_remote_code=False) where the snapshot contains custom component files (e.g. unet/my_unet_model.py) referenced from model_index.json \\u2014 same root cause; the local path skipped download() and custom component code executed. This vulnerability is fixed in 0.38.0.\"}], \"problemTypes\": [{\"descriptions\": [{\"lang\": \"en\", \"type\": \"CWE\", \"cweId\": \"CWE-94\", \"description\": \"CWE-94: Improper Control of Generation of Code (\u0027Code Injection\u0027)\"}]}], \"providerMetadata\": {\"orgId\": \"a0819718-46f1-4df5-94e2-005712e83aaa\", \"shortName\": \"GitHub_M\", \"dateUpdated\": \"2026-05-14T16:26:03.907Z\"}}}",
"cveMetadata": "{\"cveId\": \"CVE-2026-44513\", \"state\": \"PUBLISHED\", \"dateUpdated\": \"2026-08-28T12:04:31.466Z\", \"dateReserved\": \"2026-05-06T18:28:20.887Z\", \"assignerOrgId\": \"a0819718-46f1-4df5-94e2-005712e83aaa\", \"datePublished\": \"2026-05-14T16:26:03.907Z\", \"assignerShortName\": \"GitHub_M\"}",
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
Loading…
Loading…
Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
- Confirmed: The vulnerability is confirmed from an analyst perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
- Patched: This vulnerability was successfully patched by the user reporting the sighting.
- Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
- Not confirmed: The user expresses doubt about the veracity of the vulnerability.
- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.
Loading…
Loading…