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CVE-2026-44513 (GCVE-0-2026-44513)

Vulnerability from cvelistv5 – Published: 2026-05-14 16:26 – Updated: 2026-08-28 12:04
VLAI
Title
Diffusers: `trust_remote_code` bypass via `custom_pipeline` and local custom components
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.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-14 17:38 UTC
CWE
  • CWE-94 - Improper Control of Generation of Code ('Code Injection')
  • CWE-358 - Improperly Implemented Security Check for Standard
Impacted products
Vendor Product Version CPE status
huggingface diffusers Affected: < 0.38.0
guessed Create a notification for this product.
Red Hat Red Hat OpenShift AI 3.4 Unaffected: 1787077779 , < * (rpm)
    cpe:/a:redhat:openshift_ai:3.4::el9
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Red Hat Red Hat OpenShift AI 3.4 Unaffected: 1787076481 , < * (rpm)
    cpe:/a:redhat:openshift_ai:3.4::el9
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Red Hat Red Hat OpenShift AI 3.4 Unaffected: 1786611803 , < * (rpm)
    cpe:/a:redhat:openshift_ai:3.4::el9
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Red Hat Red Hat OpenShift AI 3.4 Unaffected: 1786611435 , < * (rpm)
    cpe:/a:redhat:openshift_ai:3.4::el9
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Red Hat Red Hat AI Inference Server     cpe:/a:redhat:ai_inference_server:3
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Red Hat Red Hat Enterprise Linux AI (RHEL AI) 3     cpe:/a:redhat:enterprise_linux_ai:3
Create a notification for this product.
Red Hat Red Hat OpenShift AI (RHOAI)     cpe:/a:redhat:openshift_ai
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Show details on NVD website

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      "current_release_date": "2026-08-27T15:14:26+00:00",
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      "product_status:fixed": "5",
      "product_status:known_affected": "9",
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      "source": "Red Hat CSAF VEX",
      "status": "final",
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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\"}}}",
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      "dataType": "CVE_RECORD",
      "dataVersion": "5.2"
    }
  }
}



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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.

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  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
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