CVE-2024-1432
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 · unedited** UNSUPPORTED WHEN ASSIGNED ** A vulnerability was found in DeepFaceLab pretrained DF.wf.288res.384.92.72.22 and classified as problematic. This issue affects the function apply_xseg of the file main.py. The manipulation leads to deserialization. The attack may be initiated remotely. The complexity of an attack is rather high. The exploitation is known to be difficult. The exploit has been disclosed to the public and may be used. The associated identifier of this vulnerability is VDB-253391. NOTE: This vulnerability only affects products that are no longer supported by the maintainer.
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 deserialization vulnerability exists in the apply_xseg function of main.py in DeepFaceLab's pretrained model (DF.wf.288res.384.92.72.22). The vulnerability allows remote attackers to potentially execute arbitrary code through malicious deserialization of model data. The exploit is publicly disclosed (VDB-253391) but considered difficult to exploit due to high complexity.
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= df.wf.288res.384.92.72.22CVSS 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
- Network
- Complexity
- High
- Privileges
- None
- User interaction
- Required
- Scope
- Unchanged
- Confidentiality
- Low
- Integrity
- Low
- Availability
- Low
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:L/I:L/A:L
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.
-
Verify DeepFaceLab installationLocate the DeepFaceLab installation directory. Common paths include user home folders (e.g., C:\Users\<username>\DeepFaceLab or /home/<username>/DeepFaceLab). Check for the presence of the main.py file in the root directory.Affected if DeepFaceLab is installed and the main.py file exists in the application directory
-
Identify the affected pretrained modelNavigate to the models folder within the DeepFaceLab directory. Look for a folder or file named df.wf.288res.384.92.72.22, which corresponds to the specific vulnerable pretrained model variant.Affected if A model directory or file named df.wf.288res.384.92.72.22 exists in the models folder
-
Locate the vulnerable apply_xseg functionOpen the main.py file in the DeepFaceLab root directory. Search for the function definition 'def apply_xseg' to confirm the vulnerable code path exists.Affected if The apply_xseg function is present in main.py and the code performs deserialization operations (such as using pickle, joblib, or similar deserialization methods) on model data
-
Check if model is configured for useReview any workspace configuration, training scripts, or model selection files to determine whether the df.wf.288res.384.92.72.22 model is selected or loaded as part of any active processing pipeline.Affected if The affected model is actively configured, loaded, or referenced in any active configuration or script
You are affected if DeepFaceLab is installed with the df.wf.288res.384.92.72.22 pretrained model and the apply_xseg function in main.py is present and used for model processing.
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.
From vendor dataSince DeepFaceLab is no longer supported, migrate to actively maintained deep learning frameworks or implement compensating controls such as network isolation, input validation for model files, and application sandboxing. If continued use is required, disable or remove the apply_xseg function and treat all pretrained model files as untrusted.
- Consultation8.0 h
- Implementation16.0 h
- Testing12.0 h
- Review / QA4.0 h
An estimate, not a bill — we confirm scope with you before any work starts. Need it this week? Rush from $11,200.
Scan for this in your stack
Free · runs locallyCheck whether your project pulls in CVE-2024-1432 — 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-2024-1432 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
Be the first to add a field note for this CVE — a mitigation you’ve verified, a version caveat, or a link to a working fix. Sign in above to contribute.
A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.
- Verified mitigations, workarounds, and config changes
- Version or environment caveats, and links to real fixes
- No weaponised exploit code, or anything meant to cause harm
- No spam, self-promotion, credentials, or personal data