CVE-2025-14928
Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.
NVD · uneditedHugging Face Transformers HuBERT convert_config Code Injection Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Transformers. User interaction is required to exploit this vulnerability in that the target must convert a malicious checkpoint. The specific flaw exists within the convert_config function. The issue results from the lack of proper validation of a user-supplied string before using it to execute Python code. An attacker can leverage this vulnerability to execute code in the context of the current user. Was ZDI-CAN-28253.
Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.
dbcve analysis · moderate confidenceA code injection vulnerability exists in Hugging Face Transformers' HuBERT convert_config function where user-supplied strings from malicious model checkpoints are executed as Python code without validation, allowing arbitrary code execution in the context of the current user.
Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.
Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.
NVD · CPE data= 4.57.0CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.
From the vector- Attack vector
- Local
- Complexity
- Low
- Privileges
- None
- User interaction
- Required
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.
dbcve checksWork through these to decide whether this CVE applies to you.
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Check installed Transformers versionRun: pip show transformers | grep Version or import transformers; print(transformers.__version__)Affected if Version is exactly 4.57.0
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Identify HuBERT checkpoint conversion usageSearch codebase for convert_config calls related to HubertModel, or inspect model loading scripts for HuBERT-specific conversion logicAffected if Code uses HuBERT convert_config or loads HuBERT checkpoints from untrusted sources
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Inspect model checkpoint config filesExamine config.json or config.yaml files in the model checkpoint directory for unusual string values that could be malicious codeAffected if Checkpoint config contains suspicious Python code strings in fields that get eval'd/exec'd
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Check for eval or exec usage in convert_configReview the transformers library code: look for eval()/exec() calls in hubert feature extraction or convert_config modulesAffected if The convert_config function uses eval/exec on unvalidated checkpoint strings
You are affected if you are running Transformers 4.57.0 and using it to convert or load HuBERT checkpoints, especially from untrusted or unverified sources, without input validation on checkpoint configuration strings.
Generated from the published advisory. Verify against your own configuration.
Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.
dbcve · scopedImplement strict input validation on all checkpoint configuration strings before execution, avoid using eval()/exec() with untrusted data, and sandbox the checkpoint conversion process. Users should not convert checkpoints from untrusted sources.
Latest stable Transformers release (version > 4.57.0)
- 1. Identify all systems running Hugging Face Transformers version 4.57.0 in your environment
- 2. Upgrade Transformers to the latest stable release using: pip install --upgrade transformers
- 3. Verify the upgrade completed successfully by running: python -c 'import transformers; print(transformers.__version__)'
- 4. If using a specific environment, ensure the upgrade is applied to that environment (e.g., virtualenv, conda, Docker container)
- 5. Test any code that uses the convert_config function to ensure normal operation after upgrade
- 6. Review any custom checkpoint conversion code to ensure it does not pass unsanitized input to the convert_config function as a defensive measure
Generated from the published advisory — verify against the referenced sources before acting.
- Consultation4.0 h
- Implementation8.0 h
- Testing6.0 h
- Review / QA4.0 h
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Free · runs locallyCheck whether your project pulls in CVE-2025-14928 — or any other known-vulnerable package — straight from your lock files. Free and open source; it runs locally and uploads nothing.
References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.
Primary sourcesPractitioner notes
ContributedPeer-ranked notes from engineers who’ve handled CVE-2025-14928 in production — separate from our analysis above.
The advisory tells you what broke. It rarely tells you what actually worked. If you’ve dealt with this one, that detail is what the next engineer is searching for.
- The version that genuinely resolved it — not the one the vendor claimed
- A config change or rule that shut the vector down
- A gotcha in the upgrade path that cost you an afternoon
No notes yet
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- Version or environment caveats, and links to real fixes
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