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4 vulnerabilities found for diffusers by huggingface
CVE-2026-65920 (GCVE-0-2026-65920)
Vulnerability from cvelistv5
Published
2026-07-23 17:43
Modified
2026-07-24 21:35
Severity ?
5.3 (Medium) - CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N
4.3 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:N/A:N
4.3 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:N/A:N
VLAI Severity ?
EPSS score ?
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Summary
Diffusers through 0.39.0, fixed in commit cee298c, contains a path traversal vulnerability in the _get_checkpoint_shard_files function that allows attackers to read arbitrary files by supplying malicious weight_map values in model index JSON. Attackers can use ../ sequences or absolute paths in weight_map entries to escape the model directory and read safetensors files outside the intended location during model loading.
References
| URL | Tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| huggingface | diffusers |
Version: 0 ≤ 0.39.0 |
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CVE-2026-45804 (GCVE-0-2026-45804)
Vulnerability from cvelistv5
Published
2026-07-15 16:05
Modified
2026-07-15 17:27
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-367 - Time-of-check Time-of-use (TOCTOU) Race Condition
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
| URL | Tags | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|||||||||||||||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| huggingface | diffusers |
Version: < 0.38.0 |
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CVE-2026-44827 (GCVE-0-2026-44827)
Vulnerability from cvelistv5
Published
2026-05-14 16:33
Modified
2026-05-14 18:03
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, diffusers 0.37.0 allows remote code execution without the trust_remote_code=True safeguard when loading pipelines from Hugging Face Hub repositories. The _resolve_custom_pipeline_and_cls function in pipeline_loading_utils.py performs string interpolation on the custom_pipeline parameter using f"{custom_pipeline}.py". When custom_pipeline is not supplied by the user, it defaults to None, which Python interpolates as the literal string "None.py". If an attacker publishes a Hub repository containing a file named None.py with a class that subclasses DiffusionPipeline, the file is automatically downloaded and executed during a standard DiffusionPipeline.from_pretrained() call with no additional keyword arguments. The trust_remote_code check in DiffusionPipeline.download() is bypassed because it evaluates custom_pipeline is not None as False (since the kwarg was never supplied), while the downstream code path that actually loads the module resolves the None value into a valid filename. An attacker can achieve silent arbitrary code execution by publishing a malicious model repository with a None.py file and a standard-looking model_index.json that references a legitimate pipeline class name, requiring only that a victim calls from_pretrained on the repository. This vulnerability is fixed in 0.38.0.
References
| URL | Tags | ||||
|---|---|---|---|---|---|
|
|||||
Impacted products
| Vendor | Product | Version | ||
|---|---|---|---|---|
| huggingface | diffusers |
Version: < 0.38.0 |
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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 |
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"title": "Diffusers: Diffusers: Arbitrary remote code execution via `trust_remote_code` bypass",
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"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."
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